Diffusion-weighted Imaging Images Research Articles

Delayed Lesions on Diffusion-Weighted Imaging in Initially Lesion-Negative Stroke Patients.

Background and Purpose Lesions on diffusion-weighted imaging (DWI) occasionally appear on follow-up magnetic resonance imaging (MRI) among initially DWI-negative but clinically suspicious stroke patients. We established the prevalence of positive conversion in DWI-negative stroke and determined the clinical factors associated with it. Methods This retrospective, observational, single-center study included 5,271 patients hospitalized due to stroke/transient ischemic attack (TIA) in a single university hospital during 2010 to 2017. Patients without initial DWI lesions underwent follow-up DWI imaging as a routine practice. Adjusted hazard ratios (aHRs) for recurrent stroke risk according to positive conversion were determined using Cox proportional hazard regression. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for positive conversion among initially DWI-negative patients were estimated. Results In total, 694 (13.2%) patients (mean±standard deviation age, 62.9±13.7 years; male, 404 [58.2%]) were initially DWI-negative. Among them, 22.5% had positive-conversion on follow-up DWI. Positive conversion was associated with a higher risk of recurrent stroke (aHR, 3.12; 95% CI, 1.56 to 6.26). Early neurologic deterioration (aOR, 15.1; 95% CI, 5.71 to 47.66), atrial fibrillation (aOR, 6.17; 95% CI, 3.23 to 12.01), smoking (aOR, 3.76; 95% CI, 2.19 to 6.63), pre-stroke dependency (aOR, 1.62; 95% CI, 1.15 to 2.27), objective hemiparesis (aOR, 4.39; 95% CI, 1.90 to 10.32), longer symptom duration (aOR, 2.17; 95% CI, 1.57 to 3.08), high cholesterol (aOR, 4.70; 95% CI, 1.78 to 12.77), National Institutes of Health Stroke Scale score (aOR, 1.44; 95% CI, 1.08 to 1.91), and high systolic blood pressure (aOR, 1.01; 95% CI, 1.00 to 1.02) were associated with a higher incidence of lesions with delayed appearance. Regarding the location of lesions on follow-up DWI, 34.6% and 21.2% were in the cortex and brainstem, respectively. Conclusions In DWI-negative stroke/TIA, positive conversion is associated with a higher risk of recurrent stroke. DWI-negative stroke with factors related to positive conversion may require follow-up MRI for a definitive diagnosis.

Intravoxel incoherent motion as a tool to detect early microstructural changes in meningiomas treated with proton therapy.

To assess early microstructural changes of meningiomas treated with proton therapy through quantitative analysis of intravoxel incoherent motion (IVIM) and diffusion-weighted imaging (DWI) parameters. Seventeen subjects with meningiomas that were eligible for proton therapy treatment were retrospectively enrolled. Each subject underwent a magnetic resonance imaging (MRI) including DWI sequences and IVIM assessments at baseline, immediately before the 1st (t0), 10th (t10), 20th (t20), and 30th (t30) treatment fraction and at follow-up. Manual tumor contours were drawn on T2-weighted images by two expert neuroradiologists and then rigidly registered to DWI images. Median values of the apparent diffusion coefficient (ADC), true diffusion (D), pseudo-diffusion (D*), and perfusion fraction (f) were extracted at all timepoints. Statistical analysis was performed using the pairwise Wilcoxon test. Statistically significant differences from baseline to follow-up were found for ADC, D, and D* values, with a progressive increase in ADC and D in conjunction with a progressive decrease in D*. MRI during treatment showed statistically significant differences in D values between t0 and t20 (p= 0.03) and t0 and t30 (p= 0.02), and for ADC values between t0 and t20 (p= 0.04), t10 and t20 (p = 0.02), and t10 and t30 (p= 0.035). Subjects that showed a volume reduction greater than 15% of the baseline tumor size at follow-up showed early D changes, whereas ADC changes were not statistically significant. IVIM appears to be a useful tool for detecting early microstructural changes within meningiomas treated with proton therapy and may potentially be able to predict tumor response.

Deep symmetric three-dimensional convolutional neural networks for identifying acute ischemic stroke via diffusion-weighted images.

