*4.2.1. Channel-model*

284 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications

**resonance imaging** 

**4.1. Cardiac magnetic resonance imaging** 

from the acquired complex valued MR signals.

is applied for freezing respiratory motion [61],[62].

artifacts occur

**4. Remote organ motion tracking and its application in magnetic** 

Magnetic resonance imaging (MRI) is arguably the most innovative imaging modality in cardiology and neuroscience. It is based on the detection of precessing nuclear spins, mostly from protons of tissue water, in a strong static magnetic field. Using two additional kinds of magnetic fields, the position of the spins inside the human body can be encoded. To this end, the nuclear spin system is excited by resonant RF pulses at the precession frequency of the spin system. After excitation a macroscopic RF signal can be detected by an RF coil providing amplitude and phase information of the precessing nuclear magnetization. Applying additional magnetic field gradients the spin positions can be encoded by generating a well-defined spatial variation of the precession frequencies. Proper sequencing of spin excitation and gradient switching allows the reconstruction of 2D and 3D images

MRI data depend crucially on a multitude of physical parameters, e.g. moving spins will cause an additional phase modulation of the signal. One consequence is that MR images of the moving heart or of large vessels with pulsatile blood flow are severely distorted in the whole field of view. Hence, cardiac MRI (CMR) is seriously impaired by cardiac and respiratory motion when no proper gating with respect to both relevant motion types, cardiac and respiratory motion, is applied (Fig. 25). In clinically approved CMR procedures, electrocardiography (ECG) or pulse oximetry are used for cardiac gating and breath holding

**Figure 25.** MR image (short axis view) of a human heart. a) Cardiac gating by pulse oximetry and breath hold; b) cardiac gating only, due to free breathing during image acquisition severe image

However, there are unmet needs of clinical CMR, particularly for high (≥ 3 T) and ultra-high (≥ 7 T) field MRI. Higher magnetic fields offer the chance to acquire images of better spatial resolution [63], but on the downside the ECG signal is increasingly perturbed by the magneto-hydrodynamic effect [64] until it becomes effectively useless for cardiac gating at For modeling purposes, the human body can be approximated as a multilayered dielectric structure with characteristic reflection coefficients (*f*) (s. Fig. 26) [29], [71], [72]. The UWB signal, which can be a pulse or a pseudo-noise sequence [71] of up to 10 GHz bandwidth, is transmitted utilizing appropriate pulse-radiating antennas *T*x (e.g., Double Ridged Horn or Vivaldi antennas). The reflected signal is detected by *R*x, and the first step in further signalprocessing usually is to calculate the correlation signal *RXY*() between received signal *S*Rx and transmitted signal pulse *S*Tx is [71], [72]. This represents the impulse response function (IRF) including the transfer functions of the antennas. By UWB measurement of the motion of a multi-layered dielectric phantom [29], the changes of reflections on the single interfaces can be found. Therefore, the signal variance M() of the correlation signal *RXY*() is calculated.

286 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications

ultraMEDIS – Ultra-Wideband Sensing in Medicine 287

of the multilayered dielectric structure

Tx = *E*r is archived and, accordingly, the

(

*, t*) to the variation of a certain internal

) can be calculated

, *t*) was calculated just

. The \* symbol represents the convolution operator.

logical to ask whether these oscillations are detectable by UWB radar. Due to the simultaneous occurrence of the intracranial displacement and the vibration of the whole head, decomposing both signals requires sophisticated methods. As an initial step towards the solution to this problem, we need to get a feeling for the change in the acquired UWB reflection signal due to an intracranial oscillation. An analytical approach [71], [72] was applied which models the signal path and the oscillating stratified arrangement of the brain to get signals free of any interfering compositions. Figure 28 schematically depicts the set-up used to probe the human body with a UWB device, where *S*Tx symbolizes the excitation

Tx its temporal derivative representing the free space signal *E*i in the channel. By

 \* *S*

Therefore, the spectral response of a dielectric medium is appropriately described in terms of a multiple Cole-Cole dispersion, which – by choosing parameters appropriate to each constituent - can be used to predict the dielectric behavior over the desired frequency range

