Apparent Diffusion Coefficient Map Images Research Articles

Multiparametric study for glioma grading with FLAIR, ADC map, eADC map, T1 map, and SWI images

PurposeThis paper is a preliminary attempt to compare the diagnostic efficiency and performance of Fluid-attenuated inversion recovery (FLAIR), apparent diffusion coefficient (ADC) map, exponential ADC (eADC) map, T1 map, and Susceptibility-weighted image (SWI) for glioma grading and combine these image data pairs to compare the diagnostic performance of different image data pairs for glioma grading. Material and methodsFifty-nine patients underwent FLAIR, ADC map, eADC map, Variable flip-angle (VFA) spoiled gradient recalled echo (SPGR) method, and SWI MRI imaging. The T1 map was reconstructed by the VFA-SPGR method. The average Relative Signal Contrast (RSC) and receiver operating characteristic curve (ROC) was calculated in a different image. The multivariate binary logistic regression model combined different image data pairs. ResultsThe average RSC of SWI and ADC maps in high-grade glioma is significantly lower than RSCs in low-grade. The average RSC of the eADC map and T1 maps increased with glioma grade. No significant difference was detected between low and high-grade glioma on FLAIR images. The AUC for low and high-grade glioma differentiation on ADC maps, eADC maps, T1 map, and SWI were calculated 0.781, 0.864, 0.942, and 0.904, respectively. Also, by adding different image data, diagnostic performance was increased. ConclusionInterestingly, the T1 map and SWI image have the potential to use in the clinic for glioma grading purposes due to their high performance. Also, the eADC map+T1 map and T1 map+SWI image weights have the highest diagnostic performance for glioma grading.

Preoperative identification of cytokeratin 19 status of hepatocellular carcinoma based on diffusion kurtosis imaging.

To explore the potential value of diffusion kurtosis imaging (DKI) for identification of cytokeratin 19 (CK19) status of HCCs. This study was approved by the local institute review board and written informed consent was obtained. 73 patients with pathologically confirmed HCCs were included in this prospective study. All the diffusion-weighted (DW) images were acquired using a 3.0-T MR scanner with 4 b-values (0, 800, 1500 and 2000s/mm2). The mean diffusion value (MD) and mean kurtosis coefficient (MK) from DKI, apparent diffusion coefficient (ADC) from DW imaging (b = 0, 500s/mm2), and tumor-to-liver signal intensity ratios on ADC map (SIRADC) and DW images with b-value of 500s/mm2 (SIRb500) were calculated and compared between CK19-positive (n = 23) and CK19-negative (n = 50) HCC groups. Univariate and multivariate logistic regression analyses were used to identify risk factors for the positive expression of CK19. Increased a-fetoprotein level (p = 0.021) and SIRb500 (p = 0.006) and decreased ADC (p = 0.021) and MD (p < 0.001) were significantly correlated with CK19-positive HCCs at univariate analysis. Decreased MD value (odds ratio: 0.042, p = 0.002) and a-fetoprotein level (odds ratio: 5.139, p = 0.015) were the independent risk factors for CK19-positive HCCs at multivariate analysis. The area under the curve of MD value by receiver operating characteristic analysis was 0.823 with a sensitivity of 86.96% and a specificity of 76% for the prediction of CK19-positive HCCs. The decreased MD value derived from DKI is potential quantitative biomarker for predicting CK19-positive HCCs.

Glioma segmentation with DWI weighted images, conventional anatomical images, and post-contrast enhancement magnetic resonance imaging images by U-Net.

Glioma segmentation is believed to be one of the most important stages of treatment management. Recent developments in magnetic resonance imaging (MRI) protocols have led to a renewed interest in using automatic glioma segmentation with different MRI image weights. U-Net is a major area of interest within the field of automatic glioma segmentation. This paper examines the impact of different input MRI image-weight on the U-Net output performance for glioma segmentation. One hundred forty-nine glioma patients were scanned with a 1.5T MRI scanner. The main MRI image-weights acquired are diffusion-weighted imaging (DWI) weighted images (b50, b500, b1000, Apparent diffusion coefficient (ADC) map, Exponential apparent diffusion coefficient (eADC) map), anatomical image-weights (T2, T1, T2-FLAIR), and post enhancement image-weights (T1Gd). The U-Net and data augmentation are used to segment the glioma tumors. Having the Dice coefficient and accuracy enabled us to compare our results with the previous study. The first set of analyses examined the impact of epoch number on the accuracy of U-Net, and n_epoch = 20 was selected for U-Net training. The mean Dice coefficient for b50, b500, b1000, ADC map, eADC map, T2, T1, T2-FLAIR, and T1Gd image weights for glioma segmentation with U-Net were calculated 0.892, 0.872, 0.752, 0.931, 0.944, 0.762, 0.721, 0.896, 0.694 respectively. This study has found that, DWI image-weights have a higher diagnostic value for glioma segmentation with U-Net in comparison with anatomical image-weights and post enhancement image-weights. The results of this investigation show that ADC and eADC maps have higher performance for glioma segmentation with U-Net.

