Abstract
In PET imaging, patient motion due to respiration can lead to artifacts and blurring, in addition to quantification errors. The integration of PET imaging with MRI in PET/MRI scanners provides spatially aligned complementary clinical information and allows the use of high-contrast, high-spatial-resolution MR images to monitor and correct motion-corrupted PET data. On a patient cohort, we tested the ability of our joint PET/MRI-based predictive motion model to correct respiratory motion in PET and show it can improve lesion detectability and quantitation and reduce image artifacts. Methods: Using multiple tracers and multiple organ locations, we applied our motion correction method to 42 clinical PET/MRI patient datasets containing 162 PET-avid lesions. Quantitative changes were calculated using SUV changes in avid lesions. Lesion detectability changes were explored with a study in which 2 radiologists identified lesions in uncorrected and motion-corrected images and provided confidence scores. Results: Mean increases of 12.4% for SUVpeak and 17.6% for SUVmax after motion correction were found. In the detectability study, confidence scores for detecting avid lesions increased, with a rise in mean score from 2.67 to 3.01 (of 4) after motion correction and a rise in detection rate from 74% to 84%. Of 162 confirmed lesions, 49 showed an increase in all 3 metrics—SUVpeak, SUVmax, and combined reader confidence score—whereas only 2 lesions showed a decrease. We also present clinical case studies demonstrating the effect that respiratory motion correction of PET data can have on patient management, with increased numbers of detected lesions, improved lesion sharpness and localization, and reduced attenuation-based artifacts. Conclusion: We demonstrated significant improvements in quantification and detection of PET-avid lesions, with specific case study examples showing where motion correction has the potential to affect diagnosis or patient care.
Highlights
15 min per bed position), motion during the acquisition may lead to blurring in the resulting images and errors in quantification [1,2]
Current methods for respiratory motion correction in PET/MRI show an improvement in PET image quality, all require a change to the otherwise intended PET/MRI protocol to be able to collect the respiratory signal or MRI-derived motion model in a clinical setting
We described a method of respiratory motion correction via a joint PET/MRI-based predictive motion model using 1 min of simultaneously acquired PET and MRI data to capture intercycle and intracycle breathing variations [11]
Summary
15 min per bed position), motion during the acquisition may lead to blurring in the resulting images and errors in quantification [1,2]. PET respiratory motion can be corrected by gating (splitting data into respiratory states), reconstructing separate images, and registering to a common respiratory state [2,5,6] This technique requires a good signal-to-noise ratio in each gated image for accurate registration. Many methods use an external monitoring device to obtain a respiratory signal Such devices require time for set-up and readjustment and can fail because of mispositioning, patient movement, poor calibration, or signal drift and clipping. The method addressed many of the limitations found for the discrete binning method, which was used in our previous work [12] In this current work, we performed a pilot analysis of the method on a larger patient cohort by examining changes in SUV metrics on attenuation-corrected PET reconstructions and by performing a lesion detectability study. We present several examples of how respiratory motion correction may have the potential to affect clinical patient management in such areas as staging, diagnosis, and surgical planning
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