**3. High-performance medical imaging system**

Imaging has become an essential tool in modern medicine science. Numerous powerful platforms to register, store, analyze, and process medical imaging applications have appeared in recent years. However, these systems are developed for a specific imaging application problem [19–21] and do not support generalized problems [22], which includes an indigenous system that could be customized according to the local needs. Moreover, the cost of such systems is on higher side that contributes to the increase in expenses of each scan. This results in discouraging patients, who could not afford these high costs and therefore compromise on their health. Also, in many medical imaging systems such as MRI, temporal resolution has been reported to be a major concern, which causes discomfort to patients with severe illness/pain as they have to be motionless for a longer period of time. In this chapter, a high-performance medical imaging system (HPMIS) is proposed that registers stores and processes complex and multidimensional medical imaging application in real-time. The high-performance medical imaging system provides a user-friendly programming environment and high-performance architecture to perform imaging data acquisition, registration, storage, analysis, and performs segmentation, filtering, and recognition for complex real-time complex and multidimensional medical images or videos. The proposed system is highly reliable concerning cost, performance, and power. High-performance medical imaging system architecture (shown in **Figure 1**) is implemented and tested on advanced heterogeneous multi-core and GPU-based systems.

**Figure 1.** High-performance medical imaging system.

The proposed high-performance medical imaging system (HPMIS) has five sub systems: registration system, memory system, processing system, programming toolkit, and test applications.

#### **3.1. Registration system**

Pratx et al. [12] proposed a method for processing line-projection tasks to process the PET image reconstruction. The proposed method uses Nvidia processing cores and the CUDA programming model. Owens et al. [13] addressed the implementation of RISC processors on GPU cores-based processing system architecture. The authors showed the value of GPU for the tremendous compute capacity that reproduces the CT images and presents them on

Jiang et al. [14] suggested processing of 3D discrete transformation using hardware accelerator. The proposed system decreases the size of the accelerators lookup tables. The accelerators are developed in hardware description language and examined on Xilinx Virtex-E FPGA

Coric et al. [15] displayed a CT-based parallel beam back-projection algorithm and tested on FPGA-based hardware architecture. The FPGA-based system obtains the speedups up to 100×

Tassadaq et al. proposed programmable graphics controller [16, 17] for low-cost and lowpower graphics system. The system takes two-dimensional images to process applications. Tassadaq et al. [18] also have proposed a visual processing system called ViPS for medical applications. The ViPS uses multiple RISC, vector processor, and multiple FPGA-based hardware accelerators. The ViPS gives a high performance by using advanced hardware architec-

Imaging has become an essential tool in modern medicine science. Numerous powerful platforms to register, store, analyze, and process medical imaging applications have appeared in recent years. However, these systems are developed for a specific imaging application problem [19–21] and do not support generalized problems [22], which includes an indigenous system that could be customized according to the local needs. Moreover, the cost of such systems is on higher side that contributes to the increase in expenses of each scan. This results in discouraging patients, who could not afford these high costs and therefore compromise on their health. Also, in many medical imaging systems such as MRI, temporal resolution has been reported to be a major concern, which causes discomfort to patients with severe illness/pain as they have to be motionless for a longer period of time. In this chapter, a high-performance medical imaging system (HPMIS) is proposed that registers stores and processes complex and multidimensional medical imaging application in real-time. The high-performance medical imaging system provides a user-friendly programming environment and high-performance architecture to perform imaging data acquisition, registration, storage, analysis, and performs segmentation, filtering, and recognition for complex real-time complex and multidimensional medical images or videos. The proposed system is highly reliable concerning cost, performance, and power. High-performance medical imaging system architecture (shown in **Figure 1**) is imple-

tural support such as registration system, memory system, and processing system.

mented and tested on advanced heterogeneous multi-core and GPU-based systems.

against the software running on a 1 GHz Pentium.

4 Medical Imaging and Image-Guided Interventions

**3. High-performance medical imaging system**

screen.

board.

The registration system (RS) deals with a number of medical imaging devices with various interfaces. The RS supports multidimensional and scattered graphics data. The RS manages X-ray radiography, ultrasonic images, etc. and complex images such as MRI, CT, etc. The RS utilizes a RISC core and FPGA accelerator to access data from medical imaging devices. The RISC core is employed to obtain medical images having a complex structure, whereas the FPGA core is used to gather data having fixed data flow patterns. The registration system aligns images reasonably isotropic resolution and do not distort or deform the anatomical and pathological structures of images. The system is designed for navigation and visualization of multimodality and multidimensional images for 2D/3D, 4D Cardiac-CT and 5D Cardiac-PET-CT displays. The registration system supports all DICOM Files (mono-frame, multi-frames), the MRI/CT multi-frame format, JPEG Lossy, Lossless, LS and 2000, RLE, Monochrome1, Monochrome2, RGB, YBR, Planar and Palettes. The system supports 32-bit pixel resolution and is directly linked with memory system.

#### **3.2. Memory system**

The HPMIS memory system uses three types of memories: the program memory, the specialized medical memory, and the main memory. The HPMIS program memory utilizes the memory access descriptors [APMC][PPMC][PMC] that contain the knowledge of imaging data registration, data transfers, storage device, and processing cores. The descriptors provide the medical imaging programmer to choose a processing core for image processing and explain the complicated image structure.

**3.3. Processing system**

**3.4. Programming**

The processing system is used to perform image processing on images stored in memory system. The medical application-specific hardware accelerator, vector processor, and the multi

A High-Performance System Architecture for Medical Imaging

http://dx.doi.org/10.5772/intechopen.83581

7

The HPMIS Medical Application Programming Model (MAPM) helps medical application programmers to develop applications without acknowledging hardware arrangements and details. The MAPM uses image processing functions for image segmentation, reconstruction, features extraction, and computation; and it also provides memory management and registration task and separate task for visualization. The MAPM presents various function calls, which include medical imaging flow, control, and processing. The MAPM data transfer tasks help disordered, random, strider 1D, 2D, 3D, and automated blocking for image/video transfer processes and move data between the medical imaging devices and the HPMIS memory system. The HPMIS MAPM intelligently pipeline, overlap, and parallelize image processing tasks based on hardware processing and memory resources. **Table 1** presents functions that are used to develop the applications for the HPMIS architecture. The HPMIS incorporates 100 function calls for medical applications. The application calls are written in C/C++. The func-

tion allows the programmer to execute the function on RISC or Vector processors.

**Table 1.** C/C++ device drivers to program/operate HPMIS.

RISC processor cores are used in the HPMIS processing system.

**Figure 2.** High-performance specialized medical memory.
