PET-CT Imaging and Applications

*Sikandar Shaikh*

#### **Abstract**

PET-CT is an important imaging modality which is well established in the recent years. The role of the molecular imaging in the evaluation of the various pathologies has been increased due to the various technological advances, radiotracer advances and also in the research. This chapter is emphasised to give the broader and better overview of the PET-CT imaging which will be used for various applications in broader fields. These advanced imaging techniques will form the basis of the different clinical applications of the PET-CT. Thus, there will have more precise applications in various pathologies which will increase the sensitivity and specificity of the different disease processes. The understanding of the basic techniques is important before being used in various pathologies. The techniques can be routine or special like the puff cheek technique for the better evaluation of the oral malignancies. The newer concept of the dual time point imaging which is being used to differentiate between the various infective and inflammatory lesions from the malignant pathologies. This chapter emphasises the use of the various techniques for various focussed clinical applications.

**Keywords:** molecular imaging, PET, CT, radiotracer, fluorodeoxyglucose FDG, photo multiplier tube PMT, uptake

#### **1. Introduction**

The Society of Nuclear Medicine and Molecular Imaging has defined molecular imaging as "the visualisation, characterisation, and measurement of biological processes at the molecular and cellular levels in humans and other living systems. This PET-CT imaging is based on two important software aspects two-dimensional (2D) or three-dimensional (3D) imaging techniques which are useful for the evaluation of various pathologies. The basis here is that the newer PET-CT machines are having more 3D software which is capable of better resolution. The use of the positron emission tomography (PET) by using the 18F-fluoro-2-deoxy-D-glucose as the radiotracer forms the basis of the modern imaging newer concepts leading to the personalised medicine.

The development of the PET instrumentation is based on the early development in the radioisotope manufacture in the radiochemistry. This was in the 1970s, where the Ter-Pogossian and colleagues [1] has mentioned the different compounds in the article in Scientific American which are 15O, 13N, 11C, or 18F. Along with this the FDG was also one of the compounds which was developed in first half of the 1970s at the Hospital of the University of Pennsylvania (PENN) by the Martin Reivich, David Kuhl, and Abass Alavi and also at the Brookhaven National Laboratory (BNL) [2]. The first PET center in PENN was established in August 1976, and FDG was used as the radiotracer. This was the first of the machine with the low-energy

#### **Figure 1.**

*The PET gantry showing the patient surrounding the gantry with scintillators surrounding the patient. The activated gamma camera with gamma rays passing through this ray.*

gamma photons and this was later modified into the high-energy collimators which are capable of the positron emission photons. And thus, the first whole-body image was acquired with a dual-head rectilinear scanner comprising of the high-energy collimators. This PET III scanner was shifted to the University of the Pennsylvania (**Figure 1**) [3–6]**.**

#### **2. Positron emission tomography technology**

#### **2.1 PET annihilation**

The first use of the PET in the imaging was done by the use of the various short-lived positron-emitting isotopes like 11C, 13N, 15O, and 18F which are being produced as generator-produced gallium 68 (68Ga) and rubidium 82 (82Rb). The production of these isotopes is done by the use of the proton irradiation of the various natural or enriched targets, and all these will be having the various production equation, half-lives, and also the various properties of the positronemitting radionuclides. The positron emission is the process which is also the beta plus decay (b1 decay), also known as the isobaric decay process in which the proton will be converted into a neutron by releasing a positron and a neutrino. And this decay process is made up of the proton-rich radionuclides. The use of the positron decay will be resulting in the formation of the element which will be having the atomic number which will be less by one unit. This process is known as the nuclear transmutation where there will be the conversion of one isotope or the element into the another. Thus, the isobaric decay process will be representing the mass number of the daughter nuclei which will be the same, but the atomic number will be changing.

Here the process in which the proton-rich radionuclides will be converting into the stable nuclei by the isobaric decaying of the element resulting into the positron emission or electron capture. There will be the proton rich nuclide which are capable of the absorption of the inner shell electron where there will be the

#### *PET-CT Imaging and Applications DOI: http://dx.doi.org/10.5772/intechopen.103975*

conversion of the proton into the neutron. This is the process where the insufficient energy will be having difference with the element as well as the prospective daughter. The minimum energy difference here is if less than 1.022 MeV, the positron emission is not possible. The electron capture here will be in the usual decay mode. The positron emission is common in the lower atomic-weight nuclei made up of the 11C, 13N, 15O, and 18F, and the electron capture is seen in iodine 123.

The positron is capable of the moving into the very short distance and is seen as Positron energy 1 neutrino energy with the 5-transition energy of the 1.022 MeV. During this process the electron clouds due to the various surrounding materials will be retarding the energy, this along with the electron system will be forming the positronium which is the unstable system made up of the electron and a positron. These components will annihilate each other within the fraction of the second like 125 picoseconds to produce the various annihilation photons having energy equivalent of 511 keV. This is the energy which will be equivalent to the combined mass of the electron and a positron, and this is emitted in opposite directions most of the times at 180 degrees to each other (**Figure 2**)**.**

These annihilation photons which are being emitted at 180 are being detected by PET detectors by the principle of electronic collimation. The arrival of the annihilation photons here is based on the very small timing of the window which is usually the 3–15 nanoseconds. This process will be resulting into the process of the coincidence detection in PET. These detection at 180 is resulting in the formation of the line of the response which is the straight line drawn between the 2 detectors, and this process is the line of response (LOR) or coincidence line. The common availability of the various detectors having the faster timing decay, along with the high light output, and also the higher stopping power. The use of the PET scanners having the time-of-flight (TOF) capability thus it will be resulting the principle of which relies on measuring the arrival time difference of the 2 annihilation photons. The pinpoint emission point is also seen along the LOR. The ultimate use of the TOF will result in the better contrast PET images having the better sensitivity [7]. The TOF position will be resulting the along the LOR is clearly defined and resulting into the various coincidence time resolution.

#### **Figure 2.**

*This showing the principle of the positron emission where the beta decay causing the positron electron annihilation at 180 degree.*

#### **2.2 PET-scan**

These scanners are made up of the various many small detectors which are usually placed in adjacent rings around the patient. The clinical state-of-the-art PET system was having a ring diameter of 60–90 cm with the extent of 10–25 cm and made up to 25,000 detectors. The single PET detector is made up of the very high-density scintillator crystal (eg, BGO, lutetium yttrium orthosilicate [LSO], or lutetium yttrium orthosilicate [LYSO]) which are capable of converting the photons striking on the detector into light. The scintillator is usually optically coupled to a device, of which the photo multiplier tube (PMT) is the commonly seen where the light will be converted into an amplified electric signal (**Figure 3**). These signals arising from the single detector will be added the coincidence circuit and this will be added as the PET event identified by the detection time. These various photons which are being detected within these various coincidence windows are documented as the coincidence events which will be attached to the LOR having the various 2 detectors, and all these events will be rejected.

Majority of these PET systems will be acquiring the data over a given time frame and these events will be tagged with the LOR position and also at the time point of detection.

The tomographic single slice is being reconstructed independently by only accepting LOR within the given slice. These independent reconstructions are being obtained by using the various lead or tungsten collimating rings in between crystal rings and these inter-ring coincidences can be prevented. This process of the imaging is called 2D PET. The higher sensitivity of the inter-ring coincidences will be resulting into the 3D PET. Usually the 2D PET is preferred over 3D PET as it is easier to have simple data for handling and various image reconstruction algorithms. But due to the various technological development having the better iterative reconstruction algorithms the sensitivity from 3D PET is now more preferred in the various clinical PET systems. The various 511-keV photons from the various annihilation events are being detected in the PET system which are within the coincidence window, and these are being referred as the random coincidences. These random

#### **Figure 3.**

*Image showing the blocks of the scintillating crystals which will be showing the multiple bocks of the PMTs, and each block of the scintillation crystal are made up of the 4 PMTs.*

coincidences in LOR will define the width of the coincidence window and also the various event rates in the detectors which will be defining the LOR. These will result in the various random coincidences being modelled by the various coincidence effects.

#### **2.3 Developments in PET instrumentation**

Due to the various quantitation of the smaller lesions due to the increased image contrast and along the various partial volume effects. These limitations of the PET reconstructed spatial resolution of close to the 5 mm will be resulting in various partial volume effects. This spatial resolution is based on the various size of the detector crystals, where the spatial resolution can be improved with the use of the smaller crystals. In the older versions of the conventional PET designs, the light output from a particular crystal is being shared by the use of the several PMTs. These improvements in spatial resolution will have the direct electronic readout of the light emitted by a given crystal. These features will be seen in the PET photoncounting detector designs [8] and also in PET detectors where the silicon photomultiplier detectors are being developed and also used for PET/MR imaging [9].