Acute ischemic stroke (AIS) results in high morbidity, disability, and mortality. Early and automatic diagnosis of AIS can help clinicians administer the appropriate interventions. To develop a deep symmetric 3D convolutional neural network (DeepSym-3D-CNN) for automated AIS diagnosis via diffusion-weighted imaging (DWI) images. This study includes 190 study subjects (97 AIS and 93 Non-AIS) by collecting both DWI and Apparent Diffusion Coefficient (ADC) images. 3D DWI brain images are split into left and right hemispheres and input into two paths. A map with 125×253×14×12 features is extracted by each path of Inception Modules. After the features computed from two paths are subtracted through L-2 normalization, four multi-scale convolution layers produce the final predation. Three comparative models using DWI images including MedicalNet with transfer learning, Simple DeepSym-3D-CNN (each 3D Inception Module is replaced by a simple 3D-CNN layer), and L-1 DeepSym-3D-CNN (L-2 normalization is replaced by L-1 normalization) are constructed. Moreover, using ADC images and the combination of DWI and ADC images as inputs, the performance of DeepSym-3D-CNN is also investigated. Performance levels of all three models are evaluated by 5-fold cross-validation and the values of area under ROC curve (AUC) are compared by DeLong's test. DeepSym-3D-CNN achieves an accuracy of 0.850 and an AUC of 0.864. DeLong's test of AUC values demonstrates that DeepSym-3D-CNN significantly outperforms other comparative models (p < 0.05). The highlighted regions in the feature maps of DeepSym-3D-CNN spatially match with AIS lesions. Meanwhile, DeepSym-3D-CNN using DWI images presents the significant higher AUC than that either using ADC images or using DWI-ADC images based on DeLong's test (p < 0.05). DeepSym-3D-CNN is a potential method for automatically identifying AIS via DWI images and can be extended to other diseases with asymmetric lesions.

Diffusion Weighted Imaging on a 3 Tesla Magnetic Resonance Scanner as a Diagnostic Tool in Early Detection of Prostate Cancer

Introduction: Multiparametric Magnetic Resonance Imaging (MRI) of prostate including Diffusion Weighted Imaging (DWI) is greatly evolving as a diagnostic tool in prostate cancer. Incidence of prostate cancer in India is on rise with most of the cases been diagnosed in late stages. In this scenario, DWI imaging can pick up the cases at an earlier stage causing a significant impact in the patient management. Aim: To assess the role of DW-MRI as a non invasive initial investigation tool in prostate carcinoma prior to biopsy. Materials and Methods: The study was a prospective observational study conducted during April 2014 to March 2015 in the Department of Radiodiagnosis in collaboration with Department of Urology in a hospital in Kolkata. A total of 34 patients with a clinical suspicion of prostate cancer underwent prostate DWI-MRI by a 3T scanner before Transrectal Ultrasound (TRUS)-guided biopsies. IBM Statistical Package for Social sciences (SPSS) version 17.0 was used for statistical analysis and the sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) were determined. Results: All patients (100%) with biopsy proven adenocarcinoma showed diffusion restriction. A total of 95.8% were PI-RADS 5. False positive results were found in two patients. The sensitivity, specificity, PPV and NPV were 100% (95% CI=85.75% to 100.00%), 80% (95% CI=44.39% to 97.48%), 92.31% (95% CI=74.87% to 99.05%) and 100% (95% CI=63.06% to 100.00%) Conclusion: From the present study, it was noted that sensitivity of DWI is very high (100%). DWI also has a high specificity and positive predictive value. Hence, DWI is one of the most effective adjunct non invasive tools for initial investigation in prostate carcinoma which improves the diagnostic performance and helps in performing targeted biopsies from the suspicious prostatic lesion.

Diffusion-Weighted MRI Imaging and ADC Map of Patellar Chondromalacia

Background: Chondromalacia patellae is damage to the cartilage at the back of the kneecap (patella). Magnetic resonance imaging is a valuable noninvasive technique with a unique ability to image soft tissue and has been used for cartilage evaluation.Objective: This study examines the role of apparent diffusion coefficient (ADC) map and diffusion-weighted imaging (DWI) techniques in diagnosing chondromalacia patella. Materials and Methods: The study consists of 31 participants, 19 patients with chondromalacia patella, and 12 healthy volunteers. The DWI imaging and ADC maps for all patients were recorded. Later the average amount of ADC for a lesion was calculated by drawing regions of interest. Results: The results of DWI imaging in patients with patellae chondromalacia were observed as a nonhomogeneous patella signal and with an increase in the signal intensity at the focal, thickening of cartilage with surface irregularities, reactive changes subchondral, and bone marrow edema. The mean ADC values of the chondromalacia group and the health group were 1.34 × 10–3 mm2/s and 1.07 × 10–3 mm2/s, respectively. Conclusion: Diffusion-weighted imaging can detect early-stage patellar chondromalacia. Besides, it may also help in the detection of cartilage defects

