Simulation of Human Respiration in fmri With a Mechanical Model

Transcription

1 700 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 49, NO. 7, JULY 2002 Simulation of Human Respiration in fmri With a Mechanical Model Jared R. Brosch, Member, IEEE, Thomas M. Talavage*, Member, IEEE, John L. Ulmer, and John A. Nyenhuis, Senior Member, IEEE Abstract Obtaining functional magnetic resonance images of the brain is a challenging measurement process having a low characteristic signal-to-noise ratio. Images contain various forms of noise, including those induced by physiologic processes. One of the prevalent disturbances is hypothesized to result from susceptibility fluctuations caused by abdominal volume changes during respiration. To test this hypothesis and characterize the contribution of respiration noise to both magnitude and phase images, a mechanical model of a respiring human was constructed. The model was tested by comparing data from the model with that of a resting human. Power spectrum analyses show that the model induces both phase and magnitude disturbances similar to those in the human. The disturbances are directly related to the frequency of the respiration, with the noise most prevalent in the phase images. Though magnitude image noise is hard to identify in the human, the manikin demonstrates the presence of this disturbance. The construction of the manikin rules out motion as the primary source of the observed fluctuations and variation of the electrical properties of the manikin also indicates that signal fluctuations are not primarily due to eddy currents. Therefore, the changes are most probably induced by bulk susceptibility changes correlating with respiration. Index Terms fmri, noise, respiration. I. INTRODUCTION ONE of the limiting aspects of functional magnetic resonance imaging (fmri) is the generally low signal-to-noise ratio (SNR) inherent in the data collection process [1]. The characterization of the physiologic noise present in fmri data has been a topic of notable interest over the past decade [2] [5]. These investigations have been primarily driven by the goal of developing an accurate noise model so that the statistical signal processing methodology for response detection and estimation may be improved. One of the most easily detected and categorized components of the noise is that which is related to the respiratory cycle. The noise in fmri data resulting from the respiration of the patient, first investigated in detail in [3], is primarily observed in the Manuscript received August 2, 2001; revised February 12, Asterisk indicates corresponding author. J. R. Brosch is with the Department of Biomedical Engineering, Purdue University, West Lafayette, IN, USA. *T. M. Talavage is with the Department of Biomedical Engineering and also with School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA ( tmt@ecn.purdue.edu). J. L. Ulmer is with the Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA. J. A. Nyenhuis is with the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA. Publisher Item Identifier S (02) phase of the acquired signal, with minimal apparent presence in the magnitude data. The predominance of the noise in the phase has been hypothesized to result from variations in, caused by movements of the chest wall and internal organs, leading to variation in the Larmor frequency in the brain [3]. This theory has been disputed by others (e.g., [6]) proposing instead that the major contribution of respiration to the noise is induced motion of the head and the brain parenchyma. It should be noted, however, that inspiration of pure oxygen has resulted in the observation of magnitude fluctuations between 5% and 60% of baseline levels, indicating that the susceptibility of the inhaled air may also contribute to the respiratory noise [7]. In spite of the uncertain origin of the respiration-induced noise and its small apparent contribution to the magnitude images [3], [8], [9], previous work has demonstrated that the removal of noise at the respiration rate leads to appreciable improvements in both SNR and the detection of fmri responses (e.g., [9]). Various techniques for the reduction of respiratory noise in fmri have been investigated, with mixed results. The most prevalent technique has been to postprocess using external measurements of the respiratory waveform [4], [6], [9] [11]. Also, having been proposed as compensatory techniques are navigator echo pulse sequences to account for field fluctuations as if the head were in motion [6] and postprocessing using over-sampling of the respiratory rate [8], [12]. External measurements of the respiratory waveform, obtained using a bellows around the chest yield the greatest success in postprocessing removal of respiration-induced noise. However, the additional complexity introduced by the use of external measuring devices is generally unattractive due to the extra time required to set up a bellows apparatus and the need to synchronize multiple modalities of data collection. This approach does offer the advantage that it may be conducted independent of the rate at which images are acquired, as the second data-acquisition technique is independent of the imaging process. The potential extra burden of acquiring a second set of data is eliminated when using navigator echo techniques to compensate for the field variations induced by respiration (e.g., [6]). While demonstrated to be of some benefit when using the FLASH technique, the success of this approach in echo-planar fmri has not been proven. From a theoretical perspective, this approach is likely confounded by findings such as those of Bodurka et al. [13] demonstrating that the noise induced by respiration varies as a function of position in three-dimensional space. This suggests that the treatment of respiration-induced field changes as a rigid body motion will not be optimal /02$ IEEE