The modern PET systems are made up of the TOF capabilities which will be having the biggest advantage of better image contrast, resolution with the improved noise which will be further reduced by the better localization of the annihilation position. The TOF resolution is directly related to the with the growth in the technology the signal-to-noise ratio in the new detector designs will be having better TOF resolution. The improvements in signal-to-noise ratios will be reducing the less activity as well as the scan time in each patient. There is the potential reduction in the patient dose or PET scan time which will be increasing the PET scanner sensitivity, and this is based on the advancements and improvements of the various PET detector.

#### **2.4 PET radionuclides**

FDG production 18F is produced in a cyclotron by the process of the nuclear reaction between oxygen 18 (18O)-enriched water which is being bombarded with protons by the releasing of the neutron. In the 68Ga, the germanium 68 which will be bound to the generator will result into another daughter isotope.

The half-life of 110 minutes is the ideal one for the clinical use and this will be used for the synthesis which can be used for hours. The very low positron energy (640 keV) will be resulting in the very short tissue range (2.3 mm) causing the higher resolution and low radiation dose. The 18F synthesis for the higher radioactive material will be having the advantages over the short-lived radioisotopes and also for the plasma analysis which will be needed for the quantification as well as for the evaluation.

#### **2.5 The FDG concept**

The basis of the tumour metabolism is that they consume more energy in the form of the glucose as this is the most commonly used metabolite, this happens by the process of the increased glycolysis which is also known as Warburg effect. This is the principle which is being used in the PET when FDG is used as a radiotracer. The FDG is a radiolabelled glucose analogue in which the 20-hydroxyl group is being substituted by 18F.

Many factors will be having an impact on the glycolysis of the tumour cells like the histologic type, the tumour grading, the tumour cell proliferation and most

important is the tumour vasculature all these factors are important for the delivery of glucose and oxygen to the tissues [10]. The less amount of the oxygenation will result in the tumour hypoxia, and this is being explained on the basis of the tumours with the lower partial pressure of oxygen in comparison to the normal tissues. The tumour hypoxia will also be based on the principle of the solid tumours. The detection of the hypoxic regions means more malignant potential with bad prognosis and resistance to the therapy [11].

The increased glucose and also the hyperinsulinism will be resulting in the less amount of the FDG uptake in the tumours based on the uptake of FDG and glucose which will be competing with each other. This is the reason that the at least 4–6 hours fasting is needed before the FDG injection which will reduce insulin levels and will be facilitating the better background ratio. For the evaluation of the various pathologies the blood glucose levels have to be within normal range, 150–200 mg/dL. If the sugar levels are more than these values, then the PET-CT should be rescheduled unless and until it is stabilised to normal levels. The reason for this is that the insulin-induced hypoglycaemia will show the less tumour uptake and also facilitates the normal physiological uptake in the muscles and fat, causing the significant reduction of the tumour-to background ratio.

The normal uptake which appears more prominent in the brain and the heart due to increased glycolysis. In the cerebral parenchyma it will be more uniform in the cortex and basal ganglia, and corresponding lesser uptake in white matter and in the cerebrospinal fluid. The myocardial uptake is significantly variable can be very high, low, or absent also. This pattern is seen in the left ventricle; however, the right ventricle and atria uptake are not very high (**Figure 4**). The prolonged fasting of the 18 hours or more and a the low-carbohydrate-high-fat diet will result

#### **Figure 4.**

*This PET-CT images (a–c) showing the normal mild physiological uptake, image (d) showing the physiological uptake in the bowel loops in the whole body scan.*

#### *PET-CT Imaging and Applications DOI: http://dx.doi.org/10.5772/intechopen.103975*

in the change of the metabolism like glucose to free fatty acids, this will result into the varied uptake due to the temporal and geographic diversities of the decreased glycolytic activity. The normal physiologic uptake in the various organs like the liver, spleen, and bone marrow are usually homogenous and low; on contrary the bone marrow will be showing significant uptake in following conditions like systemic inflammation, prolonged bleeding, or associated therapeutic interventions with chemotherapy or bone marrow stimulants. With this pattern of the uptake the different types of the skeletal metastases or associated malignant bone marrow infiltration; and this will result in the skeletal metastases.

Another important aspect of is that the glandular tissue of the breasts. The two areas of the physiological uptake will be giving the incidental findings especially related to the deserve special mention, namely, the thyroid gland and the gastrointestinal tract. The pattern of the uptake like the diffuse or focal uptake will be seen as in goitrous glands or thyroiditis. The closest differentials will be the malignancies or premalignant findings in as many as 33% of patients and should be further examined [12]. The Physiologic uptake in the bowel will be variable, resulting from the mild to the diffuse intense and focal uptake; more subtle in the caecum and rectosigmoid and also the patients undergoing treatment with metformin The exact cause of the intestinal uptake is not fully completely understood. The various factors which will be impacted are metabolically active mucosa, luminal contents, or glycolytic bacteria.

#### **3. PET imaging applications**

The clinical role of the correlative imaging will be used for various applications. And with the commercially available radiopharmaceuticals which are being used more commonly in various oncology [13], cardiology [14], neurology, and psychiatry [15, 16]. As discussed previously, the inner component of the PET/MR imaging design will be showing similarities of the MR head coil, PET detector ring, and MR magnet tunnel. Simultaneously acquired MR images, PET, and fused combined PET/MR images after intravenous injection of 370 MBq of FDG are shown. This tracer can be recorded for the 20 minutes at steady state till 2 hours. The earliest form of the image registration will be restricted to the various applications of the brain for the various brain tissues with the satisfactory model [17, 18]. Thus, the PET-CT is more useful for the neuroimaging applications and now one of the well-established imaging modalities. This also has the important role in the evaluation of the various central nervous system disorders like epilepsy, Alzheimer's and Parkinson's disease, head injury, and inoperable brain tumours [19–21].

The use of the various radiotracers for the assessment of the tumour metabolism and also the various physiological alteration involved in various diseases, and these are having significant impact on the PET/CT role as the emerging modality in the field of molecular imaging. The various oncological applications are being used for the evaluation of the [13, 22] various conditions in the central nervous system disorders, orthopaedic infections, and inflammatory disorders, and also for the evaluation and metastatic follow up of various pathologies.

PET imaging of radiolabelled nanoparticles has created lot of curiosity in the field of the molecular imaging. The size criteria's for the nanoparticles is related to the size range of the few to several hundred nanometres. Lot of advantages of the nanoparticles are there as the newer molecular imaging agents, which are not being limited by the ease of the physical properties and also for the surface functionalisation [23, 24]. Physically the nanoparticles will be having the larger surface area-tovolume ratio. These are capable of getting attached to the various targets which will be used as the targeting agents for the various diagnostic, and therapeutic purposes. These are the agents which will be providing the more specific binding receptor capacity with higher specificity and affinity which is more important for the more precise detection and the evaluation of the various disease markers. Another important property is that it will be having the longer half-life as compared to the free drug molecules resulting in the significantly enhanced bioavailability.

The important aspect is the important characteristics of the radioisotopes which are the imaging characteristics of isotopes; the decay half-life of the radioisotope; the isotope availability; and the reliability of the radiolabelling of the radioisotope. The lesser positron energy with the high branching ratio of β+ decay will be having the different characteristics for PET imaging. The use of the isotopes having the high positron energy will be travelling for the longer distance for the positron annihilating and will have the significant loss of the spatial resolution. The isotopes which are having the very low positron efficiency, will have the lower atoms undergoing the β+ decay as compared to the overall atoms which will be requiring the very long scan times and also the very noisy images [25].

The nanomaterial use in the biomedical engineering can be able to understand the better "absorption, distribution, metabolism, and excretion" (ADME) pattern for the materials which can strike the balance between the nanoparticle-induced benefits and also the long-term toxicity due to nanoparticle exposure [26–28]. As we all know that the PET imaging is having significant advantage of higher sensitivity and also the ability for the quantitative analysis of the whole-body imaging and due to this property the more precise biodistribution of nanoparticles can be done. Thus, the PET imaging can be able to monitor the various nanoparticles in the non-invasive manner. The labelling of the nanoparticles coordinating with the radiometal, and the chelator is more preferred option. There is also the alternative method of the evaluation.

#### **3.1 Radiolabeled nanoparticles for molecular imaging**

The nanoparticles are having the two important advantages. Different and multiple modalities can be useful for the various modalities to get integrated in the single nanoparticle platform. We all know at this point that every imaging modality is having the advantages and disadvantages. PET imaging resolution is very much sensitive upto the picomolar level and quantitative >1 mm which is very low. The magnetic resonance imaging (MRI) is having the submillimetre-level spatial resolution still having significantly low sensitivity. The use of the optical imaging is highly sensitive and easily accessible. But due to the scatter of light there is the limitation of the penetration depth and also the spatial resolution. With this the combination of the different imaging modalities will be complimentary to each other and will have better imaging quality. The nanomaterials due to its functionalization they can be prevented to get attacked by the immune system and can have longer circulation time. The multiple targeting ligands are being conjugated to a single nanoparticle which will be providing the significant enhanced receptor binding affinity by the polyvalency effect [29].

