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We present an analytical method for the estimation of rigid-body motion in sets of three-dimensional (3-D) SPECT and PET slices. This method utilizes mathematically defined generalized center-of-mass points in images, requiring no segmentation. It can be applied to compensation of the rigid-body motion in both SPECT and PET, once a series of 3-D tomographic images are available. We generalized the formula for the center-of-mass to obtain a family of points comoving with the object's rigid-body motion. From the family of possible points we chose the best three points which resulted in the minimum root-mean-square difference between images as the generalized center-of-mass points for use in estimating motion. The estimated motion was used to sum the sets of tomographic images, or incorporated in the iterative reconstruction to correct for motion during reconstruction of the combined projection data. For comparison, the principle-axes method was also applied to estimate the rigid-body motion from the same tomographic images. To evaluate our method for different noise levels, we performed simulations with the MCAT phantom. We observed that though noise degraded the motion-detection accuracy, our method helped in reducing the motion artifact both visually and quantitatively. We also acquired four sets of the emission and transmission data of the Data Spectrum Anthropomorphic Phantom positioned at four different locations and/or orientations. From these we generated a composite acquisition simulating periodic phantom movements during acquisition. The simulated motion was calculated from the generalized center-of-mass points calculated from the tomographic images reconstructed from individual acquisitions. We determined that motion-compensation greatly reduced the motion artifact. Finally, in a simulation with the gated MCAT phantom, an exaggerated rigid-body motion was applied to the end-systolic frame. The motion was estimated from the end-diastolic and end-systolic images, and used to sum them into a summed image without obvious artifact. Compared to the principle-axes method, in two of the three comparisons with anthropomorphic phantom data our method estimated the motion in closer agreement to the Polaris system than the principal-axes method, while the principle-axes method gave a more accurate estimation of motion in most cases for the MCAT simulations. As an image-driven approach, our method assumes angularly complete data sets for each state of motion. We expect this method to be applied in correction of respiratory motion in respiratory gated SPECT, and respiratory or other rigid-body motion in PET. Index Terms--Center-of-mass, emission tomography, motion compensation.

The objective of this investigation was to determine the effectiveness of three motion reducing strategies in diminishing the degrading impact of respiratory motion on the detection of small solitary pulmonary nodules (SPN) in single photon emission computed tomographic (SPECT) imaging in comparison to a standard clinical acquisition and the ideal case of imaging in the absence of respiratory motion. To do this non-uniform rational B-spline cardiac-torso (NCAT) phantoms based on human-volunteer CT studies were generated spanning the respiratory cycle for a normal background distribution of Tc-99m NeoTect. Similarly, spherical phantoms of 1.0 cm diameter were generated to model small SPN for each of 150 uniquely located sites within the lungs whose respiratory motion was based on the motion of normal structures in the volunteer CT studies. The SIMIND Monte Carlo program was used to produce SPECT projection data from these. Normal and single-lesion containing SPECT projection sets with a clinically realistic Poisson noise level were created for the cases of: 1) the end-expiration (EE) frame with all counts, 2) respiration-averaged motion with all counts, 3) one-fourth of the 32 frames centered around EE (Quarter-Binning), 4) one-half of the 32 frames centered around EE (Half-Binning), and 5) eight temporally binned frames spanning the respiratory cycle. Each of the sets of combined projection data were reconstructed with RBI-EM with system spatial-resolution compensation (RC). Based on the known motion for each of the 150 different lesions, the reconstructed volumes of respiratory bins were shifted so as to superimpose the locations of the SPN onto that in the first bin (Reconstruct and Shift). Five human-observers performed localization receiver operating characteristics (LROC) studies of SPN detection. The observer results were analyzed for statistical significance differences in SPN detection accuracy among the three correction strategies, the standard acquisition, and the ideal case of the absence of respiratory motion. Our human-observer LROC determined that Quarter-Binning and Half-Binning strategies resulted in SPN detection accuracy statistically significantly below (P < 0.05) that of standard clinical acquisition, whereas the Reconstruct and Shift strategy resulted in a detection accuracy not statistically significantly different from that of the ideal case. This investigation demonstrates that tumor detection based on acquisitions associated with less than all the counts which could potentially be employed may result in poorer detection despite limiting the motion of the lesion. The Reconstruct and Shift method results in tumor detection that is equivalent to ideal motion correction.

