Combining Compressed Sensing with motion correction in acquisition and reconstruction for PET/MR

In the field of oncology, simultaneous Positron-Emission-Tomography/Magnetic Resonance (PET/MR) scanners offer a great potential for improving diagnostic accuracy. However, to achieve a high Signal-to-Noise Ratio (SNR) for an accurate lesion detection and quantification in the PET/MR images, one has...

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Bibliographic Details
Main Authors: Kustner, Thomas, Wurslin, Christian, Schmidt, Holger, Bin Yang
Format: Conference Proceeding
Language:English
Subjects:
Compressed Sensing
Computed tomography
Image reconstruction
Logic gates
Medical Image Processing
Minimization
Motion Correction
PET/MR
sparse representation
System-on-chip
Thyristors
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Summary:In the field of oncology, simultaneous Positron-Emission-Tomography/Magnetic Resonance (PET/MR) scanners offer a great potential for improving diagnostic accuracy. However, to achieve a high Signal-to-Noise Ratio (SNR) for an accurate lesion detection and quantification in the PET/MR images, one has to overcome the induced respiratory motion artifacts. The simultaneous acquisition allows performing a MR-based non-rigid motion correction of the PET data. It is essential to acquire a 4D (3D + time) motion model as accurate and fast as possible to minimize additional MR scan time overhead. Therefore, a Compressed Sensing (CS) acquisition by means of a variable-density Gaussian subsampling is employed to achieve high accelerations. Reformulating the sparse reconstruction as a combination of the inverse CS problem with a non-rigid motion correction improves the accuracy by alternately projecting the reconstruction results on either the motion-compensated CS reconstruction or on the motion model optimization. In-vivo patient data substantiates the diagnostic improvement.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2015.7178077