#### **3.2 Special imaging techniques**

This technique was described by Weissman and Carrau [30]. By this method of the puffing the cheeks, the oral vestibule is being filled with air, creating the negative contrast separating the buccal and labial mucosa from the gingival mucosa, and due to this both the mucosal surfaces can be evaluated separately. In this procedure the buccinators muscle, the pterygomandibular raphe, and the retromolar trigone

#### *PET-CT Imaging and Applications DOI: http://dx.doi.org/10.5772/intechopen.103975*

are also seen better. The mucosal pliability is also being affected due to the trismus. So, during the FDG PET/CT acquisition if there is focal area of the uptake of FDG in the oral cavity then the use of the puffed-cheek maneuvere, should be done which will take around 4 minutes. Patient is asked to close the mouth and fully puff the cheeks while breathing through the nose during this 3- to 4-minute PET/ CT acquisition. The puffed cheek scanning time is very short and can result into the more increased salivation and associated attenuation effects [31, 32]. This technique will lead to the better localisation and demonstration of the extent of a tumour of the oral cavity. Chang and colleagues [33] demonstrated that the puffed cheek maneuvere on FDG PET/CT is more useful for the evaluation of the oral cancers and their extent as seen in the FDG PET/CT. This study has shown that the localised or extended oral cancers of puffed cheek FDG PET/CT and conventional FDG PET/ CT was 95.2% and 54.5%, respectively. FDG PET/CT delineated more oral cancers as compared to our routine conventional FDG PET/CT and also for the preoperative evaluation of the tumour thickness. The dental artefacts are significantly reduced by 70% in the puffed-cheek FDG PET/CT.

#### **3.3 Open-mouth technique**

Method is described by Henrot and colleagues [34]. In this technique the routine conventional whole-body FDG PET/CT done from the supraorbital margin to mid-thigh (**Figure 5**). After this the patient has to open the mouth. 50-mL syringe is put in between the teeth to for the correct immobilisation. The PET-CT is acquired during quiet respiration. PET/CT scan is again acquired from the orbitomeatal line to the clavicular fossa, with one field of view (15 cm, 3.5 minutes) and totally taking upto 3–4 minutes. The important indication of these is the evaluation of the tumour of the oral cavity and also the oropharynx which sometimes are difficult for the evaluation of the dental artefact. Cistaro and colleagues [32] have demonstrated that this technique is more useful in the evaluation of the oral carcinomas. With this the tumour localization, tumour extent, and surrounding structure involvement can be seen in this open-mouth view as compared with the closed mouth view.

Modified Valsalva maneuvere is used for the evaluation of the location and extent of a hypopharyngeal tumour as there will be the opposition of the mucosal surfaces and also for the evaluation of the nasopharynx when the pharyngeal recesses are collapsed. This maneuvere is done by asking the patient to utter the word "e" uniformly for at least 10 seconds and during this time the patient should hold breath for at least 10 seconds. It is advisable to instruct the patient before so that no artefacts can be seen. This technique is done from the hyoid bone to the trachea.

The phonation is indicated to differentiate between the true and false vocal cords which are needed to evaluate the precise location of the laryngeal tumour and its margins for the quiet respiration during the examination. With this the true vocal cords can be seen opposing and cannot be able to distinguish from each other as performed during the apnoea. Still, they can be abducted and not visible when the acquisition is performed during quiet respiration [34]. These techniques along with the modified Valsalva and phonation techniques are mostly used for the CT acquisition. The performing hybrid PET/CT along with this two maneuvere is difficult as the PET acquisition for one field of view requires a minimum time duration of 2–3 minutes The use of the spot and the various maneuvere will be leading to the better delineation of the hot spots [35]. Ter-Pogossian [1] showed that it will take 3 minutes to perform modified Valsalva or phonation technique for this period. There are many motion artefacts and due to this the coregistration of CT and PET images is very difficult.

**Figure 5.**

*This is the puff cheek technique which is very important for the evaluation of the small nodular lesion seen in the left buccal mucosa. CT image (A) showing nodule, (B and C) are the PET images and (C) image is the fused image showing nodular lesion with the significant uptake.*

#### **3.4 Optimization of patient preparation**

The Optimization of scan protocol will lead to the decrease in the physiologic uptake of FDG in the head and neck region. The voluntary or involuntary tongue movement or sucking actions will cause in the significant increase in the pharyngeal muscles uptake [36]. The increased uptake in the base of the tongue and anterior part of the floor of the mouth is due to the increased uptake in the genioglossus muscle in the supine position due to its role of preventing the tongue to fall posteriorly and causing obstruction of the airway especially in the rest and also during the [37]. The other false uptake can be due to the activity post injection like talking and movement this is due to the increased laryngeal muscle activity [38]. The other areas of the uptake will be seen in the various muscles like lateral pterygoid, and masseter and this is also possible due to the long wait time [39]. Mid-morning is better time for the evaluation to prevent the supine position related FDG uptake in the muscles at the base of the tongue and anterior part of the mouth floor. FDG uptake in the brown fat and neck muscle can be difficult to differentiate between the supraclavicular lymph nodes and can be masked [40]. Ter-Pogossian [1] silent

#### *PET-CT Imaging and Applications DOI: http://dx.doi.org/10.5772/intechopen.103975*

suggested not to have the liquid intake 30 minutes before the injection and also during the waiting time between FDG injection and whole-body scanning to avoid FDG uptake by the tongue and vocal muscles. Before the scan, all metal objects 7(eg, necklaces, earrings, and prosthesis) should be removed to prevent the metal attenuation artefacts. Cistaro and colleagues [32] showed the optimization of patient preparation in patients with HNC. With this technique there will be less FDG uptake in the muscles of base of tongue and floor of the mouth can be achieved. FDG, is not tumour-specific and various image interpretation pitfalls may occur because of false-positive and -negative causes of FDG uptake. The use of certain premedication, such as propranolol and diazepam, will result in the decrease physiologic FDG uptake in the brown fat and muscle.

#### **3.5 Precision medicine**

National Institutes of Health (NIH) has defined precision medicine as "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." This concept will be more useful for the doctors and researchers for evaluation of the various treatment options and other prevention aspects for the various diseases. Thus, leading to the various focus is on identifying different approach including the genetic, environmental, and lifestyle factors. The current therapy paradigm of "one-size-fits-all" approach, in which disease treatment and prevention strategies are developed.

#### **3.6 Biomarker for precision diagnostics**

A good and ideal biomarker for patient selection is very important for the evaluation of the novel therapeutic agent. Due to this there has to be companion diagnostic predictive markers, and these are being developed for the selection of the right patients. Tirapazamine (TPZ) is the benzothiazine series hypoxiaselective antitumor agent. PET/CT with fluorine-18-labelled fluoromisonidazole (18F-FMISO) or 18F fluoroazomycin arabinoside (FAZA) which is the hypoxic agent can be used with the TPZ [41, 42] as a diagnostic marker. PET/CT for the evaluation of the oestrogen receptor (ER) expression for the management and also with hormonal therapies for the various neuroendocrine tumour patients like the 68Ga DOTATATE PET/CT, before the initiation of the therapeutic management with 177LuDOTATATE [43]. The use of the 68GaPSMA PET/CT before therapy with 177Lu-PSMA therapy can also be evaluated [44]. Thus, there are more precise and the important new PET radiopharmaceuticals having the specific molecular targets, and also for the therapy along with these agents.

