20 II IEEE Nuclear Science Symposium Conference Record MICIO-3

Combined Respiratory and Rigid Body Motion

Compensation in Cardiac Perfusion SPECT using a Visual Tracking System

P. Hendrik Pretorius, Associate Member, IEEE, Michael A. King, Senior Member, IEEE, Karen L. Johnson, Joyeeta M. Mukherjee, Member, IEEE, Joyoni Dey, Member, IEEE, and Arda Konik,

Member, IEEE.

Abstract-- We report on the validation of our combined

respiratory and rigid body motion compensation strategy through

acquisitions of the Data Spectrum anthropomorphic phantom, and

investigation of clinical efficacy and robustness in 25 cardiac

perfusion patient studies, employing a visual tracking system (VTS).

The heart and liver was filled with a 2:1 concentration of Tc-99m

and two sets of SPECT data were acquired. Each set of SPECT

data consisted of a rest-perfusion baseline frame-mode emission

acquisition, a Beacon (Philips, Cleveland, OH) transmission

acquisition, and a list-mode emission acquisition. Respiratory

motion was simulated during the list-mode acquisitions using the

Quasar (Modus Medical Devices Inc. ON, Canada). Rigid-body

motion was introduced in one of the two list-mode acquisitions by

rotating the phantom around the x-axis and translating the

phantom in x, y, and z. Patient volunteers with written consent were

similarly acquired and asked to execute some predefined body

motion during the list-mode acquisition. Motion tracking was

performed using 6 near infrared Vicon cameras in combination with

7 retro-reflective markers, 5 placed on the chests of both patient

volunteers and phantom, 2 placed on the abdomen of patient

volunteers, and 2 placed on the vertical motion stage of the Quasar

to simulate abdominal phantom motion. Processing steps included,

down sampling VTS positional data to 10 Hz (100 ms) and

synchronized with 100 ms SPECT frames, separating rigid body

and respiratory motion and estimating 6 DOF rigid body motion,

amplitude bin 100 ms frames into respiratory projection sets,

reconstruct with rigid body motion compensation respiratory

projection sets, estimated respiratory motion employing intensity

based estimation, combine rigid body and respiratory motion, and

reconstruct with combined compensation. We showed for both

phantom and patient acquisitions that combined respiratory and

rigid body motion compensation improve the visual appearance of

slices.

I. INTRODUCTION

That respiratory motion can degrade cardiac perfusion

acquisitions has been known since the early days of single photon emission computed tomography (SPECT) [I). We have shown in a digital phantom study [2] that respiratory motion degrades cardiac perfusion SPECT images and others have

Manuscript received November 7, 20 II. This work was supported by the National Institute of Biomedical Imaging and Bioengineering grant ROI­EBOOI457. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. P.

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National Institute of Biomedical Imaging and Bioengineering grant ROl_lk are from

EBOOI457. The contents are solely the responsibi li ty of the authors and do not' USA.

confirmed our findings [3]. Since then we have implemented various rigid-body and respiratory motion compensation strategies [4-8], either separately [6-8] or in some combination [4,5]. Recently we completed integrating these two corrections to run with minimal initial user interaction employing a visual tracking system (VTS) from Vicon Motion Systems, Inc. (Lake Forest, CA) for motion-tracking. In this study we report on validation of our acquisition and processing implementation through acquisitions of the Data Spectrum anthropomorphic phantom with the Iowa heart insert, and a clinical investigation

of efficacy and robustness in 25 cardiac perfusion patient studies.

II. METHODS AND MATERIALS

A. Acquisitions of the Data Spectrum Anthropomorphic

Phantom

The heart and liver of the phantom was filled with a 2: I concentration of Tc-99m and two sets of SPECT data were acquired. Each set of SPECT data consisted of a baseline frame­

mode emission acquisition, a Beacon (Philips, Cleveland, OH) transmission acquisition, and a list-mode emission acquisition. Respiratory motion was simulated during the list-mode acquisitions using the Quasar device (Modus Medical Devices Inc. ON, Canada). This was accomplished by placing the anthropomorphic phantom on the horizontal stage of the Quasar with 5 retro-reflective makers on the phantom's chest to monitor both the respiratory motion in the z-axis and rigid-body motion. Two additional retro-reflective markers were placed on the vertical stage of the Quasar to simulate the vertical component of a patient's respiratory signal measured with retro-reflective markers placed on the abdomen. A 1.6 cm respiratory motion in

the z-axis was simulated. Rigid-body motion was introduced

during the second of the two list-mode acquisitions by rotating the phantom around the x-axis and translating the anthropomorphic phantom in x, y, and z. The same clinical acquisition protocol as described in section B was used.