An automatic machine learning approach for ischemic stroke onset time identification based on DWI and FLAIR imaging

Current thrombolysis for acute ischemic stroke (AIS) treatment strictly relies on the time since stroke (TSS) less than 4.5 h. However, some patients are excluded from thrombolytic treatment because of the unknown TSS. The diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) mismatch can simply identify TSS since lesion intensities are not identical at different onset time. In this paper, we propose an automatic machine learning method to classify the TSS less than or more than 4.5 h. First, we develop a cross-modal convolutional neural network to accurately segment the stroke lesions from DWI and FLAIR images. Second, the features are extracted from DWI and FLAIR according to the segmentation regions of interest (ROI). Finally, the features are fed to machine learning models to identify TSS. In DWI and FLAIR ROI segmentation, the networks obtain high Dice coefficients with 0.803 and 0.647. The classification test results show that our model achieves an accuracy of 0.805, with a sensitivity of 0.769 and a specificity of 0.840. Our approach outperforms human reading DWI-FLAIR mismatch model, illustrating the potential for automatic and fast TSS identification.

Efficacy of high-resolution, 3-D diffusion-weighted imaging in the detection of breast cancer compared to dynamic contrast-enhanced magnetic resonance imaging.

PurposeTo evaluate the utility of high-resolution, 3-D diffusion-weighted imaging (DWI) in the detection of breast cancer and to compare the sensitivity, specificity, and area under the curves of DWI and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).Material and methodProspective IEC approved study included 131 breast lesions detected on mammography and breast ultrasound. Cases underwent MRI on a 3 Tesla scanner using a dedicated breast coil. T2WI, STIR, T1WI, and dynamic post contrast MR. DWI MRI with b value of 50, 800, and 1500 s/mm2. Post-processing data with apparent diffusion coefficient (ADC) calculations and kinetic curves were obtained. Characteristics for lesions were analysed as per ACR BI-RADS descriptors. Final histopathological diagnosis was considered as the standard of reference. c2 test, t-test, receiver operating characteristic (ROC) curve analysis, pairwise comparison of ROC curves, sensitivity, specificity, diagnostic accuracy, and area under the curve (AUC) were calculated.ResultsSixty-six (50.38%) malignant and 65 (59.62%) benign lesions were included in the study. The mean ADC of malignant lesions was 0.870 × 10–3 mm2/s and 1.637 × 10–3 mm2/s (p < 0.0001) for benign lesions. Sensitivity and specificity for DWI were 95.45% and 90.76%, respectively, and for DCE-MRI they were 96.97% and 87.69%, respectively. Positive predictive value (PPV) and negative predictive value (NPV) were obtained at 91.30% and 95.16%, respectively, in DWI while in DCE-MRI they were 88.88% and 96.61%, respectively. The AUC for ADC was 0.979. In ROC comparison of AUC for DWI 0.931 and for DCE-MRI 0.923, the difference between the areas was 0.00781 (p = 0.782).ConclusionsHigh-resolution DWI is a non-contrast MRI technique, which improves the lesion detection with diagnostic performance comparable to DCE-MRI and has potential as an adjunct with screening mammography.

Role of diffusion weighted imaging (DWI) in detection and characterization of cerebral stroke

