Chamberlin Jordan H, Schaefferkoetter Joshua, Hamill James, Kabakus Ismail M, Horn Kevin P, O'Doherty Jim, Elojeimy Saeed
Department of Radiology, Medical University of South Carolina, Charleston, South Carolina, USA.
Siemens Medical Solutions USA, Inc., 810 Innovation Drive, Knoxville, Tennessee 37932, USA.
Acad Radiol. 2025 Feb;32(2):1015-1025. doi: 10.1016/j.acra.2024.09.044. Epub 2024 Oct 28.
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.
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.
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).
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.
PET与衰减校正CT（CTAC）检查之间的配准错误伪影会降低图像质量并导致诊断错误。已提出深度学习（DL）扭曲弹性配准方法来改善配准错误。
回顾性纳入30例行常规肿瘤检查的患者（20例F-FDG PET/CT和10例Cu-DOTATATE PET/CT），并使用未修改的CTAC以及DL增强的空间变换CT衰减图进行比较。主要终点包括主观图像质量和标准化摄取值（SUV）的差异。检查进行随机分组以减少阅片者偏倚，三位放射科医生使用改良的李克特量表对六个解剖部位的图像质量进行评分。还对局部偏倚和病变SUV的测量值进行了定量评估。
DL衰减校正方法与更高的图像质量和减少的配准错误伪影相关（F-FDG的DL平均质量评分为3.5 - 3.8，而标准重建（STD）为3.2 - 3.5；Cu-DOTATATE的DL平均质量评分为3.2 - 3.4，而STD为2.1 - 3.3；除Cu-DOTATATE脾脏较差外，所有STD的P均<0.05）。F-FDG和Cu-DOTATATE肝脏上部SUV的百分比变化分别为5.3±4.9和8.2±4.1%。DL的信噪比测量值比STD有显著改善（肝肺指数（HPI）[F-FDG] = 4.5±1.2对4.0±1.1，P<0.001；HPI[Cu-DOTATATE] = 16.4±16.9对12.5±5.5，P = 0.039）。
常规肿瘤PET/CT上用于CT衰减校正图的深度学习弹性配准可减少配准错误伪影，对采集时间较长的PET扫描影响更大。
