Set Of Diffusion-weighted Images Research Articles

Evaluating Biliary Malignancy with Measured and Calculated Ultra-high b-value Diffusion-weighted MR Imaging at 3T.

Although diffusion-weighted imaging (DWI) with ultra-high b-values is reported to be advantageous in the detection of some tumors, its applicability is not yet known in biliary malignancy. Therefore, this study aimed to evaluate the impact of measured b = 1400 s/mm2 (M1400) and calculated b = 1400 s/mm2 (C1400) DWI on image quality and quality of lesion discernibility using a modern 3T MR system compared to conventional b = 800 s/mm2 DWI (M800). We evaluated 56 patients who had pathologically proven biliary malignancy. All the patients underwent preoperative or baseline 3T MRI using DWI (b = 50, 400, 800, and 1400 s/mm2). The calculated DWI was obtained using a conventional DWI set (b = 50, 400, and 800). The tumor-to-bile contrast ratio (CR) and tumor SNR were compared between the different DWI images. Likert scores were given on a 5-point scale to assess the overall image quality, overall artifacts, ghost artifacts, misregistration artifacts, margin sharpness, and lesion discernibility. Repeated-measures analysis of variance with post hoc analyses was used for statistical evaluations. The CR of the tumor-to-bile was significantly higher in both M1400 and C1400 than in M800 (Pa < 0.01). SNRs were significantly higher in M800, followed by C1400 and M1400 (Pa < 0.01). Lesion discernibility was significantly improved for M1400, followed by C1400 and M800 for both readers (Pa < 0.01). Using a 3T MRI, both measured and calculated DWI with an ultra-high b-value offer superior lesion discernibility for biliary malignancy compared to the conventional DWI.

Identifying the midline thalamus in humans in vivo.

The midline thalamus is critical for flexible cognition, memory, and stress regulation in humans and its dysfunction is associated with several neurological and psychiatric disorders, including Alzheimer's disease, schizophrenia, and depression. Despite the pervasive role of the midline thalamus in cognition and disease, there is a limited understanding of its function in humans, likely due to the absence of a rigorous noninvasive neuroimaging methodology to identify its location. Here, we introduce a new method for identifying the midline thalamus in vivo using probabilistic tractography and k-means clustering with diffusion weighted imaging data. This approach clusters thalamic voxels based on data-driven cortical and subcortical connectivity profiles and then segments the midline thalamus according to anatomical connectivity tracer studies in rodents and macaques. Results from two different diffusion weighted imaging sets, including adult data (22-35years) from the Human Connectome Project (n = 127) and adolescent data (9-14years) collected at Florida International University (n = 34) showed that this approach reliably classifies midline thalamic clusters. As expected, these clusters were most evident along the dorsal/ventral extent of the third ventricle and were primarily connected to the agranular medial prefrontal cortex (e.g., anterior cingulate cortex), nucleus accumbens, and medial temporal lobe regions. The midline thalamus was then bisected based on a human brain atlas into a dorsal midline thalamic cluster (paraventricular and paratenial nuclei) and a ventral midline thalamic cluster (rhomboid and reuniens nuclei). This anatomical connectivity-based identification of the midline thalamus offers the opportunity for necessary investigation of this region in vivo in the human brain and how it relates to cognitive functions in humans, and to psychiatric and neurological disorders.

High-Resolution Diffusion-Weighted Imaging for Evaluation of Extramural Tumor Invasion in Primary Rectal Cancer.

The aim of this study was to determine the added value of high-resolution diffusion-weighted imaging (DWI) to T2-weighted imaging (T2WI) for evaluation of extramural tumor invasion (EMTI) in patients with primary rectal cancer. Seventy-eight patients who had undergone 3.0-T magnetic resonance imaging including DWI (b = 0, 1000 s/mm2, 2 mm iso-voxel) and subsequent surgery were included. Two blinded radiologists independently read the T2WI first and then the combined DWI set. They recorded their confidence level using a 5-point scale. The diagnostic accuracy was calculated by receiver operating characteristic curve analysis based on the histopathological results as the reference. The study population consisted of EMTI positive (n = 44) and negative (n = 34). The area under the curve was not significantly increased after adding DWI to T2WI (reader 1, 0.868-0.856, P = 0.5618; reader 2, 0.848-0.865, P = 0.4539). Adding DWI to T2WI showed no additional diagnostic value for the evaluation of EMTI in patients with primary rectal cancer.