Background: DW MRI is a relatively recent imaging technique in which water self-diffusion is a source of contrast on MR images. 1Because cytotoxic edema occurs in just a few minutes of a critical decrease in cerebral blood flow, DWI may show most acute infarctions. This is particularly useful when thrombolytic therapy, which can only be used within a short window of opportunity, is considered. Also, DWI identifies acute lesions in patients with multiple chronic ischemic lesions. It also identifies the new extension of the previous ischemic lesions. Methodology: 50 patients with clinical signs and symptoms of stroke who underwent conventional MR imaging of brain were randomly considered for this study. These patients were prospectively subjected for DWI along with conventional MRI study. DW images were evaluated for the calculation of ADC values and ADC maps. Result: The age range of patients in this group varied from 5 years to 82 years; majority of patients being in 51 to 70 years age group. 28 patients were male and 22 were female. Of the 50 patients in this group, the MR study was performed in time interval ranging from within 6 hours after the initial symptom onset upto 4 to 7 days time interval between onset of symptoms and study. DWI imaging shows very high sensitivity compared to T2 weighted imaging particularly imaging within first 12 hours of symptoms onset. After 12 hours DWI and T2 weighted imaging show almost equal sensitivity for lesion detection.

Preliminary study of multiple b-value diffusion-weighted images and T1 post enhancement magnetic resonance imaging images fusion with Laplacian Re-decomposition (LRD) medical fusion algorithm for glioma grading

BackgroundGrade of brain tumor is thought to be the most significant and crucial component in treatment management. Recent development in medical imaging techniques have led to the introduce non-invasive methods for brain tumor grading such as different magnetic resonance imaging (MRI) protocols. Combination of different MRI protocols with fusion algorithms for tumor grading is used to increase diagnostic improvement. This paper investigated the efficiency of the Laplacian Re-decomposition (LRD) fusion algorithms for glioma grading. ProceduresIn this study, 69 patients were examined with MRI. The T1 post enhancement (T1Gd) and diffusion-weighted images (DWI) were obtained. To evaluated LRD performance for glioma grading, we compared the parameters of the receiver operating characteristic (ROC) curves. FindingsWe found that the average Relative Signal Contrast (RSC) for high-grade gliomas is greater than RSCs for low-grade gliomas in T1Gd images and all fused images. No significant difference in RSCs of DWI images was observed between low-grade and high-grade gliomas. However, a significant RSCs difference was detected between grade III and IV in the T1Gd, b50, and all fussed images. ConclusionsThis research suggests that T1Gd images are an appropriate imaging protocol for separating low-grade and high-grade gliomas. According to the findings of this study, we may use the LRD fusion algorithm to increase the diagnostic value of T1Gd and DWI picture for grades III and IV glioma distinction. In conclusion, this article has emphasized the significance of the LRD fusion algorithm as a tool for differentiating grade III and IV gliomas.

Qualitative and Quantitative Comparison of Respiratory Triggered Reduced Field-of-View (FOV) Versus Full FOV Diffusion Weighted Imaging (DWI) in Pancreatic Pathologies

To investigate the effects of a reduced field-of-view (rFOV) acquisition in diffusion-weighted magnetic resonance imaging of the pancreas. We enrolled 153 patients who underwent routine clinical MRI work-up including respiratory-triggered diffusion-weighted single-shot echo-planar imaging (DWI) with full field-of-view (fFOV, 3 × 3 × 4 mm3 voxel size) and reduced field-of-view (rFOV, 2.5 × 2.5 × 3 mm3 voxel size) for suspected pancreatic pathology. Two experienced radiologists were asked to subjectively rate (Likert Scale 1-4) image quality (overall image quality, lesion conspicuity, anatomical detail, artifacts). In addition, quantitative image parameters were assessed (apparent diffusion coefficient, apparent signal to noise ratio, apparent contrast to noise ratio [CNR]). All subjective metrics of image quality were rated in favor of rFOV DWI images compared to fFOV DWI images with substantial-to-high inter-rater reliability. Calculated ADC values of normal pancreas, pancreatic pathologies and reference tissues revealed no differences between both sequences. Whereas the apparent signal to noise ratio was higher in fFOV images, apparent CNR was higher in rFOV images. rFOV DWI provides higher image quality and apparent CNR values, favorable in the analysis of pancreatic pathologies.