2 BROSCH et al.: SIMULATION OF HUMAN RESPIRATION IN fmri WITH A MECHANICAL MODEL 701 Given the potentially prohibitive complexity of acquisition-time compensation for respiration-induced field fluctuations, the operationally ideal mechanism of reducing respiration-induced noise would appear to be a data-driven, postprocessing procedure. A procedure of this nature requires no additional modalities of data to be collected and may be implemented with a minimum of modification to the typical fmri data-acquisition process. For example, this process may be implemented through the use of a short TR to over-sample the respiration cycle [6 14 breaths per minute (bpm)], permitting the detection and reduction of respiration-induced noise without the acquisition of a reference waveform [12]. Such a data-driven procedure is enabled by the acquisition of image data above the Nyquist rate of the respiratory rhythm. This estimation-based approach is, however, difficult to implement if only the magnitude images are acquired, given the typical observation that the energy at the respiratory rate is within the noise floor of the magnitude images in fmri data obtained on human subjects [14]. Further, the time-based technique implemented in [12] does not permit effective estimation of the respiration waveform if the data are under-sampled. In order for an effective, standardized technique for the removal of respiration-induced fmri noise to be developed, it is apparent that a clearer characterization of the respiratory disturbance in the fmri data is required. As demonstrated above, the techniques currently utilized to reduce the contribution of the respiratory cycle to the noise in fmri data have either not been entirely successful (e.g., navigator echoes) or do not incorporate sufficiently detailed models so that the remaining noise may be characterized (e.g., over-sampled fmri data). Therefore, even the most optimal (in terms of complexity of acquisition) procedures may fail to properly reduce significant components of the respiratory noise in the fmri data. We have developed a mechanical model of human respiration that will assist us in answering fundamental questions about the contribution of respiration-induced noise in the acquired magnitude and phase images and in developing automated, data-driven procedures that will compensate for respiratory noise artifacts. Our mechanical model mimics the physical fluctuations of the abdomen of a supine, respiring human, permitting more accurate modeling of the physical cause of respiratory artifacts than may be achieved in vivo. II. JUSTIFICATION FOR A MECHANICAL MODEL No active physical model has previously been utilized to answer questions regarding the origin, characterization and removal of respiratory noise. Instead, the prior research into respiratory artifacts has been performed almost exclusively on awake, behaving subjects. Two passive models have been presented in the literature. The first is an electrical model (a current-carrying wire in a gel cylinder) created by Bodurka et al. [13] to induce small field fluctuations to demonstrate the associated image distortion. The second, by Raj et al. [15], is a static spherical chamber in which concentration of oxygen is varied to mimic changes in the bulk susceptibility induced by respiration effectively testing the effect of the gaseous oxygen concentration in the chest (lungs) rather than at the head, as in Bates et al. [7]. A. Benefits of the Use of a Mechanical Model The development of a mechanical model can be useful for three primary reasons, each of which we will address in turn. 1) Absence of Other Physiologic Noise Sources: The mechanical model will be free from noise induced by the cardiac cycle (e.g., [16]) or other physiological processes. Inherently, the baseline physiologic noise (including the undetermined lowfrequency noise (e.g., [17]) is missing from data acquired on an inert phantom. These noise components are generally thought to arise from the flow of blood, the firing of neurons, and tissue heterogeneity within a particular slice [2], [3]. 2) Mechanism by Which Respiration Induces Noise: A mechanical model may permit the evaluation of competing theories of how the physical activity of respiration affects the imaging procedure. The two primary theories previously proposed are that the change in volume of the patient leads to small field fluctuations [3] or that the noise arises from physical motion of the head that correlates with the respiratory cycle [6]. A third theory may also be proposed in which eddy currents arising as the chest moves in the static field lead to field fluctuations at the level of the head. The latter two of these theories may be directly tested using the mechanical model. Isolation of the imaged head (a water phantom) from the mechanical model that mimics respiration can definitively answer whether head motion is the primary source of the respiratory noise. Changing the resistivity of the model will effect a change in the strength of the eddy currents induced by volumetric changes in the static field. Therefore, an increase in the resistivity of the model should greatly reduce field fluctuations if eddy currents contribute significantly to the respiratory artifact. Regardless of the theory being tested, it should be noted that some human physiologic characteristics that may contribute to the respiratory noise must necessarily be lost. First, the many blood vessels, bones, and tissues connecting the head and trunk will be absent in the model and, second, when a human inhales, both blood pressure and heart rate slightly increase. Whether these two ignored characteristics have an effect on the images is unknown. For the purpose of this investigation, the contributions to the noise from these physiologic sources will be assumed to be insignificant relevant to the contributions from potential movement of the abdomen. 