> (

, *t*), which is done by a sinusoidal oscillation of the white matter. Accordingly, the

interface can be analyzed [68], [73]. We simulated the physiological event by variations of

as its variation after a certain propagation time. The reconstruction of the intracranial motion applying the reconstruction algorithm proposed in [72] gave us a maximum deviation from the reference oscillation of about 4%. We conclude that the detection of intracranial oscillations using non-contact UWB is indeed feasible [72], [73]. It must be noted that for all real medical applications of this broadband technique trying to monitor variations of the body's interior, sophisticated signal processing techniques must be applied to decompose signals originating from the body's surface and signals originating from deeper sources [74]. The influence of the antenna's transfer function, in contrast, is less of an

signal and *S*

with *S*

( received current signal *S*Rx = (

the convolution of the impulse response function

Tx, the reflected electric field component

 \* *S*Tx)

**Figure 28.** Signal path model for the current transfer function *S*Rx/*S*Tx.

[71]. For such a layered arrangement, the reflection coefficient

cerebro spinal fluid varies antipodally [76]. The correlation result *Rxy*(

recursively. In this manner, the response of

**Figure 26. a)** 14-layer arrangement to mimic the reflective properties of the human thorax (not to scale). *Ei/Er*, *Hi/Hr*: incident/reflected electric/magnetic field component. *ki*: wave vector of incident wave. **b)** Top: calculated magnitude of the reflection response |(*f*)|, which is proportional to the frequency response function (FRF) of the object. Middle: unwrapped phase (*f*) of the reflection response (*f*). Bottom: normalized time domain representation (*t*) of (*f*) impulse response function (IRF) of the reflection response.

**Figure 27.** Physiological signatures received by the algorithm described in Ref. [29]. Top: Signal variance M().Bottom: Physiological signatures corresponding to the label local maxima of M().Top right: Measured and simulated correlation signal *Rxy*().

Bottom right: Radargram of the measured and simulated correlation signal *Rxy*().

The maxima in M() correspond to the interfaces containing a considerable difference in the permittivity or are close to the illumination side if the transfer functions of the antennas are removed by de-convolution. By these maxima, the time signals corresponding to the interfaces can be extracted [29]. An example of simulated and measured correlation signal *Rxy*() is given in Fig. 27, top right.

### *4.2.2. Analytical simulation of the intracranial pulsation detection*

It is well known that simultaneously to the head's vibrations intracranial oscillations with spatial varying amplitude occur, induced by physiological sources [73]. Thus, it is only logical to ask whether these oscillations are detectable by UWB radar. Due to the simultaneous occurrence of the intracranial displacement and the vibration of the whole head, decomposing both signals requires sophisticated methods. As an initial step towards the solution to this problem, we need to get a feeling for the change in the acquired UWB reflection signal due to an intracranial oscillation. An analytical approach [71], [72] was applied which models the signal path and the oscillating stratified arrangement of the brain to get signals free of any interfering compositions. Figure 28 schematically depicts the set-up used to probe the human body with a UWB device, where *S*Tx symbolizes the excitation signal and *S*Tx its temporal derivative representing the free space signal *E*i in the channel. By the convolution of the impulse response function of the multilayered dielectric structure with *S*Tx, the reflected electric field component \* *S*Tx = *E*r is archived and, accordingly, the received current signal *S*Rx = ( \* *S*Tx). The \* symbol represents the convolution operator.

**Figure 28.** Signal path model for the current transfer function *S*Rx/*S*Tx.

286 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications

Top: calculated magnitude of the reflection response |

Bottom: normalized time domain representation

reflection response.

variance M(

*Rxy*( The maxima in M(

right: Measured and simulated correlation signal *Rxy*(

) is given in Fig. 27, top right.

response function (FRF) of the object. Middle: unwrapped phase

**Figure 26. a)** 14-layer arrangement to mimic the reflective properties of the human thorax (not to scale). *Ei/Er*, *Hi/Hr*: incident/reflected electric/magnetic field component. *ki*: wave vector of incident wave. **b)**