Quantitative evaluation of the image quality using the fast nonlocal means denoising approach in diffusion-weighted magnetic resonance imaging with high b-value

Brain images acquired using the diffusion-weighted imaging (DWI) method indicate that the diagnostic efficiency of infarction improves and the noise increases as the b-value increases. In this study, we designed a fast nonlocal means (FNLM) noise reduction algorithm and evaluated its effectiveness for de-noising brain images with high b-values. The designed algorithm uses an approach that measures the similarity of local parts in an image, calculates weights based on the result, and uses the principle of reducing processing time using a simplification of the calculation. To demonstrate the effectiveness of the algorithm, we compared the qualities of the images obtained using FNLM with those obtained using previously developed algorithms with noise reduction performance and no-reference image-quality assessment parameters. The results of applying the FNLM noise reduction algorithm to DWI images obtained at high b-values indicated superior quantitative characteristics. In particular, the signal-to-noise ratio, coefficient of variation, and blind/referenceless image spatial quality evaluator (BRISQUE) results using the proposed FNLM algorithm were approximately 1.84, 1.44, and 1.21 times better than those of the noisy image, respectively. In conclusion, our results verified that the FNLM approach achieves higher noise reduction efficiency in diffusion-weighted magnetic resonance imaging.

Evaluation of DWI and ADC Sequences' Diagnostic Values in Benign and Malignant Pulmonary Lesions.

The gold standard for the diagnosis of lung cancer is conducting a histopathologic study. It is also diagnosed based on some features of a computed tomography (CT) scan. Imposed radiation is a prominent side effect of a CT scan. Diffusion-weighted imaging (DWI) apparent diffusion coefficient (ADC) images have currently been used in the diagnosis of different lesions, including those of the brain and breast, and their uses in lung lesions are being evaluated. In this study, to find a safe, sensitive, and specific method, we aimed to assess DWI imaging to replace the CT scan and the positron emission tomography scan. A total of 29 patients were enrolled in the study. In b800 images in DWI, spinal cord and lesion signals were measured, and the lesion-to-cord-signal ratio (LCR) was calculated. The ADC value was measured in a quantitative way. Lesions were also graded qualitatively in b800 DWI sequences. There was a significant difference between malignant and benign lesions in terms of DWI grading in b800 images (p<0.001). There was a significant difference between ADC means of a malignant and benign lesion (p=0.003). The mean LCR for malignant lung lesions was significantly higher than that of the benign ones (p<0.001). Considering Grade 3 as the cutoff in DWI grading results in sensitivity, specificity, and accuracy of 89%, 90%, and 89.6%, respectively. For ADC values, sensitivity, specificity, and accuracy of 79%, 80%, and 79.3%, respectively, were obtained when the cutoff was 1.027×10-3 sec/mm2. The sensitivity of 84%, the specificity of 90%, and the accuracy of 86.2% were calculated for the LCR in a cutoff of 0.983. In this study, all three parameters had an area under the curve of ≥0.8, meaning that these variables were valuable for the differentiation of benign and malignant lesions. Diffusion-weighted magnetic resonance imaging is a noninvasive tool, with no contrast agent and requiring ionizing radiations, which could be used for the qualitative, quantitative, and semiquantitative assessment of pulmonary lesions.

Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Preoperative Diffusion-Weighted MR Using Deep Learning

To investigate the value of diffusion-weighted magnetic resonance imaging for the prediction of microvascular invasion (MVI) of Hepatocellular Carcinoma (HCC) using Convolutional Neural Networks (CNN).This study was approved by the local institutional review board and the patients' informed consent was waived. Consecutive 97 subjects with 100 HCCs from July 2012 to October 2018 with surgical resection were retrieved. All subjects with diffusion-weighted imaging (DWI) examinations were performed with single-shot echo-planar imaging in a breath-hold routine. DWI parameters were three b values of 0,100,600 sec/mm2. First, apparent diffusion coefficients (ADC) images were computed by mono-exponentially fitting the three b-value points. Then, multiple 2D axial patches (28 × 28) of HCCs from b0, b100, b600, and ADC images were extracted to increase the dataset for training the CNN model. Finally, the fusion of deep features derived from three b value images and ADC was conducted based on the CNN model for MVI prediction. The data set was split into the training set (60 HCCs) and the independent test set (40 HCCs). The output probability of the deep learning model in the MVI prediction of HCCs was assessed by the independent student's t-test for data following a normal distribution and Mann-Whitney U test for data violating the normal distribution. Receiver operating characteristic curve and area under the curve (AUC) were also used to assess the performance for MVI prediction of HCCs in the fixed test set.Deep features in b600 images yielded better performance (AUC = 0.74, p = 0.004) for MVI prediction than b0 (AUC = 0.69, p = 0.023) and b100 (AUC = 0.734, p = 0.011). Comparatively, deep features in the ADC map obtained lower performance (AUC = 0.71, p = 0.012) than that of the higher b value images (b600) for MVI prediction. Furthermore, the fusion of deep features from the b0, b100, b600, and ADC images yielded the best results (AUC = 0.79, p = 0.002) for MVI prediction.Fusion of deep features derived from DWI images concerning the three b-value images and the ADC image yields better performance for MVI prediction.