3) Manifestation of Respiration-Induced Noise: In the absence of additional physiologic fluctuations and the low-frequency noise found in the magnitude power spectra, a mechanical model should more clearly exhibit respiratory artifacts in both the magnitude and phase images. As first noted by Noll et al. [3], although respiratory artifacts can be seen in both the magnitude and phase of the received signal, the contribution is most clearly observed in the phase images. This phase dominance has been noted in many papers, but the extent of the respiratory artifact in the magnitude remains unclear, but may be significant [7]. Characterization of the respiratory noise contribution to the magnitude and phase images may make apparent a deterministic relationship between the amplitude and temporal phase of these disturbances. Because functional imaging results are magnitude image dependent, it is of great importance to identify the extent to which the respiratory artifact is manifest

3 702 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 49, NO. 7, JULY 2002 in the magnitude data. The intensity value of any given magnitude or phase image voxel is subject to change if any portion of the imaged volume experiences heterogeneity in the magnetic field, because the signal obtained during MR imaging is essentially the two-dimensional (2-D) Fourier transform of the resulting image. Therefore, treatment of the net magnetic moment within a voxel as a rigid vector is not accurate and the relationship between the magnitude and phase image values for a given voxel is not trivially determined. If such a relationship exists, compensation could be greatly simplified due to the apparent ease of detecting and characterizing the respiratory noise contribution to the phase images. B. Physical Properties of a Mechanical Model While there is much to be gained from the creation of a mechanical model, the model must meet certain physical criteria to be effective. First, to reduce the background noise level, the model should be constructed of a minimal number of different materials a lesser number of materials yields a lesser number of tissue differences and, thus, lowers the intrinsic noise levels. Second, when respiring, the model should remain as still as possible. This may be achieved by using a head that is physically isolated from the body. Finally, the model should accurately reflect the human subject s size, shape, and respiration characteristics so that the results are as close as possible to that of the human. III. MATERIALS AND METHODS A. Physical Construction of Mechanical Model The mechanical model was constructed using a hollow composite manikin as the base. The manikin is the size and shape of an average human male torso, but has no head or arms and terminates just superior to the knee [see Fig. 1(A)]. The ferrous bracket attached to the manikin to permit vertical mounting was removed, permitting the manikin to be utilized in the MR environment. Because the legs of the manikin were open and a solution was to be placed inside the torso region of the manikin to effect both field loading and volumetric changes, the legs were blocked off near the upper thigh region of the manikin, using sheet bulk styrene foam. As a human breathes in the supine position, the abdomen is the region that will exhibit the greatest displacement. Since the bones of the chest and shoulders are restricted, there is great resistance to the expansion of the chest during respiration. Therefore, the least resistance is found by displacing the organs that lie caudal to the lungs; this displacement forces the abdomen and the base of the chest region (or most caudal part) to expand upward. So to most closely simulate human respiration, the abdomen and the base of the chest of the composite manikin were to have the most solution displacement. The method chosen to mimic the physical actions associated with the respiratory cycle of a supine human was to displace the solution upward by inflation of simulated lungs. To achieve appropriate displacement and deformation of the otherwise rigid manikin form, it was necessary that the solution leave the normal confines of the cavity of the manikin. Using a construction similar to that of Smith et al. [18], a standard Fig. 1. (A) Side view of manikin filled with saline solution and ready to be positioned in imager. A water phantom has been placed inside the head coil to serve as the head of the manikin. The tub has been sealed on the abdomen to allow displacement of fluid by the filling of the lungs. The air flows into the lungs via the tube in the manikin s neck. (B) and (C) These two images illustrate the tidal volume of approximately 750 ml. In (B), at full exhalation, the water level sits just above 35 L. In (C), at full inhalation, the water level has risen to approximately 36 L. plastic container (approximately 5 in 7 in), with the bottom removed, was contour fitted to the abdomen of the manikin shell and the enclosed composite material was removed [Fig. 