Technical advancements and protocol optimization of diffusion-weighted imaging (DWI) in liver

Ni, Ping; Yang, Lin; Zhong, Qun; Chen, Ziqian; Sandrasegaran, Kumar; Lin, Chen

doi:10.1007/s00261-015-0602-x

Cited by 23 publications

(18 citation statements)

References 84 publications

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“…The single-shot EPI had the drawbacks of low spatial resolution and signal-to-noise ratio Ni et al. ( 42 ). The multi-shot EPI technique addresses this drawback.…”

Section: Discussionmentioning

confidence: 99%

Multi-channel GAN–based calibration-free diffusion-weighted liver imaging with simultaneous coil sensitivity estimation and reconstruction

Lyu

Yan

et al. 2023

Front. Oncol.

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IntroductionDiffusion-weighted imaging (DWI) with parallel reconstruction may suffer from a mismatch between the coil calibration scan and imaging scan due to motions, especially for abdominal imaging.MethodsThis study aimed to construct an iterative multichannel generative adversarial network (iMCGAN)-based framework for simultaneous sensitivity map estimation and calibration-free image reconstruction. The study included 106 healthy volunteers and 10 patients with tumors.ResultsThe performance of iMCGAN was evaluated in healthy participants and patients and compared with the SAKE, ALOHA-net, and DeepcomplexMRI reconstructions. The peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), root mean squared error (RMSE), and histograms of apparent diffusion coefficient (ADC) maps were calculated for assessing image qualities. The proposed iMCGAN outperformed the other methods in terms of the PSNR (iMCGAN: 41.82 ± 2.14; SAKE: 17.38 ± 1.78; ALOHA-net: 20.43 ± 2.11 and DeepcomplexMRI: 39.78 ± 2.78) for b = 800 DWI with an acceleration factor of 4. Besides, the ghosting artifacts in the SENSE due to the mismatch between the DW image and the sensitivity maps were avoided using the iMCGAN model.DiscussionThe current model iteratively refined the sensitivity maps and the reconstructed images without additional acquisitions. Thus, the quality of the reconstructed image was improved, and the aliasing artifact was alleviated when motions occurred during the imaging procedure.

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“…The single-shot EPI had the drawbacks of low spatial resolution and signal-to-noise ratio Ni et al. ( 42 ). The multi-shot EPI technique addresses this drawback.…”

Section: Discussionmentioning

confidence: 99%

Multi-channel GAN–based calibration-free diffusion-weighted liver imaging with simultaneous coil sensitivity estimation and reconstruction

Lyu

Yan

et al. 2023

Front. Oncol.

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“…Diffusion-weighted imaging (DWI) measures the ADC values using a single exponential model, which could quantitatively reflect the diffusion motion of water molecules in tissues ( 8 ). DWI detects the physiological characteristics of tissue non-invasively by measuring the diffusion properties of water molecules ( 9 ). Several studies have applied the mean ADC value, which was significantly higher in the loose fibrosis group PDAC than in the dense fibrosis group and negatively correlated with PDAC fibrosis ( 10 – 12 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Histopathology of Pancreatic Ductal Adenocarcinoma by Intravoxel Incoherent Motion-Diffusion Weighted Imaging Comparing With Diffusion-Weighted Imaging

et al. 2021

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ObjectivesTo explore the differences between intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and diffusion-weighted imaging (DWI) in evaluating the histopathological characters of pancreatic ductal adenocarcinoma (PDAC).MethodsThis retrospective study enrolled 50 patients with PDAC confirmed by pathology from December 2018 to May 2020. All patients underwent DWI and IVIM-DWI before surgeries. Patients were classified into low- and high-fibrosis groups. Apparent diffusion coefficient (ADC), diffusion coefficient (D), false diffusion coefficient (D*), and perfusion fraction (f) were measured by two radiologists, respectively in GE AW 4.7 post-processing station, wherein ADC values were derived by mono-exponential fits and f, D, D* values were derived by biexponential fits. The tumor tissue was stained with Sirius red, CD34, and CK19 to evaluate fibrosis, microvascular density (MVD), and tumor cell density. Furthermore, the correlation between ADC, D, D*, and f values and histopathological results was analyzed.ResultsThe D values were lower in the high-fibrosis group than in the low-fibrosis group, while the f values were opposite. Further, no statistically significant differences were detected in ADC and D* values between the high- and low-fibrosis groups. The AUC of D and f values had higher evaluation efficacy in the high- and low-fibrosis groups than ADC values. A significant negative correlation was established between D values, and fibrosis and a significant positive correlation were observed between f values and fibrosis. No statistical difference was detected between DWI/IVIM parameters values and MVD or tumor cell density except for the positive correlation between D* values and tumor cell density.ConclusionsD and f values derived from the IVIM model had higher sensitivity and diagnostic performance for grading fibrosis in PDAC compared to the conventional DWI model. IVIM-DWI may have the potential as an imaging biomarker for predicting the fibrosis grade of PDAC.