#### **3.7 Tumour heterogeneity**

The important aspect of the precision medicine use in the oncology is the tumour heterogeneity, but it is still challenging due to various reasons and most important one is the heterogenous presentation of the tumour. The heterogeneity of the tumour can be again sub classified as the (1) intertumoral heterogeneity: In this category the patient will be having different tumours or lesions which looks similar histologically but may be differing in the molecular variants as well as the malignant potential; (2) on contrary the intratumor heterogeneity: where the tumour will have the different functional capabilities in the tumour heterogeneity. The application of the spatial heterogeneity of subclones in a primary lesion or metastasis will be providing a bigger challenge for precision medicine as sequencing a portion of the

tumour may miss important therapeutically relevant information. Lesions may be at locations who will get the tissue biopsy practically impossible. The end result of the clones can change with selective pressure from a targeted therapy leading to the of mutagenic activity of radiation and chemotherapy. Usually, the patient prognosis is poor when biomarkers found in the primary tumour, and metastatic lesions are varied. Thus, precision medicine will be requiring the different intratumor and intertumoral heterogeneity in the patient. The PET/CT is capable of the providing the different intratumor pattern of the heterogeneity along with the interpatient intertumoral heterogeneities. By this the evaluation of the whole-body can be done for the primary as well as metastatic lesion at one time. With the availability of the newer PET radiotracers, it will be possible for the evaluation of the intratumor and intertumoral heterogeneity. PET with 18F-FES can evaluate the regional ER expression [45] and this is having the advantage to overcome the different errors which will be arising the from disease heterogeneity. The use of the PET can also be able to measure the delivery and also the binding of oestrogen in vivo in correlation of the ER expression for the multiple tumour sites. The 18F-FES uptake in the tumour can be correlating with the ER expression corresponding to the various radioligand binding sites [45] the radiotracer uptake is directly related with the tamoxifen and aromatase inhibitor treatment [46–48]. Here the FES-PET is more important for the assessment of tumour heterogeneity of ER expression [49]. One of the recent study the role of the 18F-FES PET/CT can change the plan of the management upto the 48.5% of patients. For the detection of the ER status in the metastasis group (n 5 27), there will be significant increase in the 18F-FES PET/CT which has shown the significant increase in the metastatic lesions in 11 patients; absent in the 13 patients, and the rest of the 3 patients will be having both the 18F-FES positive and negative lesions. The 18F-FES PET/CT results has shown the better management plans in 16 patients (48.5%, 16/33) [50]. Another example is radiation therapy delivery which is significantly based on the heterogeneity of tumour hypoxia which is based on 18F-FMISO PET/CT [51]. The concentration of the 18F-FMISO in this gross tumour volume (GTV) is based on the hypoxia levels in the tumour. The 18F-FMISO PET/CT-guided intensity modulated radiotherapy (IMRT) for 10 patients in the diagnosed head and neck cancers which will be achieved 84 Gy to the GTV(h) and 70 Gy to the GTV, these can be done without exceeding the normal tissue tolerance levels. Investigators also attempted to deliver 105 Gy to the GTV(h) for 2 patients and were successful in 1, with normal tissue sparing.

#### **3.8 Therapy assessment**

The use of the various current therapy evaluations showing the anatomic changes, and these are the not sensitive biomarkers for the novel and targeted therapies. The basis here is the identification of the therapy resistance which needs to be evaluated early for the delivery of the precision medicine. The role of the PET/CT is important for the delivering precision medicine. Thus PET/CT is useful for the evaluation of the early therapy assessment along with the biology of the tumours or molecular subtypes, therapy selection, timing of early therapy assessment PET/CT, and for the performing PET/CT in a standardised manner.

#### **4. Methionine**

Methionine is the commonly used amino acid tracer, which is used in PET imaging of brain tumours, due to the low physiologic uptake of MET in brain. This is being used as it is very convenient for the radiochemical production, which will

#### *PET-CT Imaging and Applications DOI: http://dx.doi.org/10.5772/intechopen.103975*

be allowing the rapid synthesis leading to the higher radiochemical yield [52]. The significant increased uptake of methionine is to be correlated with both cellular proliferation [53] and micro vessel count [54] in gliomas. Post injection, MET uptake in the brain is low and, in combination with high tumour uptake, leading to the very higher detection rate and also the good lesion delineation [55]. The normal biodistribution of the MET uptake is lower in the cerebral cortex, cerebellum, basal ganglia, and thalamus. Moderate amount of the accumulation is seen in pituitary and glandular system (parotid and salivary glands).

#### **5. Choline**

Prostate cancer is one of the most common malignancies in men and the incidence of prostate cancer increases directly with age. This tumour is showing the biologic behaviour, from a clinically silent, intraprostatic tumour to an aggressive malignancy, and resulting into the more sensitivity. Early identification is more helpful for the benefit of therapeutic decision-making [56, 57]. Prostate cancer cells are showing the significant increased phosphocholine levels along with the elevated turnover of the cell membrane phospholipid, namely phosphatidylcholine [58]. Choline imported into the cell which is again phosphorylated by choline kinase in the first step of the Kennedy cycle. The role of the choline kinase is more in the prostate cancers, and due to this the prostate cancers will have more carbon-11 choline concentrations in the cells [59]. The important characteristic of the CHO faster blood clearance (5 min) and also the significantly faster uptake in the prostate tissue (3–5 min), this will be resulting into early excretion in the urine. The longer half-life of fluorine-18 (110 min) allows transportation of 18F-fluorocholine to centres without a cyclotron [60], although 18F-choline has a higher urinary excretion than CHO [60].

#### **6. Radiotracer advances**

Due to the technical developments various new radiotracers are in pipeline for the more precise use of these in various cancers, which is capable of the evaluation of the cell proliferation, metastasis to different organs, hypoxia in various tumours, focussed receptor status, tumour antigen levels, and various therapeutic response.

18F-fluorothymidine (18F-FLT) is being used as the cell proliferation marker which will be used for the better quantification of tumour growth and also for the metastatic work up and also for the treatment response evaluation [61, 62]. 18F-Fluoromisonidazole (FMISO), is the important hypoxia biomarker for the evaluation of the degree of hypoxia in a tumour which can be used to see the aggressiveness of the tumour and also the response to management. This will introduce the various endothelial cells which are being activated by tumour-induced angiogenesis which can be used as an indicator of the local as well as the distant metastasis. The use of the 18F-galacto-arginine-glycine-aspartic acid tripeptide, having the capacity to bind the primary tumours as well as the metastatic lesions. 68Ga-PSMA, is being widely used as the radiotracer of choice with significant low FDG avidities. Thus, this PSMA is showing in the pathologies which are showing the low uptake. The use of the 18F-Fluciclovine, is the analog used for the various pathologies. The use of the 18F-Choline PET/CT is being used as the radiotracer of choice for the leptomeningeal metastasis detection.

The radiotracers which will be targeting the various hormone receptors and HER2 are the newer development, and they are having significantly increased

efficacy. The use of the 18F-16α-fluoroestradiol (FES) as an substrate of oestrogen receptors is seen widely and can be seen as the source of the ER expression and the pharmacodynamic marker in the ER-directed therapy [63]. The use of the 68Ga-NOTA-RM26, is seen as the ER expression for the improvement in the sensitivity and specificity of breast cancer diagnosis which is seen as close to the 100 and 90.9%, respectively, in the proliferating phase of the menstruating cycles patients. Clinically HER2 status is determined by immunohistochemical or fluorescence *in situ* hybridization testing of biopsy samples. New PET tracers like the 89Zr-trastuzumab and 89Zr-pertuzumab are being used for the quantification of the HER2 expression of the primary tumour and metastases simultaneously which shows the promising results.

#### **7. PET radiomics**

Radiomics is the newer concept of using the various disease characteristics in which the various parameters/features can be taken in the region of interest like the mathematical algorithms. Non-invasive image-derived biomarkers are also generated from PET radiomics based on the pixels, their associated parameters, and their positions [64–66]. Since the MRI has significantly high sensitivity then the PET- and MRI combination will have better spectrum of features for the building of the predictive models.

#### **8. Summary**

The relevance of PET scan is the important aspect of the various techniques which can be used for the various clinical applications. The different protocols need to be set for the different conditions to have the better sensitivity of that particular pathologies. The use of different radiotracers also needs to be explained in detail for the evaluation on the lines of the precision medicine. The various clinical applications are based on the different techniques used as well as the different radiotracers used. The sensitivity of these are based on the using of the optimal parameters for the evaluation of the different tumours or the pathologies. Thus this chapter redefines the important aspects of the both techniques as well as the clinical applications.

