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Journal of Nuclear Medicine

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OtherClinical Investigations (Human)

Concurrent Respiratory Motion Correction of Abdominal PET and DCE-MRI using a Compressed Sensing Approach

Niccolo Fuin, Onofrio Antonio Catalano, Michele Scipioni, Lisanne P.W. Canjels, David Izquierdo, Stefano Pedemonte and Ciprian Catana
Journal of Nuclear Medicine February 2018, jnumed.117.203943; DOI: https://doi.org/10.2967/jnumed.117.203943
Niccolo Fuin
1 Massachusetts General Hospital and Harvard Medical School, United States;
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Onofrio Antonio Catalano
1 Massachusetts General Hospital and Harvard Medical School, United States;
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Michele Scipioni
2 Universita' di Pisa, Italy;
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Lisanne P.W. Canjels
3 Eindhoven University of Technology, Netherlands;
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David Izquierdo
1 Massachusetts General Hospital and Harvard Medical School, United States;
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Stefano Pedemonte
4 Harvard Medical School, United States;
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Ciprian Catana
5 Massachusetts General Hospital, United States
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Abstract

Purpose: We present an approach for concurrent reconstruction of respiratory motion compensated abdominal DCE-MRI and PET data in an integrated PET/MR scanner. The MR and PET reconstructions share the same motion vector fields (MVFs) derived from radial MR data; the approach is robust to changes in respiratory pattern and do not increase the total acquisition time. Methods: PET and DCE-MRI data of 12 oncological patients were simultaneously acquired for 6 minutes on an integrated PET/MR system after administration of 18 F-FDG and gadoterate meglumine. Golden-angle radial MR data were continuously acquired simultaneously with PET data and sorted into multiple motion phases based on 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. MVFs obtained using the full 6-minute (MC_6-min) and only the last 1 minute (MC_1-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-MRI images (moco_GRASP). The motion-correction methods (MC_6-min and MC_1-min) were evaluated by qualitative analysis of the MR images and quantitative analysis of maximum and mean standardized uptake values (SUVmax, SUVmean), contrast, signal-to-noise ratio (SNR) and lesion volume in the PET images. Results: Motion corrected MC_6-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 MC_1-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 VIBE 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 to routine PET/DCE-MRI protocols.

  • Image Reconstruction
  • Oncology: Liver
  • PET/MRI
  • DCE-MRI
  • PET/MRI
  • compressed sensing
  • motion-correction
  • Copyright © 2018 by the Society of Nuclear Medicine and Molecular Imaging, Inc.
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Journal of Nuclear Medicine: 66 (5)
Journal of Nuclear Medicine
Vol. 66, Issue 5
May 1, 2025
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Concurrent Respiratory Motion Correction of Abdominal PET and DCE-MRI using a Compressed Sensing Approach
Niccolo Fuin, Onofrio Antonio Catalano, Michele Scipioni, Lisanne P.W. Canjels, David Izquierdo, Stefano Pedemonte, Ciprian Catana
Journal of Nuclear Medicine Feb 2018, jnumed.117.203943; DOI: 10.2967/jnumed.117.203943

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Concurrent Respiratory Motion Correction of Abdominal PET and DCE-MRI using a Compressed Sensing Approach
Niccolo Fuin, Onofrio Antonio Catalano, Michele Scipioni, Lisanne P.W. Canjels, David Izquierdo, Stefano Pedemonte, Ciprian Catana
Journal of Nuclear Medicine Feb 2018, jnumed.117.203943; DOI: 10.2967/jnumed.117.203943
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Keywords

  • Image Reconstruction
  • Oncology: Liver
  • PET/MRI
  • DCE-MRI
  • compressed sensing
  • motion-correction
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