1(A)]. Upward flow of the solution into the attached plastic bin mimicked the displacement of the abdomen during supine respiration [Fig. 1(B) and (C)]. It is through this opening that the solution is poured into the manikin. A respiratory tract was constructed of plastic bags and tubing fixed in place inside the manikin. Two empty 1000-mL (I-V) bags were utilized as lungs, providing a maximum tidal volume consistent with the capacity of human lungs. The bags were attached to the back wall of the chest cavity by plastic clips and hot glue to ensure that the water was displaced rather than the lungs. Plastic fittings were used to attach 4 in of 0.5-in innerdiameter Tygon plastic tubing (Model #R-3603) to each lung. These two tubes met at a Y junction that protruded through the neck of the manikin, permitting a single length of tubing to inflate and deflate the lungs. All tubing was securely clipped into place and leak-down testing was performed. The displacement of the solution was achieved by the use of a respirator to inflate and deflate the bags serving as lungs. A portable large animal respirator (Model #607, Harvard Apparatus Co, Inc., Dover, MA) was utilized to provide a constant breathing rate and volume. The chosen respirator can provide a maximum tidal volume of approximately 700-mL displacement [Fig. 1(B), (C)] and a maximum respiration rate of approximately 55 bpm. This respirator is sufficient to simulate human breathing average resting rate of 12 bpm and average tidal volume of 500 ml [19]. Because the respirator is made of ferrous material it was necessary that it remain outside of the magnet room. Tubing was therefore run from the respirator, through the doorway of the imaging room, to the Y junction at the neck of the manikin, a distance of approximately four meters.

4 BROSCH et al.: SIMULATION OF HUMAN RESPIRATION IN fmri WITH A MECHANICAL MODEL 703 In order to appropriately mimic the loading on the MR field caused by a human body, the body cavity was filled with 35 liters of a 0.45% by volume saline solution. This solution was chosen such that the resistivity of the manikin body ( -cm [20]) was comparable to the resistivity of the human body at 64 MHz ( -cm [20] [23]). Changing the resistivity of the solution within the manikin is expected to change the eddy-current contribution to respiratory artifacts. For example, replacing saline with deionized water ( 10 cm [24]) is expected to greatly reduce eddy-current contributions. B. Experimental Preparation Prior to experimentation, a resting volume of air was established in the two lungs to provide accurate modeling and to protect the respirator. From a modeling perspective, a resting volume is appropriate because humans naturally have a resting volume of air in their lungs, even after complete exhalation. Further, the respirator is protected by the presence of a resting volume. In the case of an air leak, the respirator could be damaged if an inhalation was performed without air remaining in the lungs. Therefore, a resting volume of ml of air was injected into the lungs of the manikin using a syringe. C. Experimental Procedures All data were acquired on a General Electric 1.5-tesla Signa CV imager located at the Froedtert Memorial Lutheran Hospital in Milwaukee, WI. Both magnitude and phase images were saved for subsequent analysis. Axial, coronal or sagittal images were acquired of one, three or five slices (5-mm thickness) through the head (18-cm-diameter spherical water phantom) of the manikin using a blipped echo-planar image sequence. Typical imaging runs on the manikin involved a repetition time (TR) of 250, 350, or 1500 ms, a fixed echo time (TE) of 40 ms and between 128 and 256 images acquired of each slice. The TR used was dependent upon the number of slices acquired (1, 3, or 5). A minimum of 5-mm interslice spacing was utilized to prevent crosstalk between slice acquisitions. Experimental runs were conducted while the respirator cycled at a fixed rate between 0 and 30 bpm. Because respiratory rates may slow and volumes increase, slightly when breathing in the supine position, the focus of the experiments was in the range of 6 14 bpm with a displacement of approximately 700 ml. No runs were conducted in which the respiration rate was undersampled. During 0 bpm experiments, the respirator was either turned off, or was operating at 6 bpm with output redirected from the manikin. To assess the dependence of the field fluctuations on sources other than gross susceptibility changes, one session was conducted in which the manikin was imaged while empty, while filled with deionized water and while filled with saline solution. Runs were conducted with respiration rates of 0, 6, and 14 bpm. If temporal changes in bulk susceptibility are the dominant source of field fluctuations, it was expected that the empty runs would exhibit no peak at the respiratory rate and that the runs conducted with deionized water would produce power spectrum peaks essentially identical to those observed when using the saline solution. It should be noted that the susceptibility of deionized water ( 13) is of lesser magnitude than that of saline ( 30) [25], potentially producing greater field homogeneity and a greater global MR signal level for the deionized water than for the saline solution. Images of seven human subjects (five male, two female) were acquired using similar parameters (TR 360 ms, TE 40 ms, one or five slices, 5-mm thickness, and 5-mm spacing between slices). Human subjects were allowed to breathe normally while otherwise remaining inactive during the duration of the acquisition. D. Data