#### **Author details**

Sikandar Shaikh PET-CT and Radiology, Yashoda Hospitals, Hyderabad, India

\*Address all correspondence to: idrsikandar@gmail.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **References**

[1] Ter-Pogossian MM, Raichle ME, Sobel BE. Positron emission tomography. Scientific American. 1980;**243**:170-181

[2] Hess S, Høilund-Carlsen PF, Alavi A. Historic images in nuclear medicine 1976: The first issue of clinical nuclear medicine and the first human FDG study. Clinical Nuclear Medicine. 2014;**39**:701-703

[3] Rich DA. A brief history of positron emission tomography. Journal of Nuclear Medicine Technology. 1997;**25**(1):4-11

[4] Alavi A, Reivich M. The conception of FDG-PET imaging. Seminars in Nuclear Medicine. 2002;**XXXII**(1):2-5

[5] Ido T, Wan CN, Casella V, et al. Labeled 2-deoxy-D-glucose analogs. Fluorine-18-labeled 2-deoxy-2-fluoro-D-glucose, 2-deoxy-2-fluoro-Dmannose, and C-14-2-deoxy-2-fluoro-D-glucose. Journal of Labelled Compounds and Radiopharmaceuticals. 1978;**14**:175-182

[6] Reivich M, Kuhl D, Wolf A, et al. The [18F]fluorodeoxyglucose method for the measurement of local cerebral glucose utilization in man. Circulation Research. 1979;**44**:127-137

[7] Karp JS, Surti S, Daube-Witherspoon ME, et al. Benefit of time-of-flight in PET: Experimental and clinical results. Journal of Nuclear Medicine. 2008;**49**(3):462-470

[8] Miller M, Griesmer J, Jordan D, et al. Initial characterization of a prototype digital photon counting PET system. Journal of Nuclear Medicine. 2014;**55**:658

[9] Wong WH, Li H, Zhang Y, et al. A high-resolution time-of-flight clinical PET detection system using the

PMT-quadrant-sharing technology. Journal of Nuclear Medicine. 2014;**55**:657

[10] Bos R, van Der Hoeven JJ, van Der Wall E, et al. Biologic correlates of (18) fluorodeoxyglucose uptake in human breast cancer measured by positron emission tomography. Journal of Clinical Oncology. 2002;**20**:379-387

[11] Bertout JA, Patel SA, Simon MC. The impact of O2 availability on human cancer. Nature Reviews. Cancer. 2008;**8**:967-975

[12] Shie P, Cardarelli R, Sprawls K, et al. Systematic review: Prevalence of malignant incidental thyroid nodules identified on fluorine-18 fluorodeoxyglucose positron emission tomography. Nuclear Medicine Communications. 2009;**30**(9):742-748

[13] Czernin J, Allen-Auerbach M, Schelbert HR. Improvements in cancer staging with PET/CT: Literature-based evidence as of September 2006. Journal of Nuclear Medicine. 2007;**48**:78S-88S

[14] Di Carli MF, Dorbala S, Meserve J, et al. Clinical myocardial perfusion PET/CT. Journal of Nuclear Medicine. 2007;**48**:783-793

[15] Costa DC, Pilowsky LS, Ell PJ. Nuclear medicine in neurology and psychiatry. Lancet. 1999;**354**:1107-1111

[16] Tatsch K, Ell PJ. PET and SPECT in common neuropsychiatric disease. Clinical Medicine. 2006;**6**:259-262

[17] Pelizzari CA, Chen GT, Spelbring DR, et al. Accurate threedimensional registration of CT, PET, and/or MR images of the brain. Journal of Computer Assisted Tomography. 1989;**13**:20-26

[18] Woods RP, Mazziotta JC, Cherry SR. MRI-PET registration with automated

algorithm. Journal of Computer Assisted Tomography. 1993;**17**:536-546

[19] Gilman S. Imaging the brain. The New England Journal of Medicine. 1998;**338**:812-820

[20] Viergever MA, Maintz JB, Niessen WJ, et al. Registration, segmentation, and visualization of multimodal brain images. Computerized Medical Imaging and Graphics. 2001;**25**:147-151

[21] Muzik O, Chugani DC, Zou G, et al. Multimodality data integration in epilepsy. International Journal of Biomedical Imaging. 2007;**2007**:13963

[22] Bohnen NI, Djang DS, Herholz K, et al. Effectiveness and safety of 18F-FDG PET in the evaluation of dementia: A review of the recent literature. Journal of Nuclear Medicine. 2012;**53**(1):59-71

[23] Ma X, Zhao Y, Liang X-J. Theranostic nanoparticles engineered for clinic and pharmaceutics. Accounts of Chemical Research. 2011;**44**:1114-1122

[24] Cheon J, Lee J-H. Synergistically integrated nanoparticles as multimodal probes for nanobiotechnology. Accounts of Chemical Research. 2008; **41**:1630-1640

[25] Yoshida E, Tashima H, Inadama N, Nishikido F, Moriya T, et al. Intrinsic spatial resolution evaluation of the X'tal cube PET detector based on a 3D crystal block segmented by laser processing. Radiological Physics and Technology. 2013;**6**(1):21-27

[26] Soo Choi H, Liu W, Misra P, Tanaka E, Zimmer JP, Itty Ipe B, et al. Renal clearance of quantum dots. Nature Biotechnology. 2007; **25**:1165-1170

[27] Choi HS, Ashitate Y, Lee JH, Kim SH, Matsui A, Insin N, et al. Rapid translocation of nanoparticles from the

lung airspaces to the body. Nature Biotechnology. 2010;**28**:1300-1303

[28] Wang B, He X, Zhang Z, Zhao Y, Feng W. Metabolism of nanomaterials in vivo: Blood circulation and organ clearance. Accounts of Chemical Research. 2012;**46**:761-769

[29] Starmans LW, Hummelink MA, Rossin R, Kneepkens E, Lamerichs R, Donato K, et al. 89Zr-and Fe-labeled polymeric micelles for dual modality PET and T1-weighted MR imaging. Advanced Healthcare Materials. 2015;**4**:2137-2145. DOI: 10.1002/ adhm.201500414

[30] Weissman JL, Carrau RL. "Puffedcheek" CT improves evaluation of the oral cavity. American Journal of Neuroradiology. 2001;**22**:741-744

[31] Fatterpekar GM, Delman BN, Shroff MM, et al. Distension technique to improve computed tomographic evaluation of oral cavity lesions. Archives of Otolaryngology – Head & Neck Surgery. 2003;**129**:229-232

[32] Cistaro A, Palandri S, Balsamo V, et al. Assessment of a new 18F-FDG PET/CT protocol in the staging of oral cavity carcinomas. Journal of Nuclear Medicine Technology. 2011;**39**:7-13

[33] Chang CY, Yang BH, Lin KH, et al. Feasibility and incremental benefit of puffed-cheek 18F-FDG PET/CT on oral cancer patients. Clinical Nuclear Medicine. 2013;**38**(10):e374-e378

[34] Henrot P, Blum A, Toussaint B, et al. Dynamic maneuvers in local staging of head and neck malignancies with current imaging techniques: Principles and clinical applications. Radiographics. 2003;**23**(5):1201-1213

[35] Gupta T, Master Z, Kannan S, et al. Diagnostic performance of posttreatment FDG PET or FDG PET/CT imaging in head and neck cancer:

#### *PET-CT Imaging and Applications DOI: http://dx.doi.org/10.5772/intechopen.103975*

A systematic review and meta-analysis. European Journal of Nuclear Medicine and Molecular Imaging. 2011; **38**:2083-2095

[36] Kubota K. From tumor biology to clinical PET: A review of positron emission tomography (PET) in oncology. Annals of Nuclear Medicine. 2001;**15**:471-486

[37] Abouzied MM, Crawford ES, Nabi AN. 18F-FDG imaging: Pitfalls and artifacts. Journal of Nuclear Medicine Technology. 2005;**33**:145-155

[38] Kostakoglu L, Wong JCH, Barrington SF, et al. Speech-related visualization of laryngeal muscles with florine-18-FDG. Journal of Nuclear Medicine. 1996;**37**:1771-1773

[39] Rikimaru H, Kikuchi M, Itoh M, et al. Mapping energy metabolism in jaw and tongue muscles during chewing. Journal of Dental Research. 2001;**80**:1849-1853

[40] Kostakoglu L, Hardoff R, Mirtcheva R, et al. PET-CT fusion imaging in differentiating physiologic from pathologic FDG uptake. Radiographics. 2004;**24**:1411-1431

[41] Mitsudomi T, Morita S, Yatabe Y, et al. Gefitinib versus cisplatin plus docetaxel in patients with non-smallcell lung cancer harbouring mutations of the epidermal growth factor receptor (WJTOG3405): An open label, randomised phase 3 trial. The Lancet Oncology. 2010;**11**(2):121-128

[42] Zegers CM, van Elmpt W, Szardenings K, et al. Repeatability of hypoxia PET imaging using [18F]HX4 in lung and head and neck cancer patients: A prospective multicenter trial. European Journal of Nuclear Medicine and Molecular Imaging. 2015;**42**(12):1840-1849

[43] Strosberg J, Wolin E, Chasen B, et al. 177-Lu-dotatate significantly improves

progression-free survival in patients with midgut neuroendocrine tumours: Results of the phase III NETTER-1 trial. New England Journal of Medicine. 12 Jan 2017;**376**(2):125-135. DOI: 10.1056/ NEJMoa1607427

[44] Baum RP, Kulkarni HR, Schuchardt C, et al. 177Lu-labeled prostate-specific membrane antigen radioligand therapy of metastatic castration resistant prostate cancer: Safety and efficacy. Journal of Nuclear Medicine. 2016;**57**(7):1006-1013

