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A combination of MRI diffusion-derived vessel density (DDVD) and slow diffusion coefficient (SDC) can reliably
Gen-Wen Hu1, Cai-Ying Li2, Gong-Hao Ling3
1Department of Radiology, Shenzhen People's Hospital (The First Affiliated Hospital, School of Medicine, Southern University of Science and Technology), Shenzhen, China.
Background:
Magnetic resonance diffusion-derived vessel density (DDVD) is a physiological surrogate of the functional area of microvessels per unit tissue area, calculated according to: DDVD(b0b2) = Sb0/ROIarea0 - Sb2/ROIarea2, where Sb0 and Sb2 refer to the tissue signal when b is 0 or 2 (s/mm2). Sb2 and ROIarea2 can also be approximated by other low b-values (such as b=10 s/mm2) diffusion-weighted images. Slow diffusion coefficient (SDC) measures tissue slow diffusion, calculated according to: SDC = [S(b 1) - S(b 2)]/(b 2 - b 1), where b 1 and b 2 refer to a high b-value and a higher b-value, respectively, and S(b 1) and S(b 2) denote the diffusion-weighted image signal intensity acquired at the high b-value and the higher b-value. In this study, we studied whether a combination of DDVD pixelwise map (DDVDm) and SDC pixelwise map (SDCm) can reliably separate liver hemangiomas (HGs) from liver mass-forming lesions (MFLs).
Methods:
Three liver diffusion-weighted magnetic resonance imaging (MRI) datasets were tested. Dataset-1 consisted of 17 HGs, 35 hepatocellular carcinomas (HCCs), 6 intra-hepatic cholangiocarcinomas (ICCs), 2 metastases (Mets), and 7 focal nodular hyperplasias (FNHs). Images were acquired at 3.0-T, and DDVD was calculated with b=0 and b=2 s/mm2 images, SDC was calculated with b=400 and b=600 s/mm2 images. Dataset-2 consisted of 7 HGs, 56 HCCs, 4 ICCs, 14 Mets, and 8 FNHs. Images were acquired at 3.0-T, and DDVD was calculated with b=0 and b=10 s/mm2 images, SDC was calculated with b=500 and b=800 s/mm2 images. Dataset-3 consisted of 8 HGs, 12 HCCs, 2 ICCs, 13 Mets, and 1 FNH. Images were acquired at 1.5-T, and DDVD was calculated with b=0 and b=50 s/mm2 images, SDC was calculated with b=700 and b=900 s/mm2 images. Two readers jointly read all the images and made a decision regarding whether HGs could be separated from MFLs.
Results:
Liver HG typically showed high signal on the DDVDm and very high liquid signal on the SDCm. MFL typically showed iso- or slightly high signal on DDVDm and lower than liquid signal on SDCm. For the combination of dataset-1 and dataset-2, 95.8% (23/24) of the HGs and 97.7% (129/132) of the MFLs were correctly classified with confidence. One HG and two MFLs were correctly classified but without confidence, only one MFL was classified as undecided. Dataset-3 overall had a low signal-to-noise ratio, and 75.0% (6/8) of the HGs and 96.4% (27/28) of the MFLs were correctly suggested. One of the HGs was classified as undecided, while one HG was incorrectly suggested to be an MFL.
Conclusions:
When a combination of DDVDm and SDCm is used to evaluate the liver, HG and MFL can mostly be reliably separated without the need for a contrast-enhanced scan. Our results will be relevant for HG confirmation when MRI is the first-line examination for the liver.