Synthesizing High-b-Value Diffusion-weighted Imaging of the Prostate Using Generative Adversarial Networks

To develop and evaluate a diffusion-weighted imaging (DWI) deep learning framework based on the generative adversarial network (GAN) to generate synthetic high-b-value (b =1500 sec/mm2) DWI (SYNb1500) sets from acquired standard-b-value (b = 800 sec/mm2) DWI (ACQb800) and acquired standard-b-value (b = 1000 sec/mm2) DWI (ACQb1000) sets. This retrospective multicenter study included 395 patients who underwent prostate multiparametric MRI. This cohort was split into internal training (96 patients) and external testing (299 patients) datasets. To create SYNb1500 sets from ACQb800 and ACQb1000 sets, a deep learning model based on GAN (M0) was developed by using the internal dataset. M0 was trained and compared with a conventional model based on the cycle GAN (Mcyc). M0 was further optimized by using denoising and edge-enhancement techniques (optimized version of the M0 [Opt-M0]). The SYNb1500 sets were synthesized by using the M0 and the Opt-M0 were synthesized by using ACQb800 and ACQb1000 sets from the external testing dataset. For comparison, traditional calculated (b =1500 sec/mm2) DWI (CALb1500) sets were also obtained. Reader ratings for image quality and prostate cancer detection were performed on the acquired high-b-value (b = 1500 sec/mm2) DWI (ACQb1500), CALb1500, and SYNb1500 sets and the SYNb1500 set generated by the Opt-M0 (Opt-SYNb1500). Wilcoxon signed rank tests were used to compare the readers' scores. A multiple-reader multiple-case receiver operating characteristic curve was used to compare the diagnostic utility of each DWI set. When compared with the Mcyc, the M0 yielded a lower mean squared difference and higher mean scores for the peak signal-to-noise ratio, structural similarity, and feature similarity (P < .001 for all). Opt-SYNb1500 resulted in significantly better image quality (P ≤ .001 for all) and a higher mean area under the curve than ACQb1500 and CALb1500 (P ≤ .042 for all). A deep learning framework based on GAN is a promising method to synthesize realistic high-b-value DWI sets with good image quality and accuracy in prostate cancer detection.Keywords: Prostate Cancer, Abdomen/GI, Diffusion-weighted Imaging, Deep Learning Framework, High b Value, Generative Adversarial Networks© RSNA, 2021 Supplemental material is available for this article.

Reduced fractional anisotropy in projection, association, and commissural fiber networks in neonates with prenatal methamphetamine exposure.

Prenatal exposure to methamphetamine is associated with neurostructural changes, including alterations in white matter microstructure. This study investigated the effects of methamphetamine exposure on microstructure of global white matter networks in neonates. Pregnant women were interviewed beginning in mid-pregnancy regarding their methamphetamine use. Diffusion weighted imaging sets were acquired for 23 non-sedated neonates. White matter bundles associated with pairs of target regions within five networks (commissural fibers, left and right projection fibers, and left and right association fibers) were estimated using probabilistic tractography, and fractional anisotropy (FA) and diffusion measures determined within each connection. Multiple regression analyses showed that increasing methamphetamine exposure was significantly associated with reduced FA in all five networks, after control for potential confounders. Increased exposure was associated with lower axial diffusivity in the right association fiber network and with increased radial diffusivity in the right projection and left and right association fiber networks. Within the projection and association networks a subset of individual connections showed a negative correlation between FA and methamphetamine exposure. These findings are consistent with previous reports in older children and demonstrate that microstructural changes associated with methamphetamine exposure are already detectable in neonates.