[45] Mintun MA, Welch MJ, Siegel BA, et al. Breast cancer: PET imaging of estrogen receptors. Radiology. 1988;**169**(1):45-48

[46] Mortimer JE, Dehdashti F, Siegel BA, et al. Metabolic flare: Indicator of hormone responsiveness in advanced breast cancer. Journal of Clinical Oncology. 2001;**19**(11):2797-2803

[47] Mortimer JE, Dehdashti F, Siegel BA, et al. Positron emission tomography with 2-[18F]fluoro-2 deoxy-Dglucose and 16alpha-[18F] fluoro-17beta-estradiol in breast cancer: Correlation with estrogen receptor status and response to systemic therapy. Clinical Cancer Research. 1996;**2**(6): 933-939

[48] Linden HM, Stekhova SA, Link JM, et al. Quantitative fluoroestradiol positron emission tomography imaging predicts response to endocrine treatment in breast cancer. Journal of Clinical Oncology. 2006;**24**(18): 2793-2799

[49] Kurland BF, Peterson LM, Lee JH, et al. Between-patient and withinpatient (site-to-site) variability in estrogen receptor binding, measured in vivo by 18Ffluoroestradiol PET. Journal of Nuclear Medicine. 2011;**52**(10): 1541-1549

[50] Sun Y, Yang Z, Zhang Y, et al. The preliminary study of 16alpha-[18F]

fluoroestradiol PET/CT in assisting the individualized treatment decisions of breast cancer patients. PLoS One. 2015;**10**(1):e0116341

[51] Lee NY, Mechalakos JG, Nehmeh S, et al. Fluorine-18 labeledfluoromisonidazole positron emission and computed tomographyguided intensity-modulated radiotherapy for head and neck cancer: A feasibility study. International Journal of Radiation Oncology, Biology, Physics. 2008;**70**(1):2-13

[52] Langstrom B, Antoni G, Gullberg P, et al. Synthesis of L- and D-[methyl-11C]methionine. Journal of Nuclear Medicine. 1987;**28**:1037-1040

[53] Chung JK, Kim YK, Kim SK, et al. Usefulness of 11C-methionine PET in the evaluation of brain lesions that are hypo- or isometabolic on 18F-FDG PET. European Journal of Nuclear Medicine and Molecular Imaging. 2002;**29**:176-182

[54] Kracht LW, Friese M, Herholz K, et al. Methyl-[11C]-l-methionine uptake as measured by positron emission tomography correlates to microvessel density in patients with glioma. European Journal of Nuclear Medicine and Molecular Imaging. 2003; **30**:868-873

[55] Moulin-Romsee G, D'Hondt E, de Groot T, et al. Non-invasive grading of brain tumours using dynamic amino acid PET imaging: Does it work for 11C-methionine? European Journal of Nuclear Medicine and Molecular Imaging. 2007;**34**:2082-2087

[56] Albersen PC. A challenge to contemporary management of prostate cancer. Nature Clinical Practice Urology. 2009;**6**:12-13

[57] Avazpour I, Roslan RE, Bayat P, et al. Segmenting CT images of bronchogenic carcinoma with bone

metastases using PET intensity markers approach. Radiology and Oncology. 2009;**43**:180-186

[58] Ackerstaff E, Glunde K, Bhujwalla ZM. Choline phospholipid metabolism: A target in cancer cells? Journal of Cellular Biochemistry. 2003;**90**:525-533

[59] Farsad M, Schiavina R, Castelluci P, et al. Detection and localization of prostate cancer: Correlation of (11) C-choline PET/CT with histopathologic stepsection analysis. Journal of Nuclear Medicine. 2005;**46**:1642-1649

[60] Hara T, Kosaka N, Kishi H. PET imaging of prostate cancer using carbon-11-choline. Journal of Nuclear Medicine. 1998;**39**:990-995

[61] Smyczek-Gargya B, Fersis N, Dittmann H, Vogel U, Reischl G, Machulla HJ, et al. PET with [18F] fluorothymidine for imaging of primary breast cancer: A pilot study. European Journal of Nuclear Medicine and Molecular Imaging. 2004;**31**:720-724. DOI: 10.1007/s00259-004-1462-8

[62] Kenny L, Coombes RC, Vigushin DM, Al-Nahhas A, Shousha S, Aboagye EO. Imaging early changes in proliferation at 1 week post chemotherapy: A pilot study in breast cancer patients with 3′-deoxy-3′-[18F] fluorothymidine positron emission tomography. European Journal of Nuclear Medicine and Molecular Imaging. 2007;**34**:1339-1347. DOI: 10.1007/s00259-007-0379-4

[63] Zhang J, Mao F, Niu G, Peng L, Lang L, Li F, et al. Ga-BBN-RGD PET/ CT for GRPR and integrin αβ imaging in patients with breast cancer. Theranostics. 2018;**8**:1121-1130. DOI: 10.7150/thno.22601

[64] Acar E, Turgut B, Yigit S, Kaya G. Comparison of the volumetric and radiomics findings of 18F-FDG PET/CT

#### *PET-CT Imaging and Applications DOI: http://dx.doi.org/10.5772/intechopen.103975*

images with immunohistochemical prognostic factors in local/locally advanced breast cancer. Nuclear Medicine Communications. 2019;**40**:764-772. DOI: 10.1097/ MNM.0000000000001019

[65] Huang SY, Franc BL, Harnish RJ, Liu G, Mitra D, Copeland TP, et al. Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis. npj Breast Cancer. 2018;**4**:24. DOI: 10.1038/ s41523-018-0078-2

[66] Moscoso A, Ruibal A, Dominguez-Prado I, Fernandez-Ferreiro A, Herranz M, Albaina L, et al. Texture analysis of high-resolution dedicated breast 18F-FDG PET images correlates with immunohistochemical factors and subtype of breast cancer. European Journal of Nuclear Medicine and Molecular Imaging. 2018;**45**:196- 206. DOI: 10.1007/s00259-017-3830-1

#### **Chapter 5**

## Feature Extraction Methods for CT-Scan Images Using Image Processing

*Anil K. Bharodiya*

#### **Abstract**

Medical image processing covers various types of images such as tomography, mammography, radiography (X-Ray images), cardiogram, CT scan images etc. Once the CT scan image is captured, Doctors diagnose it to detect abnormal or normal condition of the captured of the patient's body. In the computerized image processing diagnosis, CT-scan image goes through sophisticated phases viz., acquisition, image enhancement, extraction of important features, Region of Interest (ROI) identification, result interpretation etc. Out of these phases, a feature extraction phase plays a vital role during automated/computerized image processing to detect ROI from CT-scan image. This phase performs scientific, mathematical and statistical operations/algorithms to identify features/characteristics from the CT-scan image to shrink image portion for diagnosis. In this chapter, I have presented an extensive review on "Feature Extraction" step of digital image processing based on CT-scan image of human being.

**Keywords:** medical images, CT-scan image, feature extraction, image processing, image diagnoses

#### **1. Introduction**

In recent medical revolution, Computer Aided Diseases Diagnoses (CADD) plays an important role. The basic aim of CADD is to detect diseases on the basis of human image as an input at low cost, better accuracy and patient's satisfaction. There are many bio-medical imaging technologies available such as Radiography, computed tomography (CT-Scan), electrocardiography (ECG), Ultrasound, magnetic resonance imaging (MRI), etc. All these medical imaging modalities are best suited depending on the type of diseases to be detected from human body [1, 2].

In the human body, e.g., arm, leg, scalp, etc., each and every bone plays an important role and function. **Figure 1(a)** shows human being's head CT-scan image; and **Figure 1(b)** shows human being's chest CT-scan image.

CADD system can be developed with the use of image processing. **Figure 2** depicts steps of digital image processing [2].

**Figure 2** shows basic steps to perform digital image processing. Image acquisition is the process of obtaining a digitized image from a real world source using imaging devices e.g., camera, cell phone, CT-scan, MRI, ultrasound etc. Images which are acquired in the first step may be blurred, out of focus or noisy so, in the

#### **Figure 1.**

*(a) Head CT-scan image; and (b) chest CT-scan image. Courtesy: https://images.google.com/.*

#### **Figure 2.**

*Basic steps in digital image processing.*

next step that is image filtering and enhancement which is used to improve the quality of image. This step includes various filtering and enhancement algorithms.

