Clinical Pilot of a Deep Learning Elastic Registration Algorithm to Improve Misregistration Artifact and Image Quality on Routine Oncologic PET/CT.

RATIONALE AND OBJECTIVES

Misregistration artifacts between the PET and attenuation correction CT (CTAC) exams can degrade image quality and cause diagnostic errors. Deep learning (DL)-warped elastic registration methods have been proposed to improve misregistration errors.

MATERIALS AND METHODS

30 patients undergoing routine oncologic examination (20 F-FDG PET/CT and 10 Cu-DOTATATE PET/CT) were retrospectively identified and compared using unmodified CTAC, and a DL-augmented spatial transformation CT attenuation map. Primary endpoints included differences in subjective image quality and standardized uptake values (SUV). Exams were randomized to reduce reader bias, and three radiologists rated image quality across six anatomic sites using a modified Likert scale. Measures of local bias and lesion SUV were also quantitatively evaluated.

RESULTS

The DL attenuation correction methods were associated with higher image quality and reduced misregistration artifacts (Mean F-FDG quality rating=3.5-3.8 for DL vs 3.2-3.5 for standard reconstruction (STD); Mean Cu-DOTATATE quality rating= 3.2-3.4 for DL vs 2.1-3.3; P < 0.05 for STD, for all except Cu-DOTATATE inferior spleen). Percent change in superior liver SUV for F-FDG and Cu-DOTATATE were 5.3 ± 4.9 and 8.2 ± 4.1%, respectively. Measures of signal-to-noise ratio were significantly improved for the DL over STD (Hepatopulmonary index (HPI) [F-FDG] = 4.5 ± 1.2 vs 4.0 ± 1.1, P < 0.001; HPI [Cu-DOTATATE] = 16.4 ± 16.9 vs 12.5 ± 5.5, P = 0.039).

CONCLUSION

Deep learning elastic registration for CT attenuation correction maps on routine oncology PET/CT decreases misregistration artifacts, with a greater impact on PET scans with longer acquisition times.