Abstract WMP22: Synthetic Image Based Deep Learning of Stroke DWI Lesion Segmentation

Introduction: The application of deep learning to stroke image analysis (and medical images in general) faces two major challenges: first, it requires a large number of images to train, which is difficult to obtain. Second, the accurate outlining of infarcts is tedious, requires a high level of expertise, is subjective and error prone. The purpose of this work was to produce a large set of diffusion-weighted images (DWI) with perfectly defined realistic-appearing synthetic acute stroke lesions and to compare the segmentation performance of a deep neural network trained on these DWI scans with synthetic stroke lesions to a network trained on DWI scans of real stroke patients with stroke lesions manually outlined by neuroradiologists. Methods: 449 DWI scans with stroke lesions (72 ± 14y) and 2027 normal DWI scans (38 ± 24y) were coregistered, resampled, cropped to 96 x 80 x 40 voxels, normalized and divided into training/testing sets (80/20%). Stroke lesions were manually segmented by 3 neuroradiologists. 2000 synthetic 3D stroke DWI were produced by fusing thresholded (min 8%) signal increase of random DWI lesions to random coregistered normal DWI ( A ). A 3D U-Net (Tensorflow, depth 3, initial 64 feature maps doubled with each downsampling, bottleneck 3; hyperparameters optimized with cross-validation) was trained separately on 3 datasets: human-labeled real stroke cases (HL); 2000 synthetic cases (S); human-labeled real stroke cases + 2000 synthetic cases (HL+S). Results: The model trained on the human-labeled real stroke cases + 2000 synthetic cases (average dice coefficient between 300 and 600 epochs= 0.66±0.14) significantly outperformed the model trained on the 2000 synthetic cases only (0.60±0.14) and the model trained on human-labeled real stroke cases only (0.55±0.18; p&lt;10 -29 for all comparisons) ( B-C ). Conclusions: Deep learning segmentation of acute stroke lesions was significantly improved and was more stable by using synthetically generated images.

Differentiation of Small Hepatocellular Carcinoma From Dysplastic Nodules in Cirrhotic Liver: Texture Analysis Based on MRI Improved Performance in Comparison Over Gadoxetic Acid-Enhanced MR and Diffusion-Weighted Imaging.

Background: Accurate characterization of small (3 cm) hepatocellular carcinoma (sHCC) and dysplastic nodules (DNs) in cirrhotic liver is challenging. We aimed to investigate whether texture analysis (TA) based on T2-weighted images (T2WI) is superior to qualitative diagnosis using gadoxetic acid-enhanced MR imaging (Gd-EOB-MRI) and diffusion-weighted imaging (DWI) for distinguishing sHCC from DNs in cirrhosis.Materials and methods: Sixty-eight patients with 73 liver nodules (46 HCCs, 27 DNs) pathologically confirmed by operation were included. For imaging diagnosis, three sets of images were reviewed by two experienced radiologists in consensus: a Gd-EOB-MRI set, a DWI set, and a combined set (combination of Gd-EOB-MRI and DWI). For TA, 279 texture features resulting from T2WI were extracted for each lesion. The performance of each approach was evaluated by a receiver operating characteristic analysis. The area under the receiver operating characteristic curve (Az), sensitivity, specificity, and accuracy were determined.Results: The performance of TA (Az = 0.96) was significantly higher than that of imaging diagnosis using Gd-EOB-MRI set (Az = 0.86) or DWI set (Az = 0.80) alone in differentiation of sHCC from DNs (P = 0.008 and 0.025, respectively). The combination of Gd-EOB-MRI and DWI showed a greater sensitivity (95.6%) but reduced specificity (66.7%). The specificity of TA (92.6%) was significantly higher than that of the combined set (P < 0.001), but no significant difference was observed in sensitivity (97.8 vs. 95.6%, P = 0.559).Conclusion: TA-based T2WI showed a better classification performance than that of qualitative diagnosis using Gd-EOB-MRI and DW imaging in differentiation of sHCCs from DNs in cirrhotic liver. TA-based MRI may become a potential imaging biomarker for the early differentiation HCCs from DNs in cirrhosis.

Preoperative detection of colorectal liver metastases: DWI alone or combined with MDCT is no substitute for Gd-EOB-DTPA-enhanced MRI.