Image quality can also be improved with the use of Image restoration. The main difference between image enhancement and image restoration is that former is subjective and later is objective. Image restoration methods are based on mathematical/ probabilistic models/algorithms of image degradation. While, Image enhancement methods are based on subjective liking of human preference during visualization [3]. The next step is Color Image Processing which deals with feature extraction on the basis of image color. Wavelet is the foundation for image resolution. This step focuses on use of wavelet to perform image resolution analysis. The next step is image compression. This step is used to decrease the size of image so that it can be

stored in minimum space or can be transmitted even on low bandwidth channel. Morphological processing step includes tools for extracting image components that are useful in the step that is representation and description of image shape. The next step is image segmentation, it means dividing the image in constituent segments on the basis of boundary, similarity, color, shape etc.

Representation and description always follow the output of a segmentation step. The first option to be taken is whether to portray the data as a border or a complete region. When the focus is on external shape properties such as corners and inflections, boundary representation is appropriate. When the focus is on internal qualities such as texture or skeletal shape, regional representation is acceptable. A strategy for characterizing the data must also be defined in order to highlight features of interest. Description, also known as feature selection, is the process of selecting features that produce quantitative information of interest or are necessary for distinguishing one object class from another [3]. The last step is object recognition which deals with assigning the label to the object/information extracted during feature extraction step. Finally, the result is displayed in the form of data or image.

The aim of this chapter is to present an extensive research review on feature extraction sub-step of image processing cycle applied to human CT-scan images. The chapter is organized as follows: Section 2 gives a brief of different feature extraction techniques; Section 3 discusses work on CT-scan Image feature extraction; finally, the paper is concluded in Section 4.

#### **2. Feature extraction techniques**

Data/dimensionality reduction, which is performed by intelligently changing the image from the lowest level of pixel data into higher level representations, is a key component in image analysis. We can extract relevant information from these representations through a process known as feature extraction [4].

The ultimate aim in a large number of image processing applications is to extract important features from image data, from which a description, interpretation, or understanding of the scene can be provided by the machine [5].

As per Nixon and Aguado [6] feature extraction techniques are broadly classified into two categories that is low level feature extraction and high level feature extraction. Low-level features extraction deals with basic features that can be extracted automatically from an image without any shape information such as thresholding and edge detection.

*Edge Detection:* It highlights image contrast. Edge is generally boundary of the image objects where intensity of the pixel changes abruptly [6].

*Thresholding:* It chooses pixels within a specified range that have a specific value or arc. If the brightness level (or range) of an object is known, it can be used to locate it within a photograph. This implies that the brightness of the object must also be known [6].

*Detecting image curvature (corner extraction):* Curvature is normally defined by considering a parametric form of a planar curve. This technique is used to detect corner from the image [6].

*Region/patch analysis:* Collection of pixel is usually refers to region of the image. This technique is used to detect particular region on the basis of certain algorithm [6].

*Hough transform:* It defines an efficient implementation of template matching for binary templates. This technique is capable of extracting simple shapes such as lines and quadratic forms as well as arbitrary shapes [6].

*Image motion detection:* In the case of motion there is more than one image. If we have two images obtained at different times, the simplest way in which we can detect motion is by image differencing [6].

*Histogram:* The intensity histogram shows how individual brightness levels are occupied in an image; the image contrast is measured by the range of brightness levels [6].

*Haar wavelets:* Haar wavelets are binary basis functions. There is (theoretically) an infinite range of basis functions. Discrete signals can map better into collections of binary components rather than sinusoidal ones [6].

*Texture extraction:* Texture is an arrangement of pattern after certain interval in the image. Many techniques are used to extract texture from the image such as Local Binary Pattern (LBP), Fourier Transform, Co-occurrence matrices etc. [6, 7].

The above discussion provides brief overview of different techniques that can be used in digital image processing for the feature extraction from digital image. However, it is not an exhaustive discussion of the feature extraction techniques.

#### **3. CT-scan image feature extraction**

A feature extraction is a process through which region of interest (ROI) extracted for analyzing image. It includes modifying the image from the lower level of pixel data into higher level representations. From these higher level representations we can gather useful information; a process called feature extraction [8].

Ma and Wang [9] proposed a novel method to automatically detect the texts embedded in CT-scan Image. Authors have used Histogram of Oriented Gradients (HOG) as a statistical feature descriptor which reflects the distribution of oriented gradients in a selected region. Further, they have adopted AdaBoost classifier to separate the text regions from non-text regions. This method achieved 84% precision rate which is greater than edge base method (45%) and hybrid method (76%).

Shuqi et al. [10] proposed an algorithm to extract local features from mammographic image. In this paper, the SIFT algorithm is combined with the sliding window to extract the ROI region, that is, the breast region, and remove most of the background region. It follows the experimental process as Background de-noising, Using SIFT to extract the key point, Using the SVM and sliding window to detect the ROI position, Extract the features of the ROI region and Design BP neural network. The experimental results show that the accuracy of neural network classifier based on SIFT is 96.57%, which is 3.44% higher than that of traditional SVM classification accuracy.

Poomimadevi and Sulochana [11] presents an automated approach to detect tuberculosis using chest radiographs. The proposed approach basically includes three main steps such as Preprocessing, Registration and watershed segmentation. Lung region is extracted by using registration based segmentation methods. The accuracy of proposed segmentation and global thresholding is 59.8 and 59.4% respectively. While, the accuracy of active contour method is 34.4%. Joykutty et al. [12] also proposed a novel mechanism to detect tuberculosis in chest radiographs. The proposed method includes a three stage process of accurate detection of tuberculosis.

Barabas et al. [13] have developed a software namely Visualizer which allows the viewing of individual CT/MRI image slices, slice reconstruction in various projections, detailed analysis of slices and 3D reconstruction of desired object(s) as well as localization of various anatomical structures for further evaluation of parameters.

Chaudary and Sukhraj et al. [14] have worked on lung cancer detection from CT scan images using image processing steps such as pre-processing, segmentation and feature extraction. In this paper, authors have used MATLAB as image processing tool and concentrated on Area, Perimeter, Roundness and Eccentricity features of image.

Suzuki et al. [15] have used computer aided diagnostic scheme to detect abnormalities from Chest radiograph image of human beings using means of massive training artificial neural network.

Chen and Huang [16] presented an image feature extraction and fusion algorithm based on K-SVD, in order to better fuse CT and MRI images. The sliding window divides images into chunks in this technique. The column vectors are compiled into the dictionary. The K-singular value decomposition (K-SVD) approach is used to learn the redundant dictionary. The image feature fusion is then realized by solving the sparse coefficient matrix for each original picture and then combining sparse coefficient of nonzero members.

Ding et al. [17] have proposed a method based on the exploitation of features closely related to image inherent quality. Specifically, in the novel method, Sobel operator, log Gabor filter and local pattern analysis are employed for complementary representation of image quality. Finally, support vector regression is implemented for the synthesis of the multiple distortion indices and mapping the quantification into an objective quality score.

Litjens et al. [18] presented a survey on deep learning in CT-scan Image analysis. Authors have stated that feature extraction from CT-scan Image can also be done through efficient deep learning algorithm. Kaur and Jindal [19] have worked on OPEN CV Environment to extract features using SURF technique. They have emphasized on the feature extraction phase of content-based image retrieval (CBIR) [20] and concluded that SURF is efficient image processing technique in terms of detect ability, accuracy, rotation and execution time.

According to Hossein and Jacques [21], if prior shape and a straightened boundary image (SBI) based algorithm are applied on CT-scan Image segmentation then, feature extraction will be more easy. Using an adaptive thresholding technique, Oishila et al. [22] provided a tool that first segments the bone region of an input digital CT-scan Image from its surrounding flesh region and then generates the bone contour. It then undertakes unsupervised rectification of bone-contour discontinuities that may have been caused by segmentation mistakes, before detecting the presence of a fracture in the bone.

Seyyed et al. [23] has presented a novel feature which is the combination of shape and texture features. The feature extraction is started by edge and shape information of CT-scan Image then, Gabor filter is used to extract spectral texture features from shape images.

Ratnasari et al. [24] have concentrated on five statistical features like mean, standard deviation, skewness, kurtosis, and entropy to find out the CT-scan Image features for the development of computer applications for identification of lung tuberculosis (TB) disease and concluded that features extraction can be done effectively using combination of thresholding-based ROI template and PCA (Principle Component Analysis) methods.

Kazeminia et al. [25] proposed a novel method to eliminate the non-ROI data from bone CT-scan Images based on the histogram dispersion method. ROI is separated from the background and it is compressed with a lossless compression method. This method contains 3 steps such as Noise Reduction and Smoothing, ROI Boundary Detection and Compression.

Kumar and Bhatia [26] discussed different methods of feature extraction such as Diagonal based feature extraction technique, Fourier descriptor, Principal

**Figure 3.** *Brain CT-scan image processing.*

component analysis (PCA), Independent Component Analysis (ICA), Gabor filter, Fractal theory technique Shadow Features of character, Chain Code Histogram of Character Contour, Finding Intersection/Junctions, Sector approach for Feature Extraction, Extraction of distance and angle features, Extraction of occupancy and end points features, Transition feature and Zernike Moments.