For the 2011 FDA approved Parkinson's Disease (PD) SPECT imaging agent I-123 labeled DaTscan, the volume of interest (VOI) is the interior portion of the brain. However imaging of the occipital lobe is also required with PD for calculation of the striatal binding ratio (SBR), a parameter of significance in early diagnosis, differentiation of PD from other disorders with similar clinical presentations, and monitoring progression. Thus we propose the usage of a combination of a multi-pinhole (MPH) collimator on one head of the SPECT system and a fan-beam on the other. The MPH would be designed to provide high resolution and sensitivity for imaging of the interior portion of the brain. The fan-beam collimator would provide lower resolution but complete sampling of the brain addressing data sufficiency and allowing a volume-of-interest to be defined over the occipital lobe for calculation of SBR's. Herein we focus on the design of the MPH component of the combined system. Combined reconstruction will be addressed in a subsequent publication. An analysis of 46 clinical DaTscan studies was performed to provide information to define the VOI, and design of a MPH collimator to image this VOI. The system spatial resolution for the MPH was set to 4.7 mm, which is comparable to that of clinical PET systems, and significantly smaller than that of fan-beam collimators employed in SPECT. With this set, we compared system sensitivities for three aperture array designs, and selected the 3 × 3 array due to it being the highest of the three. The combined sensitivity of the apertures for it was similar to that of an ultra-high resolution fan-beam (LEUHRF) collimator, but smaller than that of a high-resolution fan-beam collimator (LEHRF). On the basis of these results we propose the further exploration of this design through simulations, and the development of combined MPH and fan-beam reconstruction.

Purpose: We investigate the differences without/with respiratory motion correction in apparent imaging agent localization induced in reconstructed emission images when the attenuation maps used for attenuation correction (from CT) are misaligned with the patient anatomy during emission imaging due to differences in respiratory state., Methods: We investigated use of attenuation maps acquired at different states of a 2 cm amplitude respiratory cycle (at end-expiration, at end-inspiration, the center map, the average transmission map, and a large breath-hold beyond range of respiration during emission imaging) to correct for attenuation in MLEM reconstruction for several anatomical variants of the NCAT phantom which included both with and without non-rigid motion between heart and sub-diaphragmatic regions (such as liver, kidneys etc). We tested these cases with and without emission motion correction and attenuation map alignment/non-alignment., Results: For the NCAT default male anatomy the false count-reduction due to breathing was largely removed upon emission motion correction for the large majority of the cases. Exceptions (for the default male) were for the cases when using the large-breathhold end-inspiration map (TI&#95;EXT), when we used the end-expiration (TE) map, and to a smaller extent, the end-inspiration map (TI). However moving the attenuation maps rigidly to align the heart region, reduced the remaining count-reduction artifacts. For the female patient count-reduction remained post motion correction using rigid map-alignment due to the breast soft-tissue misalignment. Quantitatively, after the transmission (rigid) alignment correction, the polar-map 17-segment RMS error with respect to the reference (motion-less case) reduced by 46.5% on average for the extreme breathhold case. The reductions were 40.8% for end-expiration map and 31.9% for end-inspiration cases on the average, comparable to the semi-ideal case where each state uses its own attenuation map for correction., Conclusions: Two main conclusions are that even rigid emission motion correction to rigidly align the heart region to the attenuation map helps in average cases to reduce the count-reduction artifacts and secondly, within the limits of the study (ex. rigid correction) when there is lung tissue inferior to the heart as with the NCAT phantom employed in this study endexpiration maps (TE) might best be avoided as they may create more artifacts than the end-inspiration (TI) maps.

Respiratory motion of the heart impacts the diagnostic accuracy of myocardial-perfusion emission-imaging studies. Amplitude binning has come to be the method of choice for binning list-mode based acquisitions for correction of respiratory motion in PET and SPECT. In some subjects respiratory motion exhibits hysteretic behavior similar to damped non-linear cyclic systems. The detection and correction of hysteresis between the signals from surface movement of the patient's body used in binning and the motion of the heart within the chest remains an open area for investigation. This study reports our investigation in nine volunteers of the combined MRI tracking of the internal respiratory motion of the heart using Navigators with stereo-tracking of markers on the volunteer's chest and abdomen by a visual-tracking system (VTS). The respiratory motion signals from the internal organs and the external markers were evaluated for hysteretic behavior analyzing the temporal correspondence of the signals. In general, a strong, positive correlation between the external marker motion (AP direction) and the internal heart motion (SI direction) during respiration was observed. The average ± standard deviation in the Spearman's ranked correlation coefficient ( ρ ) over the nine volunteer studied was 0.92 ± 0.1 between the external abdomen marker and the internal heart, and 0.87 ± 0.2 between the external chest marker and the internal heart. However despite the good correlation on average for the nine volunteers, in three studies a poor correlation was observed due to hysteretic behavior between inspiration and expiration for either the chest marker and the internal motion of the heart, or the abdominal marker and the motion of the heart. In all cases we observed a good correlation of at least either the abdomen or the chest with the heart. Based on this result, we propose the use of marker motion from both the chest and abdomen regions when estimating the internal heart motion to detect and address hysteresis when binning list-mode emission data.

SPECT is primarily used in the clinic for cardiac myocardial perfusion imaging. However, for SPECT, sensitivity is impaired due to the need for collimation. System resolution FWHM is poor as well (~ 1 cm). In this work the resolution of a curved detector was theoretically derived. The advantage of a curved detector over a flat detector with pinhole collimation was demonstrated for cardiac applications using theoretical derivations as well as a ray-tracing voxel-based forward projector. For the flat detector using parameters close to what was expected for the new multi-pinhole GE Discovery system, it is shown that using a paraboloid detector one may obtain a better system resolution (about 29% better on the average), keeping same pinhole opening. Alternately, sensitivity gains of as much as 2.25 may be obtained, for similar resolutions as a flat detector by just using a different pinhole with higher hole-diameter.