IMG-01. DWI RATIO OF HISTOLOGICAL MOLECULAR SUBTYPES OF PAEDIATRIC MEDULLOBLASTOMAS

AIMTo evaluate if diffusion weighted imaging (DWI) ratio on MRI is able to distinguish between the histological molecular subtypes of paediatric medulloblastomas.MATERIALS AND METHODSFrom 2002 to 2017, 38 cases of medulloblastoma with preoperative MRI available had histological subtyping performed with NanoString nCounter technology. The medulloblastomas were classified into 4 subtypes. There were 3 Sonic Hedgehog (SHH), 9 Wingless (WNT), 12 Group 3 and 14 Group 4 subtypes. Single operator manually outlined solid non-haemorrhagic component of the tumour on DWI images with largest axial tumour cross sectional diameter, correlating with the other MRI images (T1 pre and post contrast, SWI/GRE, FLAIR) to identify areas of haemorrhage. The same operator also drew region of interest to identify normal cerebellar tissue on the same axial images on which the tumour was outlined. All MRI images were obtained from the department’s Radiological Information System Picture Archiving and Communicating System (RIS PACS). DWI ratio for each case was obtained by dividing the values obtained from tumour by normal cerebellar tissue seen on the same axial image.RESULTSDWI ratio of all medullloblastomas is 1.34 +/- 0.18. DWI ratio of SHH subtype is 1.43 +/- 0.07. DWI ratio of WNT subtype is 1.40 +/- 0.07. DWI ratio of Group 3 subtype is 1.31 +/- 0.25. DWI ratio of Group 4 subtype is 1.30 +/- 0.17. There is no significant statistical differences in the DWI ratio between the various subtypes.CONCLUSIONDWI ratio of medulloblastoma is unable to distinguish between the 4 medulloblastoma subtypes.

Visibility of significant prostate cancer on multiparametric magnetic resonance imaging (MRI)\u2014do we still need contrast media?

ObjectivesTo assess the visibility of clinically significant prostate cancer (PCA) lesions on the sequences multiparametric MRI of the prostate (mpMRI) and to evaluate whether the addition of dynamic contrast–enhanced imaging (DCE) improves the overall visibility.MethodsWe retrospectively evaluated multiparametric MRI images of 119 lesions in 111 patients with biopsy-proven clinically significant PCA. Three readers assigned visual grading scores for visibility on each sequence, and a visual grading characteristic analysis was performed. Linear regression was used to explore which factors contributed to visibility in individual sequences.ResultsThe visibility of lesions was significantly better with mpMRI when compared to biparametric MRI in visual grading characteristic (VGC) analysis, with an AUCVGC of 0.62 (95% CI 0.55–0.69; p < 0.001). This benefit was seen across all readers. Multivariable linear regression revealed that a location in the peripheral zone was associated with better visibility on T2-weighted imaging (T2w). A higher Prostate Imaging-Reporting and Data System (PI-RADS) score was associated with better visibility on both diffusion-weighted imaging (DWI) and DCE. Increased lesion size was associated with better visibility on all sequences.ConclusionsVisibility of clinically significant PCA is improved by using mpMRI. DCE and DWI images independently improve lesion visibility compared to T2w images alone. Further research into the potential of DCE to impact on clinical decision-making is suggested.Key Points• DCE and DWI images independently improve clinically significant prostate cancer lesion visibility compared to T2w images alone.• Multiparametric MRI (DCE, DWI, T2w) achieved significantly higher visibility scores than biparametric MRI (DWI, T2w).• Location in the transition zone is associated with poor visibility on T2w, while it did not affect visibility on DWI or DCE.

Development and validation of radiomics model built by incorporating machine learning for identifying liver fibrosis and early-stage cirrhosis.