Background Magnetic resonance imaging (MRI) with hepatocyte specific contrast has the highest sensitivity for colorectal liver metastases but comes at high costs and long examination times. Purpose To evaluate if preoperative detection of colorectal liver metastases with less resource-consuming diffusion-weighted imaging (DWI) alone or in combination with multidetector computed tomography (MDCT) can compete with gadolinium-etoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA)-enhanced MRI. Material and Methods Forty-four patients with 123 colorectal liver metastases received MDCT and Gd-EOB-DTPA-enhanced MRI including DWI before liver resection for colorectal liver metastases. Five image sets were evaluated by two radiologists. The DWI set consisted of DWI, ADC map, coronal, axial T2-weighted single-shot sequences. The DWI-T2F set contained additionally respiratory-triggered T2-weighted TSE-SPIR sequences. The MDCT set consisted of four-phase MDCT, the MDCT-DWI set also contained DWI. The CE-MRI set contained all MRI sequences including Gd-EOB-DTPA. Reference standards was histopathology and follow-up. Results CE-MRI set had highest sensitivity ( P ≤ 0.013) with 95% compared to 72%, 73%, 73%, and 87% the for DWI set, DWI-T2W-FS set, MDCT set, and MDCT-DWI set, respectively. The CE-MRI set had the highest sensitivity ( P≤0.012) for colorectal liver metastases &lt;10 mm with 87% compared to 55%, 52%, 23%, and 58% for the DWI set, DWI-T2W-FS set, MDCT set, and MDCT-DWI set, respectively. The MDCT-DWI set improved sensitivity overall and in size-dependent subgroup analyses compared to the MDCT set ( P ≤ 0.031). The MDCT-DWI set showed the highest specificity of 98% followed by 98%, 98%, 95%, and 88% for the DWI set, DWI-T2W-FS set, MDCT set, and CE-MRI set, respectively. Conclusion The less resource and time-consuming DWI sets are inferior to Gd-EOB-DTPA-enhanced MRI for the detection of colorectal liver metastases. Gd-EOB-DTPA-enhanced MRI with its excellent sensitivity should be the preferred preoperative modality when meticulous lesion identification is essential. Combination of DWI with MDCT improved significantly sensitivity compared to each modality alone.

Accelerating cardiac diffusion tensor imaging combining local low-rank and 3D TV constraint.

Diffusion tensor magnetic resonance imaging (DT-MRI, or DTI) is a promising technique for invasively probing biological tissue structures. However, DTI is known to suffer from much longer acquisition time with respect to conventional MRI and the problem is worsened when dealing with in vivo acquisitions. Therefore, faster DTI for both ex vivo and in vivo scans is highly desired. This paper proposes a new compressed sensing (CS) reconstruction method that employs local low-rank (LLR) model and three-dimensional (3D) total variation (TV) constraint to reconstruct cardiac diffusion-weighted (DW) images from highly undersampled k-space data. The LLR model takes the set of DW images corresponding to different diffusion gradient directions as a 3D image volume and decomposes the latter into overlapping 3D blocks. Then, the 3D blocks are stacked as two-dimensional (2D) matrix. Finally, low-rank property is applied to each block matrix and the 3D TV constraint to the 3D image volume. The underlying constrained optimization problem is finally solved using the first-order fast method. The proposed method is evaluated on real ex vivo cardiac DTI data as a prerequisite to in vivo cardiac DTI applications. The results on real human ex vivo cardiac DTI images demonstrate that the proposed method exhibits lower reconstruction errors for DTI indices, including fractional anisotropy (FA), mean diffusivities (MD), transverse angle (TA), and helix angle (HA), compared to existing CS-based DTI image reconstruction techniques. The proposed method provides better reconstruction quality and more accurate DTI indices in comparison with the state-of-the-art CS-based DW image reconstruction methods.

Quantitative evaluation of fiber tractography with a Delaunay triangulation-based interpolation approach.