As per Dubey et al. [27] edge detection techniques are also used for feature extraction. These techniques can be pewitt, sobel, Rober, Kirsch, Robinson, Marr-Hildreth, LoG, Canny etc.

**Figure 3** shows image processing of human's brain CT-scan image. As per Kumar and Bhatia [26] and Dubey et al. [27], authors have implemented Gabor filter and edge detection technique to process the human brain CT-scan image in order to detect cancerous part of the brain. **Figure 3** is divided into 6 different sub-images as an output generated from the computerized digital image processing. In the first step original captured CT-scan image is fed to the system, image pre-processing and enhancement are conducted in the second step, edge detection using canny and prewitt method are done in the third step, fourth step focus on the Gabor filter in order to detect ROI, fifth step focuses on feature extraction using BLOB (binary large object) analysis and in the last that is step number 6 produces the final output image. Pseudocode of this process is given below:

```
Pseudocode: Human's brain CT-Scan image processing
READ CT-Scan image
CONVERT an inputted image into gray scale image(If RGB)
DO Pre-Processing and Image Enhancement
Do Edge detection using canny & prewitt methods
APPLY Gabor filter to detect ROI
DETECT features using BLOB analysis
DISPLAY processed CT-Scan image as an output
```
## **4. Conclusion and future attempts**

X-Ray and CT-scan images is an important medical imaging component to detect bone related issues and diseases. Many researchers have shown their interest to work in the field of X-Ray image processing. The broad survey presented in the above section III proves that researchers have worked in features extraction from human being's X-Ray and CT-scan images. This research review is further useful for researchers to develop automatic application or decision support system to analyze human being's X-Ray and CT-scan images to detect bone related diseases such bone fracture identification, fatigue of knee joint, bone age assessment, lung module diagnoses, osteoporosis, arthritis, bone tumor, bone infection etc.

#### **Author details**

Anil K. Bharodiya UCCC & SPBCBA & SDHG College of BCA & IT, Surat, Gujarat, India

\*Address all correspondence to: anilbharodiya@gmail.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **References**

[1] Bharodiya AK, Gonsai AM. Research review on human being's X-ray image analysis through image processing. In: Proceedings of National Conference on Sustainable Computing and Information Technology. Surat: SCET; 2017. pp. 38-42

[2] Bhowmik M, Ghoshal D, Bhowmik S. Automated medical image analyser. In: IEEE ICCSP 2015 Conference. New York: IEEE; 2015. pp. 0974-0978

[3] Gonzalez R, C., and Woods, R., E. Digital Image Processing. USA: PE & PH; 2008. pp. 1-34

[4] Mohamed MH, AbdeISamea MM. An efficient clustering based texture feature extraction for medical image. In: IEEE Proceedings of International Workshop on Data Mining and Artificial Intelligence, Bangladesh. New York: IEEE; 2008. pp. 88-93

[5] Jain AK. Fundamentals of Digital Image Processing. USA: PE & PH; 1989. pp. 342-425

[6] Nixon MS, Aguado AS. Feature Extraction & Image Processing for Computer Vision. Third ed. UK: Elsevier; 2012. pp. 137-212

[7] Rogers LF, Talianovic MS, Boles CA, et al. Grainger & Allison's Diagnostic Radiology: A Textbook of Medical Imaging. New York: Churchill Livingstone, Chap; 2008. p. 46

[8] Kodogiannisa VS, Boulougourab M, Wadgea E, Lygourasc N. The usage of soft-computing methodologies in interpreting capsule endoscopy. Elsevier Engineering Applications of Artificial Intelligence. 2007;**20**(4):539-553

[9] Ma Y, Wang Y. Text detection in medical images using local feature extraction and supervised learning. In: IEEE 12th International Conference On Fuzzy Systems and Knowledge Discovery (FSKD). New York: IEEE; 2015. pp. 953-958

[10] Shuqi C, Shen C, et al. Application of neural network based on SIFT local feature extraction in medical image classification. In: IEEE 2nd International Conference on Image, Vision and Computing. New York: IEEE; 2017. pp. 92-97

[11] Poomimadevi CS, Sulochana HC. Automatic detection of pulmonary tuberculosis using image processing techniques. In: IEEE WiSPNET Conference. New York: IEEE; 2016. pp. 798-802

[12] Joykutty B, Satheeshkumar KG, Samuvel B. Automatic tuberculosis detection using adaptive Thresholding in chest radiographs. In: IEEE International Conference on Emerging Technological Trends. New York: IEEE; 2016

[13] Barabas J, Capka M, Babusiak B, et al. Analysis, 3D reconstruction and anatomical feature extraction from medical images. In: IEEE International Conference on Biomedical Engineering and Biotechnology. New York: IEEE; 2012. pp. 731-735

[14] Chaudhary A, Sukhraj SS. Lung cancer detection on CT images by using image processing. In: IEEE International Conference on Computing Sciences. New York: IEEE; 2012. pp. 142-146

[15] Suzuki K et al. False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Academic Radiology. 2005;**12**(2): 191-201

[16] Chen H, Huang ZH. Medical Image Feature Extraction and Fusion

#### *Feature Extraction Methods for CT-Scan Images Using Image Processing DOI: http://dx.doi.org/10.5772/intechopen.102573*

Algorithm Based on K-SVD. In: IEEE Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing. New York: IEEE; 2014. pp. 333-337

[17] Ding Y, Zhao Y, Zhao X. Image quality assessment based on multifeature extraction and synthesis with support vector regression. Elsevier Signal Processing: Image Communication. 2017;**54**:81-92

[18] Litjens G, Kooi T, Babak EB, et al. A survey on deep learning in medical image analysis. Elsevier Medical Image Analysis. 2017;**42**:60-88

[19] Kaur B, Jindal S. An implementation of feature extraction over medical images on OPEN CV environment. In: IEEE International Conference on Devices, Circuits &Communications. New York: IEEE; 2014

[20] Swati VS, Vrushali GN. Design of Feature Extraction in content based image retrieval (CBIR) using color and texture. International Journal of Computer Science & Informatics. 2011;**I**(II):57-61

[21] Hossein MM, Jacques AD. Enhanced X-ray image segmentation method using prior shape. IET Computer Vision. 2017;**11**(2):145-152

[22] Oishila B, Arindam B, Bhargab BB. Long-bone fracture detection in digital X-ray images based on digital-geometric techniques. Elsevier Computer Methods and programs in Biomedicine. 2016;**123**:2-14

[23] Seyyed MM, Mohammad SH, et al. Novel shape texture feature extraction for medical X-ray image classification. International Journal of Innovative Computing, Information and Control. 2012;**8**(1-B):659-673

[24] Ratnasari NR, Adhi S, Indah S, et al. Thoracic X-ray features extraction using thresholding-based ROI template and PCA-based features selection for lung TB classification purposes. In: IEEE 3rd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME). New York: IEEE; 2013. pp. 65-69

[25] Kazeminia N, Karimi SM, Soroushmehr R, et al. Region of interest extraction for lossless compression of bone X-ray images. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). New York: IEEE; 2015. pp. 3061-3064

[26] Kumar G, Bhatia PK. A detailed review of feature extraction in image processing systems. Fourth International Conference on Advanced Computing & Communication Technologies. 2014;**2014**:5-12. DOI: 10.1109/ACCT.2014.74

[27] Dubey P, Dubey PK, Changlani S. A hybrid technique for digital image edge detection by combining second order derivative techniques log and canny. In: 2nd International Conference on Data, Engineering and Applications (IDEA). New York: IEEE; 2020. pp. 1-6. DOI: 10.1109/IDEA49133.2020.9170672

## *Edited by Reda R. Gharieb*

A computed tomography (CT) scan uses X-rays and a computer to create detailed images of the inside of the body. CT scanners measure, versus different angles, X-ray attenuations when passing through different tissues inside the body through rotation of both X-ray tube and a row of X-ray detectors placed in the gantry. These measurements are then processed using computer algorithms to reconstruct tomographic (crosssectional) images. CT can produce detailed images of many structures inside the body, including the internal organs, blood vessels, and bones. This book presents a comprehensive overview of CT scanning. Chapters address such topics as instrumental basics, CT imaging in coronavirus, radiation and risk assessment in chest imaging, positron emission tomography (PET), and feature extraction.

Published in London, UK © 2022 IntechOpen © Andrei Orlov / iStock

Computed-Tomography (CT) Scan

Computed-Tomography

(CT) Scan

*Edited by Reda R. Gharieb*