Patient motion is an ever-present potential cause of artifacts that can limit the accuracy of diagnostic imaging. The problem is especially significant for imaging modalities such as SPECT and PET, which require the patient to remain motionless for protracted periods of time, and multimodality imaging where patient alignment between the modalities is essential. Our hypothesis is that integrating into iterative reconstruction combined information from a visual-tracking-system (VTS) and the imaging modalities themselves will result in a robust motion-correction strategy. By VTS we mean a computational system that processes stereo-images of retro-reflective markers on the patient surface to provide a source of motion information that is independent of the clinical imaging system(s). The types of patient motion for which compensation will be investigated for cardiac perfusion SPECT imaging with this combined approach are rigid-body motion (RBM), non-rigid-body motion (non-RBM), respiratory motion (RM), cardiac upward creep (which is caused by changes in RM), and motion between sequential multi-modality imaging studies. Note we correct body-motion and RM differently, thus we differentiate between them as part of motion estimation. In this competitive renewal we propose to extend our current VTS to include application with multimodality imaging, emission data-consistency, and finite-element-modeling (FEM) based correction of non-RBM. MRI of volunteers will be employed to establish the relationship between external-marker motion and the motion of the heart within the body. MRI will also provide high-resolution anatomy of the chest at different motion states in combination with measured-marker motion to guide the creation of realistic SPECT simulations for refinement of algorithms. Initial clinical testing will make use of repeat-rest cardiac-SPECT studies in patient-volunteers where the first rest imaging study serves as the standard for judging the success of correction of the second study during which the patients intentionally move. The ultimate assessment of the success of our hypothesis will be physician-observer ROC studies comparing the detection accuracy of coronary artery disease (CAD) with and without motion compensation for patients undergoing SPECT perfusion imaging with the results of cardiac catheterization serving as the "gold" standard. We believe the significance of our proposed investigations lies not only in the potential to improve diagnostic accuracy through motion-correction of both cardiac and non-cardiac SPECT, PET, and multimodality imaging but also in affording a better understanding of patient motion during imaging.

Public Health Relevance

Patients commonly move during the protracted imaging period required by SPECT and PET to collect a statistically sufficient number of events to form images for making an accurate diagnosis. Such motion can cause artifactual changes in the slices which may mislead the physician. In multi-modality imaging patients also may move between emission imaging and imaging used to form attenuation maps and provide an anatomical framework for the functional changes in emission imaging. This can cause a misregistration between the two sets of images which can significantly impact clinical interpretation. Also all images acquired while patients are breathing suffer from respiratory motion blurring of the structures being imaged. The goal of this project is to develop an accurate and robust motion-correction methodology for all forms of patient motion which minimally alters current acquisition protocols so that it can be employed in a busy clinic. The test-bed we have chosen for development of this methodology is cardiac perfusion SPECT imaging which is not only the single most frequent SPECT imaging procedure performed today, but also plays a critical role in providing perfusion and functional information needed for risk assessment and stratification regarding coronary artery disease. Once the methodology is perfected for cardiac SPECT it can be adapted for use with other SPECT and PET imaging procedures. The proposed research will also provide a better understanding of patient motion during medical imaging and treatment.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Biomedical Imaging Technology Study Section (BMIT)
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Sastre, Antonio
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University of Massachusetts Medical School Worcester
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Dasari, Paul; Johnson, Karen; Dey, Joyoni et al. (2014) MRI Investigation of the Linkage Between Respiratory Motion of the Heart and Markers on Patient's Abdomen and Chest: Implications for Respiratory Amplitude Binning List-Mode PET and SPECT Studies. IEEE Trans Nucl Sci 61:192-201
Dasari, Paul K R; Shazeeb, Mohammed Salman; Könik, Arda et al. (2014) Adaptation of the modified Bouc-Wen model to compensate for hysteresis in respiratory motion for the list-mode binning of cardiac SPECT and PET acquisitions: testing using MRI. Med Phys 41:112508
Könik, Arda; Connolly, Caitlin M; Johnson, Karen L et al. (2014) Digital anthropomorphic phantoms of non-rigid human respiratory and voluntary body motion for investigating motion correction in emission imaging. Phys Med Biol 59:3669-82
Mukherjee, Joyeeta Mitra; Hutton, Brian F; Johnson, Karen L et al. (2013) An evaluation of data-driven motion estimation in comparison to the usage of external-surrogates in cardiac SPECT imaging. Phys Med Biol 58:7625-46
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Dey, Joyoni; King, Michael A (2009) Theoretical and Numerical Study of MLEM and OSEM Reconstruction Algorithms for Motion Correction in Emission Tomography. IEEE Trans Nucl Sci 56:2739-2749
McNamara, Joseph E; Pretorius, P Hendrik; Johnson, Karen et al. (2009) A flexible multicamera visual-tracking system for detecting and correcting motion-induced artifacts in cardiac SPECT slices. Med Phys 36:1913-23
Mukherjee, Joyeeta Mitra; McNamara, Joseph E; Johnson, Karen L et al. (2009) Estimation of Rigid-Body and Respiratory Motion of the Heart From Marker-Tracking Data for SPECT Motion Correction. IEEE Trans Nucl Sci 56:147-155
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