The recent challenge in high angular resolution diffusion imaging (HARDI) is to find a tractography process that provides information about the neural architecture within the white matter of the brain in a clinically feasible measurement time. The great success of the HARDI technique comes from its capability to overcome the problem of crossing fiber detection. However, it requires a large number of diffusion-weighted (DW) images which is problematic for clinical time and hardware. The main contribution of this paper is to develop a full tractography framework that gives an accurate estimate of the crossing fiber problem with the aim of reducing data acquisition time. We explore the interpolation in the gradient direction domain as a method to estimate the HARDI signal from a reduced set of DW images. The experimentation was performed in a first time on simulated data for a quantitative evaluation using the Tractometer system. We used, also, in vivo human brain data to demonstrate the potential of our pipeline. Results on both simulated and real data illustrate the effectiveness of our approach to perform the brain connectivity. Overall, we have shown that the proposed approach achieves competitive results to other tractography methods according to Tractometer connectivity metrics. Graphical Abstract.

A fractional motion diffusion model for a twice-refocused spin-echo pulse sequence.

The purpose of this study was to develop an analytical expression for a fractional motion (FM) diffusion model to characterize diffusion-induced signal attenuation in a twice-refocused spin-echo (TRSE) sequence that is resilient to eddy currents, and to demonstrate its applicability to human brain imaging in vivo. Based on the FM theory, which provides a unified statistical description for Langevin motions, the diffusion-weighted (DW) MR signal was measured with a TRSE sequence that balances the concomitant gradients. The analytical expression was fitted to a set of DW images acquired with 14 b-values (0-4000s/mm2 ) from a total of 10 healthy human subjects at 3T, yielding three FM parameter maps based on anomalous diffusion coefficient Dφ, ψ , diffusion increment variance φ, and diffusion correlation ψ, respectively. These parameters were used to characterize different brain regions in gray matter (GM), white matter (WM), and cerebrospinal fluid. The analytical expression for the TRSE-based FM model accurately described diffusion signal attenuation in healthy brain tissues at high b-values. TRSE's robustness against eddy currents was illustrated by comparing results from an expression for a conventional Stejskal-Tanner sequence. The TRSE-based FM model also produced consistent GM-WM contrast (p<0.01) across all brain regions studied, whereas the consistency was not observed with the Stejskal-Tanner-based FM model. This new analytical expression is expected to enable further investigations to probe tissue structures by exploiting anomalous diffusion properties without being hindered by eddy-current perturbations at high b-values.

Probabilistic vs. deterministic fiber tracking and the influence of different seed regions to delineate cerebellar-thalamic fibers in deep brain stimulation.

This study compared tractography approaches for identifying cerebellar-thalamic fiber bundles relevant to planning target sites for deep brain stimulation (DBS). In particular, probabilistic and deterministic tracking of the dentate-rubro-thalamic tract (DRTT) and differences between the spatial courses of the DRTT and the cerebello-thalamo-cortical (CTC) tract were compared. Six patients with movement disorders were examined by magnetic resonance imaging (MRI), including two sets of diffusion-weighted images (12 and 64 directions). Probabilistic and deterministic tractography was applied on each diffusion-weighted dataset to delineate the DRTT. Results were compared with regard to their sensitivity in revealing the DRTT and additional fiber tracts and processing time. Two sets of regions-of-interests (ROIs) guided deterministic tractography of the DRTT or the CTC, respectively. Tract distances to an atlas-based reference target were compared. Probabilistic fiber tracking with 64 orientations detected the DRTT in all twelve hemispheres. Deterministic tracking detected the DRTT in nine (12 directions) and in only two (64 directions) hemispheres. Probabilistic tracking was more sensitive in detecting additional fibers (e.g. ansa lenticularis and medial forebrain bundle) than deterministic tracking. Probabilistic tracking lasted substantially longer than deterministic. Deterministic tracking was more sensitive in detecting the CTC than the DRTT. CTC tracts were located adjacent but consistently more posterior to DRTT tracts. These results suggest that probabilistic tracking is more sensitive and robust in detecting the DRTT but harder to implement than deterministic approaches. Although sensitivity of deterministic tracking is higher for the CTC than the DRTT, targets for DBS based on these tracts likely differ.