Background:Liver fibrosis (LF) continues to develop and eventually progresses to cirrhosis. However, LF and early-stage cirrhosis (ESC) can be reversed in some cases, while advanced cirrhosis is almost impossible to cure. Advances in quantitative imaging techniques have made it possible to replace the gold standard biopsy method with non-invasive imaging, such as radiomics. Therefore, the purpose of this study is to develop a radiomics model to identify LF and ESC.Methods:Patients with LF (n = 108) and ESC (n = 116) were enrolled in this study. As a control, patients with healthy livers were involved in the study (n = 145). Diffusion-weighted imaging (DWI) data sets with three b-values (0, 400, and 800 s/mm2) of enrolled cases were collected in this study. Then, radiomics features were extracted from manually delineated volumes of interest. Two modeling strategies were performed after univariate analysis and feature selection. Finally, an optimal model was determined by the receiver operating characteristic area under the curve (AUC).Results:The optimal models were built in plan 1. For model 1 in plan 1, the AUCs of the training and validation cohorts were 0.973 (95% confidence interval [CI] 0.946–1.000) and 0.948 (95% CI 0.903–0.993), respectively. For model 2 in plan 1, the AUCs of the training and validation cohorts were 0.944, 95% CI 0.905 to 0.983, and 0.968, 95% CI 0.940 to 0.996, respectively.Conclusions:Radiomics analysis of DWI images allows for accurate identification of LF and ESC, and the non-invasive biomarkers extracted from the functional DWI images can serve as a better alternative to biopsy.

The value of bi-exponential and non-Gaussian distribution diffusion-weighted imaging in the differentiation of recurrent soft tissue neoplasms and post-surgical changes.

BackgroundMany researches focused on the quantitative mono-exponential diffusion-weighted imaging (DWI) in the assessment of soft tissue neoplasms (STN), but few focused on the value of bi-exponential and non-Gaussian DWI in the application of Recurrent Soft Tissue Neoplasms (RSTN). This study aimed to explore the feasibility of bi-exponential decay and non-Gaussian distribution DWI in the differentiation of RSTN and Post-Surgery Changes (PSC), and compared with mono-exponential DWI.MethodsThe clinical, mono-exponential, bi-exponential [intravoxel incoherent motion (IVIM)] and non-Gaussian [diffusion kurtosis imaging (DKI)] DWI imaging of a cohort of 27 patients [15 RSTN (22 masses), and 12 PSC (12 lesions)] with 34 masses, from Nov 01 2017 to Sep 30 2018, were reviewed. The differences of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), mean diffusivity (MD), and mean kurtosis (MK) values were compared between RSTN and PSC groups. The mono-, bi-exponential, and non-Gaussian distribution based predictive models for RSTN and PSC were built and compared. ROC curves were generated and compared by the DeLong test.ResultsIntra-class correlation coefficient (ICC) of all IVIM/DKI parameters was high (≥0.841). There were significant differences in ADC, D, f, MD, and MK values between RSTN and PSC, but no difference in D* value. The ADC_IVIM, D, f and MD values of RSTN were lower than those of PSC, but with higher MK value. The ADC_IVIM and D values did better than f value in differentiating these two groups (P<0.05). While there was no significant difference in AUCs among ADC_DKI, MD, and MK values. Also, no significant difference was detected in AUCs between bi-exponential and mono-exponential (P=0.38), or between mono-exponential and non-Gaussian distribution based prediction models (P=0.09).ConclusionsADC, D, f, MD, and MK values can be used in the differentiation of RSTN and PSC.

Neuronal intranuclear inclusion disease with mental abnormality: a case report

BackgroundNeuronal intranuclear inclusion disease (NIID) is a chronic progressive neurodegenerative disease that is characterized by the discovery of eosinophilic hyaline intranuclear inclusions in the central and peripheral nervous systems and visceral organs. In this paper, we report a case of an adult-onset neuronal intranuclear inclusion disease presenting with mental abnormality in China.Case presentationA 62-year-old woman presented with mental abnormality and forgetfulness for 3 months before she was admitted to our hospital. There were prodromal symptoms of fever before she had the mental disorder. Encephalitis was first suspected, and the patient underwent lumbar puncture and brain magnetic resonance imaging (MRI). A cerebrospinal fluid (CSF) examination indicated normal pressure, a normal white blood cell count, and slightly elevated protein and glucose levels. Coxsackie B virus, enterovirus, and cytomegalovirus tests showed normal results. Bacterial culture and Cryptococcus neoformans test results were negative. The contrast-enhanced MRI of the brain was normal. The brain diffusion-weighted imaging (DWI) showed a symmetrically distributed strip-shaped hyperintensity signal of the corticomedullary junction in the bilateral frontal, parietal, and temporal lobes. We considered the diagnosis of the NIID, and therefore, skin biopsy was performed. The electron microscopy revealed that intranuclear inclusions in the nucleus of fibrocytes existed and were composed of filaments.ConclusionsNIID is a rare neurodegenerative disease with diverse clinical manifestations. In clinical work, when a patient presents with abnormal mental behavior and exhibits hyperintensity signals on DWI images of the corticomedullary junction, it is crucial to consider the diagnosis of NIID.