Processing Two preprocessing steps were taken to improve the frequency-domain visualization of respiration-induced signal changes. First, the mean of each voxel was removed from the time-series of magnitude images. The removal of this constant offset was done to permit better visualization of the power spectrum of each voxel. The second preprocessing step performed was smoothing of the first three time points of the magnitude image data in each voxel, such that for and. This prevented the initially steep approach to steady-state magnetization from introducing significant highfrequency artifacts. The comparison of data acquired on humans to data acquired on the phantom was performed using a power spectrum analysis. A power spectrum was computed for each voxel in the acquired magnitude and phase images, using a combination of MATLAB and in-house software at Purdue University. The resulting frequency-domain data were then recombined into a volumetric data set, with the time axis now corresponding to frequency a spectral volume that permits the analysis of the signal energy distribution throughout the volume of the imaged object. The zero-frequency point was set to zero for all voxels, more readily permitting use of the spectral volume to identify peaks (including respiratory and cardiac noise) in the spectrum of a single-voxel. Images from these spectral volumes, indicating the 2-D distribution of energy at a given frequency, are shown in Figs. 5 and 6. IV. RESULTS Magnitude and phase time-courses are displayed in Fig. 2 from a central voxel in the water phantom (the head of the manikin) during an experiment conducted with mechanical respiration set to 6 bpm. Power spectra of axial magnitude and phase images taken from the center of the water phantom (the head of the manikin) are shown for rates of 0, 6, and 14 bpm in Fig. 3. All of the plots in the two figures represent the central pixel of the image i.e.,. No clear peak at the respiratory rate was observed for any magnitude power spectra [Fig. 3(top)], but a strong peak is visible in phase power spectra corresponding to nonzero respiratory rates [Fig. 3(bottom)]. Observe the shift in the position of the phase respiration peak as the rate of mechanically-induced respiration changes from 6 to 14 bpm. No peak was observed for any case of 0 bpm, either

5 704 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 49, NO. 7, JULY 2002 Fig. 2. Magnitude and phase image time-courses from a central voxel of the water phantom used as the head of the manikin. Data were acquired during an experiment in which the respirator was operating the lungs of the manikin at a rate of 6 bpm. These plots illustrate the lack of readily observable respiration energy in the magnitude, in contrast with the clear oscillation present in the phase. The data shown represent 256 images acquired using a TR of 0.25 s. Fig. 4. Magnitude and phase images (left) and voxel spectra (right; location indicated by the white cross on the image) for two slices from a five-slice axial acquisition on a human. (A) Slice 5, the superior-most slice. (B) Slice 3, the middle slice. Observe the strong respiration peaks near 18 bpm in the phase image power spectra (A, B; bottom), but the lack of such peaks in the magnitude image power spectra (A, B; top). Also, observe the presence of an oversampled (TR = 360 ms) cardiac peak near 75 cycles/min in the magnitude spectra. The dc component of each spectrum has been set to zero to ease identification of respiratory and cardiac energy peaks. Fig. 3. Magnitude and phase image power spectra from a central voxel of the water phantom used as the head of the manikin. The rates of mechanical respiration were 0, 6, and 14 bpm. These power spectra demonstrate the strong presence of the respiration noise in the phase data, but the lack of a clear peak in the magnitude data. The 6 bpm power spectra correspond to the time-course data shown in Fig. 2. Note that a typical block paradigm experiment would be expected to introduce a peak at a rate between 1 cycle/min (30 s On/Off) and 3 cycles/min (10 s On/Off), whereas an event-related experiment would be expected to introduce energy throughout the spectrum. when the respirator was not in operation or when the respirator was operating, but not connected to the manikin. The consistent peak at 95 cycles/min indicates an oscillation with a period of 625 ms, corresponding to an artifact with period 2.5 TR that we found to be present in all data acquired on this imager, though it was more obvious in data acquired using the manikin. Power spectra of axial magnitude and phase images taken from the white matter of a human are shown in Fig. 4. Consistent with the results of Noll et al. [3], the respiratory peak near 18 bpm is readily visible in power spectra of the phase images acquired in the human (A, B; bottom), but not in the magnitude images (A, B; top). Note the presence of an oversampled (TR 360 ms) cardiac peak at a rate of approximately 75 cycles/min in the magnitude spectra. The distribution of the respiration rate energy is similar in human and manikin data for phase images, but is not identical for magnitude images. Using the spectral volume, a single-voxel frequency spectrum [Fig. 5(A)] may be used to identify the respiration and cardiac peaks. Use of the corresponding spectral images allows the comparison of magnitude and phase power spectrum images. In Fig. 5(B), these data are presented for the manikin (left: respiration rate at 14 bpm) and a resting human (middle: at the rate of respiration; right: at the rate of the cardiac rhythm). For both the human and the manikin, the respiration data exhibit a similar, relatively uniform distribution of energy. In the magnitude images, the concentration of respiration energy in the manikin is largely peripheral (consistent with the report of [7] and the predictions of [15]), while the energy in the human image is concentrated at the interhemispheric sulcus. In contrast, the human cardiac rhythm energy is concentrated in the central region of the brain for both the phase and magnitude images, with additional energy in the peripheral vasculature of the magnitude power spectra.