가돌리늄 조영증강 자기공명영상과 확산강조영상의 간신경내분비종양 진단능력에 대한 비교연구

Background/Aims: To investigate the diagnostic performance of diffusion-weighted imaging (DWI) for hepatic neuroendocrine tumors (NET) compared with combined DWI and contrastenhanced magnetic resonance imaging (MRI) .MethodsFifteen patients with hepatic NET (n=128) underwent enhanced MRI and DWI with multiple-b values. We analyzed three different sets: Precontrast set; DWI set (added DWI); combined set (added enhanced image). Two reviewers rated possibility of NET using a 5-point scale for each image set. Their diagnostic performance was compared using Jackknife alternative free-response ROC (JAFROC).ResultsDiagnostic performance was better on the combined set (figure of merit [FOM]=0.852, 0.761) than the precontrast set (FOM=0.427, 0.572, P<0.05) and the DWI set (FOM=0.682, 0.620, P<0.05). However, DWI improved performance compared with precontrast set without statistical difference. In small NETs (<1 cm), all sets showed low sensitivity (10.7-65.9%) with high specificity (95.4-100%). Interobserver agreement was moderate in all image sets (k=0.521 to 0.589).ConclusionsCombined DWI and enhanced MRI were more useful for detecting NET. Although statistically insignficant, there was a trend in improved diagnostic performance with DWI.

Improve the image quality of orbital 3 T diffusion-weighted magnetic resonance imaging with readout-segmented echo-planar imaging

The aim of our study is to compare the image quality of readout-segmented echo-planar imaging (rs-EPI) and that of standard single-shot EPI (ss-EPI) in orbital 3 T diffusion-weighted (DW) magnetic resonance (MR) imaging in healthy subjects. Forty-two volunteers underwent two sets of orbital DW imaging scan at a 3 T MR unit, and image quality was assessed qualitatively and quantitatively. As a result, we found that rs-EPI could provide better image quality than standard ss-EPI, while no significant difference was found on the apparent diffusion coefficient between the two sets of DW images.

Diagnostic accuracy of diffusion-weighted MR imaging versus delayed gadolinium enhanced T1-weighted imaging in middle ear recurrent cholesteatoma: A retrospective study of 39 patients

MR imaging using diffusion-weighted (DW) images and delayed gadolinium-enhanced T1-weighted images is evolving into an alternative to second look surgery in detection of recurrent cholesteatomas. The aim of this study was to retrospectively compare the DW images, the post-gadolinium T1-weighted images and the combination of both methods in this indication. We retrospectively evaluated the MR examination of 39 patients clinically suspected for a recurrent cholesteatoma. Patients in the study underwent DW sequences, delayed gadolinium enhanced T1-weighted sequences as well as standard uninjected protocol using T1 and T2 sequences. Three blinded radiologists evaluated three data sets: a set of post-gadolinium T1-weighted images, a set of DW images and a set of the combination of both methods. The interobserver agreement was evaluated and the diagnostic accuracy of each method was described by sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). The performances of the three techniques were also evaluated using ROC curves, from which the AUC were compared. Results were compared with surgical results or a two-year follow-up. The overall sensitivity and specificity were respectively 63% and 71% for the post-gadolinium T1-weighted images, 88% and 75% for the DW images and 84% and 75% for the combined images. The PPV and NPV were respectively 89% and 33% for the post-gadolinium T1-weighted images, 93% and 62% for the DW images and 93% and 55% for the combined images. The sensitivity and the NPV were significantly different between the three methods (P<0.0001 and P=0.027). There was no statistically significant difference in specificity or PPV between the three methods (P=0.931 and P=0.650). The diagnostic accuracy evaluated with the AUC showed no statistically significant difference between the DW images and the combined images (P=0.433). MR imaging reliably identifies those patients suspected for recurrent cholesteatoma who require a surgical second look by using DW MR imaging. The combination with delayed gadolinium enhanced T1-weighted sequences does not significantly increase the diagnostic accuracy of the examination.