B. Patient Acquisitions

Patient volunteers with written consent (n=25) were asked to execute some predefined body motion during a list-mode second perfusion acquisition, which followed their regular rest perfusion portion of a stress-rest perfusion exam. The regular rest perfusion studies were used as a baseline for rigid-body motion compensation. The majority of these patients underwent a one­day stress-rest TI-201 exam (n=24), while one underwent a one­day stress-rest Tc-99m MIBI exam. The acquisition was performed using the standard clinical protocol with 68 projections acquired through 204 degrees employing two heads

necessarily represent the official views of the National Institutes of Health. P. Hendrik Pretorius (email: Hcndrik.Prctorius@umassmcd.cdu), Michael A. King, Karen L. Johnson, Joyeeta M Mukherjee, Joyoni Dey, and Arda Konik are from the University of Massachusetts Medical School Worcester, MA 01655 USA.

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of the IRIX scintillation camera, which rotated with 3-degree steps during emission acquisition. The pixel size was 0.4669 cm in a 128x128 acquisition matrix. Transmission acquisitions were acquired through 264 degrees in 6-degree steps. The patients were monitored by the visual tracking system throughout the whole study in two stages. The first stage comprised of the first frame-mode rest and Beacon transmission, and the second during the list-mode second rest. As for the anthropomorphic phantom, 7 retro-reflective markers were placed on the patients, 5 on the chest and 2 on the abdomen.

C. Visual Tracking System Acquisitions

Motion tracking was accomplished by employing 6 near infrared Vicon cameras in combination with the 7 retro-reflective

markers (described previously) positioned on the anthropomorphic phantom as well as patient volunteers. To summarize, 5 retro-reflective markers were placed on the 'chest' of the anthropomorphic phantom and two on the vertical motion stage of the Quasar, also called the chest wall platform. The latter two retro-reflective markers were tracked by the VTS to simulate the vertical motion component of abdominal respiratory motion of a patient during the acquisition. For patient volunteers, 5 retro­reflective markers were also placed on the chest, 2 right, 3 left, while 2 retro-reflective markers were placed on the abdomen to track abdominal respiration as depicted by fig 1.

Fig I: The placement of the retro-reflective markers on patient volunteers.

Although motion tracking was performed during all phases of the anthropomorphic phantom study, rigid body motion and/or respiratory motion were only introduced during the list-mode portion of both acquisition sets. During the list-mode acquisitions a repeating square-wave with a period of lOs generated by the VTS system, was inserted into the list to accurately synchronize the VTS and SPECT acquisitions. The analog pulse was sampled every 10 ms. This synchronization scheme allowed us to only estimate and compensate respiratory motion for the second rest study.

D. Rigid Body and Respiratory Motion Estimation

Processing was fully automatic except for the placement of a predefined ellipsoidal region of interest (ROI) around the heart using an initial reconstruction. Processing steps were 1) reformatting list-mode data into 68 projections as well as 100 ms frames, 2) down sampling 30 Hz VTS positional measurements to 10 Hz (100 ms) and synchronization with 100 ms SPECT

frames, 3) separating rigid-body and respiratory motion and estimating 6 degrees-of freedom (DOF) rigid-body motion as

described in [9],4) amplitude binning the 100 ms frames into an odd number (N) of respiratory projection data sets, 5) scaling and reconstruction with rigid-body motion compensation N-l projection sets, with the center or reference bin excluded [7], 6) scaling and reconstructing with rigid-body motion compensation N-l unique reference projection sets such that each reference

projection set has the same number of non-zero projections as the individual projection sets in previous step [7], 7) estimating respiratory motion employing the ellipsoidal ROI to isolate the heart on the reconstructed reference bin in combination with an intensity based estimation method, and 8) combing rigid body and respiratory motion estimates in a final single reconstruction employing all the acquired data.

£. Reconstructions

An ordered subsets expectation maXImIzation (OS EM)

algorithm incorporating a 3-dimensional Gaussian rotator [4] was

used for all reconstructions. During respiratory estimation

reconstructions, 17 projections per subset were employed, and 4

projections per subset for the final combined rigid body and

respiratory motion compensation reconstructions. The reason for

the large number of projections per subset during respiratory

motion estimation reconstruction was to minimize reconstruction

artifacts when incomplete data were present (amplitude bins with

little or no counts at some projection angles were discarded), but

still obtain some measure of speed up compared to our first

efforts in [7] where maximum likelihood expectation

maximization (MLEM) were used. Only 3 iterations of OSEM

were employed (�12 MLEM equivalent iterations). The final

combined compensation for rigid-body and respiratory motion

were implemented by reconstructing each projection as N sub­

projections (equal to the number respiratory amplitude bins)

sequentially before stepping to the next projection in the subset.

Each of the sub-projections was weighted with its fractional

contribution to the full projection. Rigid-body motion

compensation reconstructions (without respiratory motion

compensation) were also performed as reference comparison.

