3,025 research outputs found

    Multiresolution spatiotemporal mechanical model of the heart as a prior to constrain the solution for 4D models of the heart.

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    In several nuclear cardiac imaging applications (SPECT and PET), images are formed by reconstructing tomographic data using an iterative reconstruction algorithm with corrections for physical factors involved in the imaging detection process and with corrections for cardiac and respiratory motion. The physical factors are modeled as coefficients in the matrix of a system of linear equations and include attenuation, scatter, and spatially varying geometric response. The solution to the tomographic problem involves solving the inverse of this system matrix. This requires the design of an iterative reconstruction algorithm with a statistical model that best fits the data acquisition. The most appropriate model is based on a Poisson distribution. Using Bayes Theorem, an iterative reconstruction algorithm is designed to determine the maximum a posteriori estimate of the reconstructed image with constraints that maximizes the Bayesian likelihood function for the Poisson statistical model. The a priori distribution is formulated as the joint entropy (JE) to measure the similarity between the gated cardiac PET image and the cardiac MRI cine image modeled as a FE mechanical model. The developed algorithm shows the potential of using a FE mechanical model of the heart derived from a cardiac MRI cine scan to constrain solutions of gated cardiac PET images

    INFLUENCE OF CARDIOVASCULAR RISK FACTORS ON AORTIC WALL MOTION AFTER REPAIR OF TYPE A AORTIC DISSECTION: AN ECG-GATED CT STUDY

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    OBJECTIVES: To evaluate aortic shape changes during cardiac cycle with dynamic computed tomographic angiography at important thoracic aorta anatomic landmarks in patients who previously underwent ascending aorta repair because of type A dissection, and correlate aortic wall motion with several cardiovascular risk factors. METHODS: From December 2009 to December 2011, 18 patients (14 men and 4 women, mean age 64 ± 12 y.o.) with previous aortic repair, underwent ECG-gated-CT follow-up. Aortic systolic and diastolic diameter and cross-sectional area were measured at 4 levels: 1 cm proximal (level A) and 1 (B), 3 (C) and 10 cm (D) distal to the origin of left subclavian artery. Results were assessed according to presence of diabetes, hypertension, smoking and age (2 groups: ≤ 55 and ≥56 years). RESULTS: This morpho-functional evaluation of aortic distensibility demonstrated a significant influence (p<0,05) on aortic wall-motion of hypertension at level A and diabetes at level D. Smoke has a borderline significance at level C and D. No significant correlation between aortic wall motion and age was evident, being results not significantly different in two age groups. CONCLUSIONS: Smoking, diabetes and hypertension play a role in impairing aortic distensibility and previous surgical repair does not interfere with vessel wall motion. Aortic distensibility might predict wall structural alteration due to cardiovascular risk factors before they become morphologically evident. This might influence timing of surveillance, making this specifically tailored for any single subject

    Motion estimation and correction for simultaneous PET/MR using SIRF and CIL

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    SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'

    Motion estimation and correction for simultaneous PET/MR using SIRF and CIL

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    SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'
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