The goal of this work is to investigate, for a large set of patients, the motion of the heart with respiration during free-breathing supine medical imaging. For this purpose we analyzed the motion of the heart in 32 non-contrast enhanced respiratory-gated 4D-CT datasets acquired during quiet unconstrained breathing. The respiratory-gated CT images covered the cardiac region and were acquired at each of 10 stages of the respiratory cycle, with the first stage being end-inspiration. We devised a 3-D semi-automated segmentation algorithm that segments the heart in the 4D-CT datasets acquired without contrast enhancement for use in estimating respiratory motion of the heart. Our semi-automated segmentation results were compared against interactive hand segmentations of the coronal slices by a cardiologist and a radiologist. The pairwise difference in segmentation among the algorithm and the physicians was on the average 11% and 10% of the total average segmented volume across the patient, with a couple of patients as outliers above the 95% agreement limit. The mean difference among the two physicians was 8% with an outlier above the 95% agreement limit. The 3-D segmentation was an order of magnitude faster than the Physicians' manual segmentation and represents significant reduction of Physicians' time. The segmented first stages of respiration were used in 12 degree-of-freedom (DOF) affine registration to estimate the motion at each subsequent stage of respiration. The registration results from the 32 patients indicate that the translation in the superior-inferior direction was the largest component motion, with a maximum of 10.7 mm, mean of 6.4 mm, and standard deviation of 2.2 mm. Translation in the anterior-posterior direction was the next largest component of motion, with a maximum of 4.0 mm, mean of 1.7 mm, and standard deviation of 1.0 mm. Rotation about the right-left axis was on average the largest component of rotation observed, with a maximum of 4.6 degrees, mean of 1.6 degrees, and standard deviation of 2.1 degrees. The other rotation and shear parameters were all close to zero on average indicting the motion could be reasonably well approximated by rigid-body motion. However, the product of the three scale factors averaged about 0.97 indicating the possibility of a small decrease in heart volume with expiration. The motion results were similar whether we used the Physician's segmentation or the 3-D algorithm.

Patient body-motion and respiratory-motion impacts the image quality of cardiac SPECT and PET perfusion images. Several algorithms exist in the literature to correct for motion within the iterative maximum-likelihood reconstruction framework. In this work, three algorithms are derived starting with Poisson statistics to correct for patient motion. The first one is a motion compensated MLEM algorithm (MC-MLEM). The next two algorithms called MGEM-1 and MGEM-2 (short for Motion Gated OSEM, 1 and 2) use the motion states as subsets, in two different ways. Experiments were performed with NCAT phantoms (with exactly known motion) as the source and attenuation distributions. Experiments were also performed on an anthropomorphic phantom and a patient study. The SIMIND Monte Carlo simulation software was used to create SPECT projection images of the NCAT phantoms. The projection images were then modified to have Poisson noise levels equivalent to that of clinical acquisition. We investigated application of these algorithms to correction of (1) a large body-motion of 2 cm in Superior-Inferior (SI) and Anterior-Posterior (AP) directions each and (2) respiratory motion of 2 cm in SI and 0.6 cm in AP. We determined the bias with respect to the NCAT phantom activity for noiseless reconstructions as well as the bias-variance for noisy reconstructions. The MGEM-1 advanced along the bias-variance curve faster than the MC-MLEM with iterations. The MGEM-1 also lowered the noiseless bias (with respect to NCAT truth) faster with iterations, compared to the MC-MLEM algorithms, as expected with subset algorithms. For the body motion correction with two motion states, after the 9th iteration the bias was close to that of MC-MLEM at iteration 17, reducing the number of iterations by a factor of 1.89. For the respiratory motion correction with 9 motion states, based on the noiseless bias, the iteration reduction factor was approximately 7. For the MGEM-2, however, bias-plot or the bias-variance-plot saturated with iteration because of successive interpolation error. SPECT data was acquired simulating respiratory motion of 2 cm amplitude with an anthropomorphic phantom. A patient study acquired with body motion in a second rest was also acquired. The motion correction was applied to these acquisitions with the anthropomorphic phantom and the patient study, showing marked improvements of image quality with the estimated motion correction.

Motion of patients undergoing cardiac SPECT perfusion imaging causes artifacts in the acquired images which may lead to difficulty in interpretation. Our work investigates a technique of obtaining patient motion estimates from retro-reflective markers on stretchy bands wrapped around the chest and abdomen of patients being imaged clinically. Motion signals obtained from the markers consist of at least two components, body motion (BM) and periodic motion (PM) due to respiration. We present a method for separating these components from the motion-tracking data of each marker, and then report a method for combining the BM estimated from chest markers to estimate the 6-degree-of-freedom (6-DOF) rigid-body motion (RBM) of the heart. Motion studies of volunteers and patients are used to evaluate the methods. Illustrative examples of the motion of the heart due to patient body movement and respiration (upward creep) are presented and compared to estimates of the motion of the heart obtained directly from SPECT data. Our motion-tracking method is seen to give reasonable agreement with the motion-estimates from the SPECT data while being considerably less noisy.