A whole-body diffusion MRI normal atlas: development, evaluation and initial use

Cancer Imaging

Research Article

Alexander Korenyushkin¹  Gunilla Enblad²  Filip Malmberg³  Robin Strand³  Sambit Tarai⁴  Therese Sjöholm⁴  Håkan Ahlström⁵  Joel Kullberg⁵ 

[1] Antaros Medical AB, Mölndal, Sweden;Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden;Department of Information Technology, Uppsala University, Uppsala, Sweden;Department of Surgical Sciences, Uppsala University, Uppsala, Sweden;Department of Surgical Sciences, Uppsala University, Uppsala, Sweden;Antaros Medical AB, Mölndal, Sweden;

关键词: Whole-body DWI;    ADC;    Normal atlas;    Voxel-wise analysis;    Precision;    Lymphoma;    Automated segmentation;   

DOI  :  10.1186/s40644-023-00603-5

 received in 2023-03-09, accepted in 2023-08-28,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundStatistical atlases can provide population-based descriptions of healthy volunteers and/or patients and can be used for region- and voxel-based analysis. This work aims to develop whole-body diffusion atlases of healthy volunteers scanned at 1.5T and 3T. Further aims include evaluating the atlases by establishing whole-body Apparent Diffusion Coefficient (ADC) values of healthy tissues and including healthy tissue deviations in an automated tumour segmentation task.MethodsMulti-station whole-body Diffusion Weighted Imaging (DWI) and water-fat Magnetic Resonance Imaging (MRI) of healthy volunteers (n = 45) were acquired at 1.5T (n = 38) and/or 3T (n = 29), with test-retest imaging for five subjects per scanner. Using deformable image registration, whole-body MRI data was registered and composed into normal atlases. Healthy tissue ADCmean was manually measured for ten tissues, with test-retest percentage Repeatability Coefficient (%RC), and effect of age, sex and scanner assessed. Voxel-wise whole-body analyses using the normal atlases were studied with ADC correlation analyses and an automated tumour segmentation task. For the latter, lymphoma patient MRI scans (n = 40) with and without information about healthy tissue deviations were entered into a 3D U-Net architecture.ResultsSex- and Body Mass Index (BMI)-stratified whole-body high b-value DWI and ADC normal atlases were created at 1.5T and 3T. %RC of healthy tissue ADCmean varied depending on tissue assessed (4–48% at 1.5T, 6–70% at 3T). Scanner differences in ADCmean were visualised in Bland-Altman analyses of dually scanned subjects. Sex differences were measurable for liver, muscle and bone at 1.5T, and muscle at 3T. Volume of Interest (VOI)-based multiple linear regression, and voxel-based correlations in normal atlas space, showed that age and ADC were negatively associated for liver and bone at 1.5T, and positively associated with brain tissue at 1.5T and 3T. Adding voxel-wise information about healthy tissue deviations in an automated tumour segmentation task gave numerical improvements in the segmentation metrics Dice score, sensitivity and precision.ConclusionsWhole-body DWI and ADC normal atlases were created at 1.5T and 3T, and applied in whole-body voxel-wise analyses.

【 授权许可】

CC BY   
© International Cancer Imaging Society (ICIS) 2023

【 图 表 】

Fig. 5

42004_2023_990_Article_IEq32.gif

13690_2023_1170_Article_IEq30.gif

13690_2023_1170_Article_IEq28.gif

Fig. 1

Fig. 3

42004_2023_995_Article_IEq11.gif

Fig. 4

Fig. 5

Fig. 2

12888_2023_5159_Article_IEq1.gif

40249_2023_1135_Article_IEq5.gif

40249_2023_1135_Article_IEq4.gif

42004_2023_990_Article_IEq59.gif

Fig. 1