F. Image Analysis and Evaluation

These final reconstructions were filtered, and reoriented to

short axis, horizontal long axis, and vertical long axis slices.

Visual inspection and polar map quantification were used for

evaluation. Baseline studies of the anthropomorphic phantom

were used as reference, while the first rest acquisition of patient

volunteers was only useful to judge rigid body motion

compensation (respiratory motion estimation and compensation

were not possible).

III. RESULTS AND DISCUSSION

From fig 2, 4, and 6 it is evident that combined rigid body and respiratory motion compensation results in the best visual quality. When respiratory amplitude was large, the combined rigid body and respiratory motion compensation is even superior to the first rest baseline where no respiratory motion compensation was done. Figures 3, 5, and 7 depict the polar map renditions of the figures 2, 4, and 6 respectively. The effects of simulated respiratory motion are visible in the inferior and anterior portions of the polar map when only rigid body motion is compensated (second to last from right, fig 3) similar to what we shown for the MCA T phantom in reference 2. These effects

are also present in patient studies when respiratory motion is

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pronounced (fig 5, left and second to last from right) while it is not obvious when less respiratory motion is observed (fig 7). As expected, the respiratory motion estimates vary greatly between patients, with a minimum of �0.4 cm and a maximum of 1.9 cm. In some patients (n=4) , respiratory motion estimation and compensation degraded the visual quality. All these patients were either low count (poor statistics) studies or studies where the

VTS retrospectively filled in gaps present in the abdominal retro­reflective marker data. It appears as if the algorithm used by the VTS manufacturer is not able to reliably fill the gaps when repetitive motion of short duration (such as respiratory motion)

occur when the markers are not visualized.

Fig 2: Short-axis slices of the Data Spectrum anthropomorphic phantom with 1.6 cm respiratory motion and a maximum x, y, z displacement of 1.0 cm, 1.2 cm, and -0.7 cm respectively. Also present was -8 .4 and -2.2 degrees of rotation about the x and y axes respectively. From top to bottom we have the baseline study without any motion, slices for rigid-body and respiratory motion present but not corrected, rigid-body motion compensation included slices, and finally rigid-body and respiratory motion compensation included slices. The enlarged short-axis slices taken from the last two rows (white box) show the apparent elongation of the heart in the superior / inferior direction without respiratory motion correction and change with correction

Fig 3: Polar maps of the short-axis slices displayed in fig. 2. From left to right we have the baseline without any motion, rigid body and respiratory motion present but not corrected, rigid body motion compensation included, and finally rigid body and respiratory motion compensation included. Note the cooling in the inferior and anterior portions of the polar map only corrected for rigid body motion (third from the left) compared to the baseline and when rigid body motion was corrected in unison with respiratory motion.

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Fig 4. Short axis slices of a male patient volunteer with an estimated 1.9 cm respiratory motion and a maximum x, y, z displacement of -1.4 cm, 1.4 cm, and 3.5 cm respectively. Also present was -7.2 and -5 .1 degrees of rotation about the x and y axes respectively. From top to bottom we have the baseline without rigid body motion, rigid body and respiratory motion present but not corrected, rigid body motion compensation included, and finally rigid body and respiratory motion compensation included. Notice the improved blood pool and wall visualization in bottom row with respiratory motion correction compared to the row just above as well as the top row. The enlarged portion from the drawn white box shows the elongation and improvement more clearly.

Fig 5: Polar maps of the short axis slices displayed in fig. 5. From left to right we have the baseline without any rigid body motion (respiratory motion present), rigid body and respiratory motion present but not corrected, rigid body motion compensation included, and finally rigid body and respiratory motion compensation included. Note the differences in the inferior and anterior portions of the baseline (left most) and rigid body motion corrected (third from the left) polar maps compared to when rigid body motion was corrected in unison with respiratory motion. Also, the cooling at the apex is more prominent when combined correction was done.

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Fig 6. Short axis slices of a male patient volunteer with an estimated 0.8 cm respiratory motion and a maximum z displacement of 3.0 cm. The first 3 rows depict the short, horizontal long and vertical long axis slices of the heart with only rigid body motion compensation, while the next 3 rows depict the same slices for rigid body motion compensation in combination with respiratory motion compensation. The improvement in this case is more subtle and only appreciated when looking at the enlarged portion (from the drawn boxes) at the bottom.

Fig 7: Polar maps of the short axis slices displayed in fig. 6. From \eft to right we have the baseline without any rigid body motion (respiratory motion present), rigid body and respiratory motion present but not corrected, rigid body motion compensation included, and finally rigid body and respiratory motion compensation included. Note the differences in the inferior and anterior portions of the baseline (left most) and rigid body motion corrected (third from the left) polar maps compared to when rigid body motion was corrected in unison with respiratory motion. Also, the cooling at the apex is more prominent when combined correction was done.

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