Concurrent Respiratory Motion Correction of Abdominal PET and Dynamic Contrast-Enhanced–MRI Using a Compressed Sensing Approach
| العنوان: | Concurrent Respiratory Motion Correction of Abdominal PET and Dynamic Contrast-Enhanced–MRI Using a Compressed Sensing Approach |
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| المؤلفون: | Michele Scipioni, Stefano Pedemonte, David Izquierdo-Garcia, Niccolo Fuin, Ciprian Catana, Lisanne P.W. Canjels, Onofrio A. Catalano |
| المصدر: | Journal of Nuclear Medicine. 59:1474-1479 |
| بيانات النشر: | Society of Nuclear Medicine, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Physics and Instrumentation, Scanner, Time Factors, DCE-MRI, Image quality, Computer science, Image Processing, Movement, media_common.quotation_subject, Contrast Media, Iterative reconstruction, Signal-To-Noise Ratio, 030218 nuclear medicine & medical imaging, Respiratory motion correction, 03 medical and health sciences, Computer-Assisted, 0302 clinical medicine, Compressed sensing, Motion-correction, PET/MRI, Radiology, Nuclear Medicine and Imaging, Nuclear Medicine and Imaging, Abdomen, Image Processing, Computer-Assisted, Humans, Contrast (vision), Radiology, Nuclear Medicine and imaging, media_common, business.industry, Respiration, Magnetic Resonance Imaging, Motion vector, Positron-Emission Tomography, Dynamic contrast-enhanced MRI, Radiology, Nuclear medicine, business, 030217 neurology & neurosurgery |
| الوصف: | 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 (18)F-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 (MC(6-min)) and only the last 1 min (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-MR 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 SUV(max) and SUV(mean), 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 SUV(max), SUV(mean), 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 SUV(max), SUV(mean), 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. |
| تدمد: | 2159-662X 0161-5505 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42b2c1537b0a140d694935305de182b8Test https://doi.org/10.2967/jnumed.117.203943Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....42b2c1537b0a140d694935305de182b8 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 2159662X 01615505 |
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