Hilar Cholangiocarcinoma: Value of Adding DW Imaging to Gadoxetic Acid–enhanced MR Imaging with MR Cholangiopancreatography for Preoperative Evaluation

To assess the benefit of adding diffusion-weighted (DW) imaging to gadoxetic acid-enhanced magnetic resonance (MR) imaging and MR cholangiopancreatography in the preoperative evaluation of hilar cholangiocarcinoma. The institutional review board approved this retrospective study and waived the requirement for informed consent. The study included 52 patients (36 men, 16 women; mean age, 63.4 years) with surgically confirmed hilar cholangiocarcinoma who underwent gadoxetic acid-enhanced MR imaging and DW imaging at 3.0 T between August 2010 and December 2011. Two observers independently reviewed two image sets--a gadoxetic acid set, including images from MR cholangiopancreatography, and a combined gadoxetic acid set and DW imaging set--to evaluate the tumor involvement of each biliary confluence and vascular and liver invasion by using receiver operating characteristic (ROC) curve analysis. For each observer, area under the ROC curve (Az) values for tumor involvement of the biliary confluence were 0.965 and 0.957 for the gadoxetic acid set and 0.980 and 0.975 for the combined set, respectively (P > .05). For detecting 105 biliary confluences with tumor involvement, the sensitivities with the combined set (97.1% [102 of 105] and 98.1% [103 of 105]) were higher than those with the gadoxetic acid set (91.4% [96 of 105] for both observers) (P = .029 and P = .016), although the specificities were similar with both image sets (P > .05). For the detection of liver invasion, the combined set (75.0% [15 of 20] for both observers) yielded better sensitivity than the gadoxetic acid set (50.0% [10 of 20] and 45.0% [nine of 20]) (P = .016 and P = .031). For evaluation of vascular invasion, the two image sets showed similar diagnostic performance. In the preoperative evaluation of hilar cholangiocarcinoma, the addition of DW imaging to gadoxetic acid-enhanced MR imaging could improve sensitivity in the evaluation of tumor extent along the bile duct and liver invasion.

Diagnosis of focal liver lesions with gadoxetic acid‐enhanced MRI: Is a shortened delay time possible by adding diffusion‐weighted imaging?

To determine whether the diagnostic performance of combined gadoxetic acid-enhanced dynamic phases and diffusion-weighted imaging (DWI) is comparable to the standard protocol with hepatobiliary phase (HBP) and DWI for detection and characterization of focal liver lesions in chronic liver disease. A total of 176 patients with 181 HCCs, 15 cholangiocarcinomas, and 32 benign lesions were included. Three combined gadoxetic acid-enhanced and DWI sets (dynamic phases [arterial, portal, and 3-min delay {3-min set} and dynamic phases with 10-min HBP [10-min set] or 20-min HBP [20-min set]) were analyzed by two observers to determine the diagnostic accuracy and sensitivity in detection of malignancy, and ability for lesion characterization. There was a trend, although not statistically significant, toward highest diagnostic accuracy and sensitivity for detecting malignancies in the 20-min set (mean, 0.945, 96.2), followed by the 10-min set (0.937, 95.9), and the 3-min set (0.923, 94.1) (P > 0.05). For lesion characterization, three image sets were equivalent (P > 0.05). For lesion detection and characterization in chronic liver diseases, 3-min set with DWI showed comparable efficacy to 10-min or 20-min set. However, the best diagnostic performance could be achieved by combination of all image sets.

Computed diffusion-weighted imaging of the prostate at 3 T: impact on image quality and tumour detection

To investigate the impact of prostate computed diffusion-weighted imaging (DWI) on image quality and tumour detection. Forty-nine patients underwent 3-T magnetic resonance imaging using a pelvic phased-array coil before prostatectomy, including DWI with b values of 50 and 1,000 s/mm(2). Computed DW images with b value 1,500 s/mm(2) were generated from the lower b-value images. Directly acquired b-1,500 DW images were obtained in 39 patients. Two radiologists independently assessed DWI for image quality measures and location of the dominant lesion. A third radiologist measured tumour-to-peripheral-zone (PZ) contrast. Pathological findings from prostatectomy served as the reference standard. Direct and computed b-1,500 DWI showed better suppression of benign prostate tissue than direct b-1,000 DWI for both readers (P ≤ 0.024). However, computed b-1,500 DWI showed less distortion and ghosting than direct b-1,000 and direct b-1,500 DWI for both readers (P ≤ 0.067). Direct and computed b-1,500 images showed better sensitivity and positive predictive value (PPV) for tumour detection than direct b-1,000 images for both readers (P ≤ 0.062), with no difference in sensitivity or PPV between direct and computed b-1,500 images (P ≥ 0.180). Tumour-to-PZ contrast was greater on computed b-1,500 than on either direct DWI set (P < 0.001). Computed DWI of the prostate using b value ≥1,000 s/mm(2) improves image quality and tumour detection compared with acquired standard b-value images. • Diffusion weighted MRI is increasingly used for diagnosing and assessing prostate carcinoma. • Prostate computed DWI can extrapolate high b-value images from lower b values. • Computed DWI provides greater suppression of benign tissue than lower b-value images. • Computed DWI provides less distortion and artefacts than images using same b value. • Computed DWI provides better diagnostic performance than lower b-value images.

