Comparison of tri-exponential decay vs. bi-exponential decay and full fitting vs. segmented fitting for modeling liver intravoxel incoherent motion diffusion MRI
Olivier Chevallier, Nan Zhou, Jean-Pierre Cercueil, Jian He, Romaric Loffroy, Yì Xiáng J. Wáng
Abstract
PurposeTo determine whether bi- or tri-exponential models, and full or segmented fittings, better fit IVIM imaging signal of healthy livers.\n\nMaterials and methodsDiffusion-weighted images were acquired with a 3-T scanner using respiratory-triggered echo-planar sequence and 16 b-values (0[~]800 s/mm2). Eighteen healthy volunteers had liver scanned twice in the same session, and then once again in another session. Region of interest (ROI)-based measurements were processed with bi-exponential model full fitting and segmented fitting (threshold b-value = 80 s/mm2), as well as tri-exponential model full fitting and segmented fitting (threshold b-value = 200 s/mm2).\n\nResultsWith all scans signal averaged, bi-exponential model full fitting showed Dslow=1.14, Dfast=193.6x10-3 mm2/s, and PF=16.9%, and segmented fitting showed Dslow=1.03, Dfast=56.7x10-3 mm2/s, and PF=21.3%. IVIM parameters derived from tri-exponential model were similar for full fitting and segmented fitting, with a slow (Dslow=0.98x10-3 mm2/s; Fslow=76.4 or 76.6%), a fast (Dfast=15.1 or 15.4x10-3 mm2/s; Ffast=11.8 or 11.7%) and a very fast (DVfast=445.0 or 448.8x10-3 mm2/s; FVfast=11.8 or 11.7 %) diffusion compartments. Tri-exponential model provided an overall better fit than bi-exponential model. For bi-exponential model, full fitting provided better fit at very low and low b-values compared with segmented fitting with the later tended to underestimate Dfast, however, segmented method demonstrated lower error in signal prediction for high b-values. Compared with full fitting, tri-exponential segmented fitting offered better scan-rescan reproducibility.\n\nConclusionFor healthy liver, tri-exponential modelling is preferred than bi-exponential modelling. For bi-exponential model, segmented fitting underestimates Dfast, but offers more accurate estimation of Dslow.
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