Comparison of tri-exponential decay versus bi-exponential decay and full fitting versus segmented fitting for...

To determine whether bi- or tri-exponential models, and full or segmented fittings, better fit the intravoxel incoherent motion (IVIM) imaging signal of healthy livers.

Diffusion-weighted images were acquired with a 3 T scanner using a respiratory-triggered echo-planar sequence and 16 b-values (0-800 s/mm² ). Eighteen healthy volunteers had their livers scanned twice in the same session, and then once in another session. Liver parenchyma region-of-interest-based measurements were processed with bi-exponential and tri-exponential models, with both full fitting and segmented fitting (threshold b-value = 200 s/mm² ).

With the signal of all scans averaged, bi-exponential model full fitting showed D_slow  = 1.14 × 10^-3  mm² /s, D_fast  = 193.6 × 10^-3  mm² /s, and perfusion fraction (PF) = 16.9%, and segmented fitting showed D_slow  = 0.98 × 10^-3  mm² /s, D_fast  = 42.2 × 10^-3  mm² /s, and PF = 23.3%. IVIM parameters derived from the tri-exponential model were similar for full fitting and segmented fitting, with slow (D'_slow  = 0.98 × 10^-3  mm² /s; F'_slow  = 76.4 or 76.6%), fast (D'_fast  = 15.1 or 15.4 × 10^-3  mm² /s; F'_fast  = 11.8 or 11.7%) and very fast (D'_Vfast  = 445.0 or 448.8 × 10^-3  mm² /s; F'_Vfast  = 11.8 or 11.7%) diffusion compartments. The tri-exponential model provided an overall better fit than the bi-exponential model. For the bi-exponential model, full fitting provided a better fit at very low and low b-values compared with segmented fitting, with the latter tending to underestimate D_fast ; however, the 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.

For healthy liver, tri-exponential modeling is preferred to bi-exponential modeling. For the bi-exponential model, segmented fitting underestimates D_fast , but offers a more accurate estimation of D_slow .