6 BROSCH et al.: SIMULATION OF HUMAN RESPIRATION IN fmri WITH A MECHANICAL MODEL 705 Fig. 5. (A) Fourier transform of the time-course of a single voxel in human phase image data, used to identify the respiration (Resp) and cardiac (Card) noise peaks. and (B) Spectral volume images demonstrating the spatial distribution of energy at the respiratory rate (left two columns) and cardiac rate (right column) in the magnitude (top) and phase (bottom) images. The first column of (B) illustrates the energy distribution observed in the water phantom used as the head of the manikin. Note that the energy at the mechanical respiration rate is located at the periphery of the magnitude image, but is distributed throughout the phase image. In the human respiration rate images (B; middle column) the peripheral energy distribution is again observed, with the greatest energy in the inter-hemispheric sulcus. As in the manikin data, the respiration noise is distributed throughout the phase image. The respiratory data may be contrasted with the cardiac rate data (B; right column), in which the energy of the cardiac rhythm is relatively localized in both the magnitude and phase data. When the resistivity of the manikin was increased by replacing the saline solution with deionized water, the energy in the phase images at the mechanical respiration rate increased. When the manikin was filled with deionized water, the energy at the respiratory rate of a central voxel was 30.0% 4.9% of the total nondc spectral energy (four experiments). When filled with saline the energy at the respiratory rate was 25.1% 3.2% of the total nondc spectral energy (three experiments). In contrast, the average magnitude signal observed with the deionized water ( ) was slightly lesser than the signal obtained when the manikin was filled with saline solution ( ). No discernable peaks at the rate of mechanical respiration (6 or 14 bpm) were observed in the four experiments in which the water phantom was imaged while the manikin was empty. V. DISCUSSION A. Validity of the Mechanical Model The mechanical model effectively reproduces the phase image artifacts observed in humans, while generating a subtly different, yet relevant, distribution of magnitude image artifact. This more peripheral localization of the magnitude image noise would place it within the gray matter typically of interest within a slice in fmri analyses, potentially altering the signal-to-noise ratio and associated probability of detection. Fig. 6. Matrix of images illustrating the positional dependence of the respiration noise in the spectral volume of the phase images. The disturbance in the phase data tends to decrease as one moves further from the torso. For example, the noise distribution is relatively uniform within an axial slice, independent of inferior-superior position (top row). However, the noise decreases as one moves in a superior direction, away from the torso. In this case the most superior axial slice (top right) exhibits a lesser disturbance than the middle axial slice (top middle), in turn exhibiting a lesser disturbance than the most inferior slice (top left). This spatial position dependence is further illustrated in both sagittal (middle row) and coronal (bottom row) images. The successful reproduction of the phase image artifacts is demonstrated by two observations. First, the location of the peak in the power spectrum in both the magnitude and phase image series, when present, corresponds to the known rate of the animal respirator [e.g., Fig. 3(B)]. Second, the spatial distribution of the energy at the rate of respiration was equivalent to that observed in the human. In both cases, the respiration energy is greatest in the inferior portion of the brain and is distributed in a relatively uniform fashion within an axial plane (Fig. 6), consistent with the results of Bodurka et al. [13]. The observed magnitude image artifacts are consistent with the predictions of Raj et al. [15], in which subtle shifts in the centroid of the imaged object could lead to correlated signal variations at the edges of the object. This shift effectively a change in the position on which the field-of-view is centered would be expected to produce fluctuations wherever signal intensity gradients exist within the image. The observation only of a rim artifact in the manikin data is consistent with this expectation and with the report of Bates et al. [7] in which signal changes were induced solely by susceptibility fluctuations. We hypothesize that the respiration energy is generally undetected in human magnitude images because it is overwhelmed by other physiologic noise sources (e.g., movement, cardiac rhythm) that are absent from our mechanical model. Greater partial volume averaging at the periphery of the brain than exists at the edge of the water phantom would also reduce the observed fluctuations. This hypothesis is consistent with the observation that the power spectrum of the human has a substantially greater noise floor than that of the mechanical model.