> (*t*) of

**Figure 27.** Physiological signatures received by the algorithm described in Ref. [29]. Top: Signal

Bottom right: Radargram of the measured and simulated correlation signal *Rxy*(

*4.2.2. Analytical simulation of the intracranial pulsation detection* 

).Bottom: Physiological signatures corresponding to the label local maxima of M(

).

permittivity or are close to the illumination side if the transfer functions of the antennas are removed by de-convolution. By these maxima, the time signals corresponding to the interfaces can be extracted [29]. An example of simulated and measured correlation signal

It is well known that simultaneously to the head's vibrations intracranial oscillations with spatial varying amplitude occur, induced by physiological sources [73]. Thus, it is only

) correspond to the interfaces containing a considerable difference in the

(*f*)|, which is proportional to the frequency

(*f*) impulse response function (IRF) of the

(*f*) of the reflection response

). (*f*).

).Top Therefore, the spectral response of a dielectric medium is appropriately described in terms of a multiple Cole-Cole dispersion, which – by choosing parameters appropriate to each constituent - can be used to predict the dielectric behavior over the desired frequency range [71]. For such a layered arrangement, the reflection coefficient () can be calculated recursively. In this manner, the response of (*, t*) to the variation of a certain internal interface can be analyzed [68], [73]. We simulated the physiological event by variations of (, *t*), which is done by a sinusoidal oscillation of the white matter. Accordingly, the cerebro spinal fluid varies antipodally [76]. The correlation result *Rxy*(, *t*) was calculated just as its variation after a certain propagation time. The reconstruction of the intracranial motion applying the reconstruction algorithm proposed in [72] gave us a maximum deviation from the reference oscillation of about 4%. We conclude that the detection of intracranial oscillations using non-contact UWB is indeed feasible [72], [73]. It must be noted that for all real medical applications of this broadband technique trying to monitor variations of the body's interior, sophisticated signal processing techniques must be applied to decompose signals originating from the body's surface and signals originating from deeper sources [74]. The influence of the antenna's transfer function, in contrast, is less of an issue for real applications. For simplicity, we had assumed an ideal transfer function in the above simulation but non-ideal antenna behavior can be extracted from the received signal by using proper de-convolution techniques. Furthermore, the time courses of the ideal channel can be regained [72].

ultraMEDIS – Ultra-Wideband Sensing in Medicine 289

of reconstructed physiological signatures from deep sources by finding the optimized antenna position regarding the better penetrability of selected body areas. This, of course, requires the adaptation of the model to the actual thorax geometry of the patient as obtained

We compared the motion detection by variance calculation in a combined MRI/UWB measurement using appropriate MR-compatible phantoms [29]. The dielectric phantoms were arranged in a sandwich structure to mimic the sequence of biological tissue layers of

**Figure 30. a)** Set-up of the combined MRI/UWB measurement; **b)** Comparison of the reference profile with the data obtained simultaneously from MR and UWB radar measurements. The profiles are offset

Such a sandwich was placed in a moveable sledge-like fixture inside a birdcage MR head coil. The motion profile of the sandwich structure was shaped to approximate respiratory motion of the thorax superimposed by cardiac oscillations (Fig. 30). An M-sequence UWB-Radar system (up to 5 GHz) [76] and MR compatible UWB antennas [10], [32] were utilized to detect the motion of the phantom inside a 3-T MR scanner (Bruker MEDSPEC 30/100). A flow-compensated gradient echo CINE (time resolution 50 ms) sequence was used to reduce

Additionally, the physiological signatures monitored by UWB-radar were validated by comparison to simultaneously acquired MR measurements on the same subject [13], [29],

Subject motion appears to be a limiting factor in numerous MR imaging applications especially in the case of high and ultra-high fields, e.g. high-resolution functional MRI (fMRI). For head imaging the subject's ability to maintain the same head position is limiting the total acquisition time. This period typically does not exceed several minutes and may be

by MRI scans.

the human thorax.

for clarity.

**4.3. Detection of motion by UWB radar** 

*4.3.1. Motion detection for a multilayered phantom* 

artifacts generated by the phantom movements.

[77] (cf. Section 4.5.2 and 4.6).

*4.3.2. Detection of micro motion* 