Role of diffusion-weighted magnetic resonance imaging in the diagnosis of gallbladder cancer

To determine the additional diagnostic value of high b-value diffusion-weighted imaging (DWI) compared to conventional biliary magnetic resonance imaging (MRI) for differentiating gallbladder (GB) cancer from benign GB diseases with wall thickening. Thirty-nine patients with GB cancers and 36 patients with cholecystitis having preoperative biliary MRIs were included. All patients underwent unenhanced T1- and T2-weighted imaging (T2WI), Gd-enhanced dynamic MRI, and DWI (b values 0, 100, 500, 1000 s/mm(2) ). Two radiologists independently analyzed two sets of MRI for characterization of GB lesions: a conventional biliary image (CBI) set with T1- and T2WI and a dynamic image; and a DWI set composed of DWI and a CBI set. Diagnostic accuracy and sensitivity were evaluated using the receiver operator characteristic method. The mean apparent diffusion coefficient (ADC) values of the lesions were also calculated. The Az values were 0.856 and 0.960 for reviewers 1 and 2, respectively, with the CBI set and increased to 0.952 and 0.983 with the DWI set. The mean ADC value of GB carcinoma was 1.46 ± 0.45 × 10(-3) mm(2) /s and that of cholecysititis was 2.16 ± 0.56 × 10(-3) mm(2) /s (P < 0.0001). Adding DWI to the standard biliary MRI protocol may improve sensitivity for distinguishing GB cancers from benign GB diseases with wall thickening.

Detection of hepatocellular carcinoma in gadoxetic acid-enhanced MRI and diffusion-weighted MRI with respect to the severity of liver cirrhosis

As gadoxetic acid-enhanced magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) have been widely used for the evaluation of hepatocellular carcinoma (HCC), it is clinically relevant to determine the diagnostic efficacy of gadoxetic acid-enhanced MRI and DWI for detection of HCCs with respect to the severity of liver cirrhosis. To compare the diagnostic accuracy and sensitivity of gadoxetic acid-enhanced MRI and DWI for detection of HCCs with respect to the severity of liver cirrhosis. A total of 189 patients with 240 HCCs (≤3.0 cm) (Child-Pugh A, 81 patients with 90 HCCs; Child-Pugh B, 65 patients with 85 HCCs; Child-Pugh C, 43 patients with 65 HCCs) underwent DWI and gadoxetic acid-enhanced MRI at 3.0 T. A gadoxetic acid set (dynamic and hepatobiliary phase plus T2-weighted image) and DWI set (DWI plus unenhanced MRIs) for each Child-Pugh class were analyzed independently by two observers for detecting HCCs using receiver-operating characteristic analysis. The diagnostic accuracy and sensitivity were calculated. There was a trend toward decreased diagnostic accuracy for gadoxetic acid and DWI set with respect to the severity of cirrhosis (Child-Pugh A [mean 0.974, 0.961], B [mean 0.904, 0.863], C [mean 0.779, 0.760]). For both observers, the sensitivities of both image sets were highest in Child-Pugh class A (mean 95.6%, 93.9%), followed by class B (mean 83.0%, 77.1%), and class C (mean 60.6%, 60.0%) (P < 0.05). In HCC detection, the diagnostic accuracy and sensitivity for gadoxetic acid-enhanced MRI and DWI were highest in Child-Pugh class A, followed by Child-Pugh class B, and Child-Pugh class C, indicating a tendency toward decreased diagnostic capability with the severity of cirrhosis.