7 706 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 49, NO. 7, JULY 2002 The current movement of clinical and research-oriented MRI facilities toward short-bore imaging systems increases the usefulness of these observations. This data was collected using a GE Signa CV a short-bore (2 m) imager. Such systems are known to have more pronounced field heterogeneity than traditional long-bore (3 m) imagers, such as the GE Signa LX. The increased heterogeneity of the static magnetic field may lead to greater inherent variations in the local field, producing more intensity gradients within the image and accentuating the observed perturbations due to respiration. B. Source of Respiration Disturbance Our results may be used to examine the contribution to respiration noise from three possible sources: head movement, eddy currents in the body and the bulk susceptibility change resulting from movement of the abdomen and abdominal organs. By demonstrating the presence of a respiration artifact while controlling for both head movement and eddy currents in the body, we have found that these are not the dominant generators of respiratory artifact. Our findings demonstrate that the volumetric change alone is sufficient to induce respiratory artifacts in fmri data. Based on the construction of our model, we conclude that rigid-body movement of the head is not the primary contributor to the respiration artifact. Movement of the head would be expected to produce signal fluctuations wherever significant contrast exists within the brain. However, as may be seen by comparing the magnitude image of Fig. 4(B) with the magnitude power spectrum at the rate of respiration in Fig. 5(B), the respiration energy is largely limited to the edges of the hemispheres, rather than being present at the many locations of significant magnitude contrast. In addition, while head motion can be a consequence of respiration, a substantial artifact has been observed in the strict absence of such movement, achieved by isolation of the head (water phantom) from the body of the mechanical model. Further, Raj et al. [15] have demonstrated that some apparent motion is likely a consequence of changes in the susceptibility of the air in the chest, in agreement with the findings of Bates et al. examining gases at the level of the head [7]. The second possible source examined in this paper eddy currents in the body has also been found not to be a dominant generator of respiration artifact. This conclusion is supported by the observation of a significant peak at the respiratory rate even when the resistivity of the manikin was greatly increased through the use of deionized water in place of the saline solution. The total absence of a peak at the rate of respiration when the manikin was empty of fluid demonstrates that the peak is not due to electrical or mechanical artifacts, or even due to the air that flows through the neck tube into the lungs. Eddy currents were, however, observed to alter the properties of the respiration noise. While yielding similar magnitude image signal levels, the energy at the respiration rate was increased by an increase in the resistivity of the fluid filling the manikin. This change should correlate with a decrease in the strength of eddy currents that coincide with the periodicity of the respiratory cycle and a corresponding decrease in the variability of the static field fluctuations, culminating in lesser fluctuation at the respiratory rate. Instead we have observed greater amplitude fluctuations for the case of greater resistivity. To resolve this contradiction, we hypothesize that the direction and amplitude of the eddy currents generated within the body cavity are sufficiently random that they reduce the coherence and amplitude of the disturbance to the static magnetic field, resulting in less total energy at the rate of respiration. C. Conclusion The mechanical model we have built may be used as a practical model of human respiration for the development of postprocessing algorithms or imaging sequences that may be used to limit or remove respiratory artifacts in fmri data. The lack of physiologic noise in the mechanical model provides a less noisy environment in which to characterize respiration-induced noise and to develop algorithms to reduce or remove these respiratory artifacts from fmri data (e.g., [26]). ACKNOWLEDGMENT The authors would like to thank Dr. R. Prost, K. Foster, M. Clark, U. Ziyan, and R. B. Wolfgang for their ideas and assistance in conducting this work. REFERENCES [1] T. B. Parrish, D. R. 