High resolution diffusion-weighted imaging with readout segmentation of long variable echo-trains for determining myometrial invasion in endometrial carcinoma

BackgroundWe assessed the image quality of endometrial cancer lesions by readout segmentation of long variable echo-trains (RESOLVE) diffusion-weighted imaging (DWI) compared with that by single-shot echo-planar imaging (SS-EPI) DWI, aimed to explore the value of RESOLVE DWI for determining myometrial invasion and clinical stage in endometrial cancer.Materials and methodsFrom April 2017 to March 2018, a total of 30 endometrial cancer patients (mean age 52.8 ± 9.0 years), who had undergone RESOLVE DWI and SS-EPI DWI, were included in the study. The image quality of endometrial carcinoma by two kinds of DWI scanning methods was compared qualitatively and quantitatively. The Spearman rank correlation test was used to assess the correlation of qualitative image quality scores between two readers. The accuracy of two DWI methods in detecting myometrial invasion and staging of endometrial carcinoma was calculated according to postoperative pathological results. The indexes were analyzed including sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV).ResultsThe qualitative score of RESOLVE DWI group was superior to SS-EPI DWI group in every aspect of five aspects (all P < 0.001). Interobserver agreement of depiction was good or excellent in two DWI sequences. Signal to noise ratio and contrast to noise ratio values in RESOLVE DWI group were both higher than those in SS-EPI DWI group (P<0.001). No statistical difference of apparent diffusion coefficient value was observed between two DWI groups (P = 0.261). The specificity, accuracy, PPV, and NPV of estimating myometrial invasion by RESOLVE DWI in three cases (intramucosal lesion, <50% superficial invasion and ≥ 50% deep invasion) were all higher than those by SS-EPI DWI for endometrial carcinoma. Especially RESOLVE DWI was valuable in judging <50% superficial invasion (95%CI:0.586, 0.970). No significant difference in accuracy staging was between the two DWI groups (P = 0.125).ConclusionRESOLVE DWI can provide higher quality images of endometrial carcinoma than SS-EPI DWI. The high-quality images are helpful for precise assessment of myometrial invasion in endometrial cancer.

Diffusion MRI with quantification of ADC value in characterization of benign and malignant hepatic lesions and their correlation with cyto-histopathology

Diffusion weighted Imaging (DWI) is a useful noninvasive tool in MRI as it can be performed quickly and does not require contrast injection. In addition to it, DWI imaging and apparent diffusion coefficient (ADC) quantification not only can add additional anatomical data about the lesion but can also help in characterization of focal liver lesions into malignant or benign. The study attempts to establish a cut off value of ADC differentiating a benign from malignant lesion.32 patients with age group of 40-85 years with 46 diagnosed focal liver lesions on CT and MRI were included in the study. MRI was performed using 1.5 Tesla GE Healthcare HDxT machine. Conventional sequences followed by diffusion weighted sequences were acquired. Quantitative analysis was derived from ADC maps with calculation of ADC values. ADC values of the hepatic lesions were compared with histopathology as reference standard and analyzed statistically. In this study, 25 focal lesions in 18 patients had histopathological diagnosis of malignant pathology and had mean ADC value 1.13(x10mm/s) and 21 lesions in 14 patients with histopathological diagnosis of benign pathology had ADC value of 1.63(x10mm/s). Statistically significant difference between ADC value of benign and malignant lesions was found.: The study proclaimed that DWI with ADC quantification be used as an additional non invasive MRI tool to differentiate benign and malignant hepatic lesions with a sensitivity of 85.7%, specificity of 88%, PPV of 88% and NPV of 85.7%