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“…DWI was demonstrated to be a useful adjunct sequence to DCE imaging, improving diagnostic accuracy of liver nodules compared to DCE imaging alone. Apparent diffusion coefficient (ADC) is obtained by the monoexponential model of conventional DWI with the assumption that water diffusion obeys the Gaussian law [11]. Previous studies have reported DWI has improved the sensitivity and detection rate of HCC when combined with enhanced MRI [12].…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have reported DWI has improved the sensitivity and detection rate of HCC when combined with enhanced MRI [12]. However, some reports have also suggested that it may be difficult to differentiate HCC from other solid hepatic lesions like FNH, HCA, and metastasis by DWI due to the overlapping ADC values between HCC and these solid lesions as a result of their increased cellularity [11, 13, 14]. In 2005, Diffusion kurtosis MR imaging (DKI), first proposed by Jensen et al [15], was described as water diffusion deviates from the Gaussian law due to the complexity of microenvironment, such as cell membranes, vessels, or macromolecules.…”

Section: Introductionmentioning

confidence: 99%

Diffusion Kurtosis MR Imaging versus Conventional Diffusion-Weighted Imaging for Distinguishing Hepatocellular Carcinoma from Benign Hepatic Nodules

Jia

Cai

Wang

et al. 2019

Contrast Media & Molecular Imaging

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Objectives. To assess the efficacy of diffusion kurtosis imaging (DKI) and compare DKI-derived parameters with conventional diffusion-weighted imaging (DWI) for distinguishing hepatocellular carcinoma (HCC) from benign hepatic nodules including focal nodular hyperplasia (FNH), hemangioma, and hepatocellular adenoma (HCA). Materials and Methods. 151 patients with 182 hepatic nodules (114 HCCs and 68 benign nodules including 33 FNHs, 29 hemangiomas, and 6 HCAs) were analyzed. Preoperative MRI examinations including DKI (b values: 0, 200, 500, 800, 1500, and 2000 sec/mm2) were performed, and kurtosis (K), diffusivity (D), and apparent diffusion coefficient (ADC) were calculated. The efficacy of DKI-derived parameters K, D, and ADC for distinguishing HCC from these benign nodules was analyzed. Results. ROC (receiver operating characteristic curve) analysis showed the optimal cutoff values of ADC, D, and K for identification of these benign nodules, and HCCs were 1.295 (area under the curve (AUC): 0.826; sensitivity 80.6%; specificity 70.8%), 1.787 (AUC: 0.770; sensitivity 83.6%; specificity 59.6%), and 1.002 (AUC: 0.761; sensitivity 65.5%; specificity 79.0%), respectively. Statistically significant differences were found in ADC, D, and K values between groups of HCC-FNH and HCC-hemangioma (P<0.05). There were significant differences in K and ADC values between groups of FNH-hemangioma and HCA-hemangioma (P<0.05), respectively. Using logistic regression analysis, a regression equation was obtained: LogitP=−1.982X1+1.385X3+1.948(X1: ADC; X3: K), and odds ratios (OR) were 0.138 (95% confidence interval (CI): 0.052, 0.367), and 8.996 (95% CI: 0.970, 16.460), respectively. Conclusion. Both ADC value and DKI-derived parameters K and D values have demonstrated a higher preoperative efficacy in distinguishing HCC from FNH, hemangioma, and HCA. No evidence was shown to suggest D or K value was superior to the ADC value.

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Technical advancements and protocol optimization of diffusion-weighted imaging (DWI) in liver

Abstract: An area of rapid advancement in abdominal MRI is diffusion-weighted imaging (DWI). By

Cited by 23 publications

References 84 publications

Multi-channel GAN–based calibration-free diffusion-weighted liver imaging with simultaneous coil sensitivity estimation and reconstruction

Multi-channel GAN–based calibration-free diffusion-weighted liver imaging with simultaneous coil sensitivity estimation and reconstruction

Evaluating the Histopathology of Pancreatic Ductal Adenocarcinoma by Intravoxel Incoherent Motion-Diffusion Weighted Imaging Comparing With Diffusion-Weighted Imaging

Diffusion Kurtosis MR Imaging versus Conventional Diffusion-Weighted Imaging for Distinguishing Hepatocellular Carcinoma from Benign Hepatic Nodules

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