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8 BROSCH et al.: SIMULATION OF HUMAN RESPIRATION IN fmri WITH A MECHANICAL MODEL 707 [14] J. R. Brosch, J. L. Ulmer, and T. M. Talavage, Comparison of respiratory artifacts between human subjects and a mechanical model, NeuroImage, vol. 11, no. 5, p. S564, [15] D. Raj, D. Paley, A. W. Anderson, R. P. Kennan, and J. C. Gore, Respiratory effects in functional magnetic resonance imaging due to buk susceptibility changes, in Proc. Int. Soc. Magnetic Resonance in Medicine, 2001, p [16] B. P. Poncelet, V. J. Wedeen, R. M. Weisskoff, and M. S. Cohen, Brain parenchyma motion: Measurement cine echo-planar MR imaging, Radiology, vol. 185, pp , Dec [17] J. Mayhew, Y. Zheng, Y. Hou, J. Berwick, S. Askew, and P. Coffey, Low frequency haemodynamic and metabolic 0.1 Hz oscillations in brain tissue, in Proc. Int. Soc. Magnetic Resonance in Medicine, 1999, p [18] C. D. Smith, A. V. Kildishev, J. A. Nyenhuis, K. S. Foster, and J. D. Bourland, Interactions of magnetic resonance imaging radio frequency magnetic fields with elongated medical implants, J. Appl. Phys., vol. 87, pp , May [19] G. J. Tortora and S. R. Grabowski, Principles of Anatomy and Physiology, 8 ed. New York: Harper Collins, [20] L. A. Geddes and L. E. Baker, The specific resistance of biological material a compendium of data for the biomedical engineer and physiologist, Med. Biol. Eng., vol. 5, pp , May [21] M. A. Stuchly, T. W. Athey, S. S. Stuchly, G. M. Samaras, and G. Taylor, Dielectric properties of animal tissues in vivo at frequencies 10 mhz 1 ghz, Bioelectromagnetics, vol. 2, pp , [22] M.-A Golombeck, O. Dössel, A. Staubert, and V. M. Tronnier, Magnetic resonance imaging with implanted neurostimulators: A first numerical approach using finite integration theory, in Proc. Int. Symp. Electromagnetic Compatibility, Magdeburg, Germany, Oct. 5 7, 1999, pp [23] T. J. Faes, H. A. van der Meij, J. C. de Munck, and R. M. Heethaar, The electric resistivity of human tissues (100 Hz 10 MHz): A meta-analysis of review studies, Physiol. Meas., vol. 20, pp. R1 10, [24] W. H. Hayt, Jr., Engineering Electromagnetics, 5 ed. New York: Mc- Graw-Hill, [25] CRC Handbook of Chemistry and Physics, 66 ed., CRC, Boca Raton, FL, [26] U. Ziyan, J. R. Brosch, and T. M. Talavage, Amplitude-adaptive filtering of respiration noise in fmri data, in Proc. Int. Soc.Magnetic Resonance in Medicine, 2001, p Jared R. Brosch (M 98) received the B.S. degree in electrical engineering in 1999 and the M.S. degree in biomedical engineering in 2000 from Purdue University, West Lafayette, IN. He joined Etalon a Piezo Technologies Co., Lebanon, IN, as an Engineer in Thomas M. Talavage (M 89) received the B.S. and M.S. degrees in electrical engineering from Purdue University, West Lafayette, IN, in 1992 and 1993 and the Ph.D. degree in speech and hearing sciences from the Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, in He joined the Purdue University School of Electrical and Computer Engineering and Department of Biomedical Engineering, as an Assistant Professor in John L. Ulmer received the B.S. degree in biology and the M.D. degree from the University of Kentucky, Lexington, in He joined the Department of Radiology at the Medical College of Wisconsin, Milwaukee, in John A. Nyenhuis (SM 83) received the B.S. degree in physics from Indiana University, Bloomington, in He received the M.S. degree in materials engineering and the Ph.D. degree in electrical engineering from Purdue University, West Lafayette, IN, in 1978 and 1983, respectively. He is currently a Professor of Electrical and Computer Engineering at Purdue University. His principal research interests are in bioelectromagnetics and magnetic measurements.