Niccolo Fuin1, Onofrio A Catalano1, Michele Scipioni1,2, Lisanne P W Canjels3, David Izquierdo-Garcia1, Stefano Pedemonte1, Ciprian Catana4. 1. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts. 2. Department of Information Engineering, University of Pisa, Pisa, Italy; and. 3. Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. 4. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts ccatana@nmr.mgh.harvard.edu.
Abstract
We present an approach for concurrent reconstruction of respiratory motion-compensated abdominal dynamic contrast-enhanced (DCE)-MRI and PET data in an integrated PET/MR scanner. The MR and PET reconstructions share the same motion vector fields derived from radial MR data; the approach is robust to changes in respiratory pattern and does not increase the total acquisition time. Methods: PET and DCE-MRI data of 12 oncologic patients were simultaneously acquired for 6 min on an integrated PET/MR system after administration of 18F-FDG and gadoterate meglumine. Golden-angle radial MR data were continuously acquired simultaneously with PET data and sorted into multiple motion phases on the basis of a respiratory signal derived directly from the radial MR data. The resulting multidimensional dataset was reconstructed using a compressed sensing approach that exploits sparsity among respiratory phases. Motion vector fields obtained using the full 6-min (MC6-min) and only the last 1 min (MC1-min) of data were incorporated into the PET reconstruction to obtain motion-corrected PET images and in an MR iterative reconstruction algorithm to produce a series of motion-corrected DCE-MR images (moco_GRASP). The motion-correction methods (MC6-min and MC1-min) were evaluated by qualitative analysis of the MR images and quantitative analysis of SUVmax and SUVmean, contrast, signal-to-noise ratio (SNR), and lesion volume in the PET images. Results: Motion-corrected MC6-min PET images demonstrated 30%, 23%, 34%, and 18% increases in average SUVmax, SUVmean, contrast, and SNR and an average 40% reduction in lesion volume with respect to the non-motion-corrected PET images. The changes in these figures of merit were smaller but still substantial for the MC1-min protocol: 19%, 10%, 15%, and 9% increases in average SUVmax, SUVmean, contrast, and SNR; and a 28% reduction in lesion volume. Moco_GRASP images were deemed of acceptable or better diagnostic image quality with respect to conventional breath-hold Cartesian volumetric interpolated breath-hold examination acquisitions. Conclusion: We presented a method that allows the simultaneous acquisition of respiratory motion-corrected diagnostic quality DCE-MRI and quantitatively accurate PET data in an integrated PET/MR scanner with negligible prolongation in acquisition time compared with routine PET/DCE-MRI protocols.
We present an approach for concurrent reconstruction of respiratory motion-compensated abdominal dynamic contrast-enhanced (DCE)-MRI and PET data in an integrated PET/MR scanner. The MR and PET reconstructions share the same motion vector fields derived from radial MR data; the approach is robust to changes in respiratory pattern and does not increase the total acquisition time. Methods: PET and DCE-MRI data of 12 oncologic patients were simultaneously acquired for 6 min on an integrated PET/MR system after administration of 18F-FDG and gadoterate meglumine. Golden-angle radial MR data were continuously acquired simultaneously with PET data and sorted into multiple motion phases on the basis of a respiratory signal derived directly from the radial MR data. The resulting multidimensional dataset was reconstructed using a compressed sensing approach that exploits sparsity among respiratory phases. Motion vector fields obtained using the full 6-min (MC6-min) and only the last 1 min (MC1-min) of data were incorporated into the PET reconstruction to obtain motion-corrected PET images and in an MR iterative reconstruction algorithm to produce a series of motion-corrected DCE-MR images (moco_GRASP). The motion-correction methods (MC6-min and MC1-min) were evaluated by qualitative analysis of the MR images and quantitative analysis of SUVmax and SUVmean, contrast, signal-to-noise ratio (SNR), and lesion volume in the PET images. Results: Motion-corrected MC6-min PET images demonstrated 30%, 23%, 34%, and 18% increases in average SUVmax, SUVmean, contrast, and SNR and an average 40% reduction in lesion volume with respect to the non-motion-corrected PET images. The changes in these figures of merit were smaller but still substantial for the MC1-min protocol: 19%, 10%, 15%, and 9% increases in average SUVmax, SUVmean, contrast, and SNR; and a 28% reduction in lesion volume. Moco_GRASP images were deemed of acceptable or better diagnostic image quality with respect to conventional breath-hold Cartesian volumetric interpolated breath-hold examination acquisitions. Conclusion: We presented a method that allows the simultaneous acquisition of respiratory motion-corrected diagnostic quality DCE-MRI and quantitatively accurate PET data in an integrated PET/MR scanner with negligible prolongation in acquisition time compared with routine PET/DCE-MRI protocols.
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