Automated detection and grading of prostate cancer in multiparametric MRI

Prostate cancer is a major health issue worldwide and automatic segmentation of prostate from magnetic resonance imaging (MRI) is crucial task in image guided intervention. The objective of this paper is to develop a transparent and meticulous feature learning framework for prostate cancer detection and grading of prostate cancer using multiparametric magnetic resonance imaging (MPMRI). Prostate cancer is confirmed using approved rules of prostate cancer diagnosis from MPMRI data. The clustering is done in apparent diffusion coefficient (ADC) and diffusion weighted images (DWI) to obtain a probabilistic map which confirms cancerous region. The performance of proposed work is enormously tested on the dataset that contains T2Weighted, DWI and ADC map images of 236 subjects. In this study a total of 218 regions were used for analysis which includes 53 non-cancerous regions and 165 cancerous lesions. We have obtained tumour detection accuracy of 93.2% and AUC of 0.94 by using random forest classifier. The results yield by proposed algorithm is validated by two experienced radiologists.

Machine Learning-Based Integration of Prognostic Magnetic Resonance Imaging Biomarkers for Myometrial Invasion Stratification in Endometrial Cancer.

Estimation of the depth of myometrial invasion (MI) in endometrial cancer is pivotal in the preoperatively staging. Magnetic resonance (MR) reports suffer from human subjectivity. Multiparametric MR imaging radiomics and parameters may improve the diagnostic accuracy. To discriminate between patients with MI ≥ 50% using a machine learning-based model combining texture features and descriptors from preoperatively MR images. Retrospective. One hundred forty-three women with endometrial cancer were included. The series was split into training (n= 107, 46 with MI ≥ 50%) and test (n= 36, 16 with MI ≥ 50%) cohorts. Fast spin echo T2-weighted (T2W), diffusion-weighted (DW), and T1-weighted gradient echo dynamic contrast-enhanced (DCE) sequences were obtained at 1.5 or 3 T magnets. Tumors were manually segmented slice-by-slice. Texture metrics were calculated from T2W and ADC map images. Also, the apparent diffusion coefficient (ADC), wash-in slope, wash-out slope, initial area under the curve at 60 sec and at 90 sec, initial slope, time to peak and peak amplitude maps from DCE sequences were obtained as parameters. MR diagnostic models using single-sequence features and a combination of features and parameters from the three sequences were built to estimate MI using Adaboost methods. The pathological depth of MI was used as gold standard. Area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, precision and recall were computed to assess the Adaboost models performance. The diagnostic model based on the features and parameters combination showed the best performance to depict patient with MI ≥ 50% in the test cohort (accuracy=86.1% and AUROC=87.1%). The rest of diagnostic models showed a worse accuracy (accuracy=41.67%-63.89% and AUROC=41.43%-63.13%). The model combining the texture features from T2W and ADC map images with the semi-quantitative parameters from DW and DCE series allow the preoperative estimation of myometrial invasion. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.

MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study

ObjectivesTo build a machine learning (ML) model to detect extraprostatic extension (EPE) of prostate cancer (PCa), based on radiomics features extracted from prostate MRI index lesions.MethodsConsecutive MRI exams of patients undergoing radical prostatectomy for PCa were retrospectively collected from three institutions. Axial T2-weighted and apparent diffusion coefficient map images were annotated to obtain index lesion volumes of interest for radiomics feature extraction. Data from one institution was used for training, feature selection (using reproducibility, variance and pairwise correlation analyses, and a correlation-based subset evaluator), and tuning of a support vector machine (SVM) algorithm, with stratified 10-fold cross-validation. The model was tested on the two remaining institutions’ data and compared with a baseline reference and expert radiologist assessment of EPE.ResultsIn total, 193 patients were included. From an initial dataset of 2436 features, 2287 were excluded due to either poor stability, low variance, or high collinearity. Among the remaining, 14 features were used to train the ML model, which reached an overall accuracy of 83% in the training set. In the two external test sets, the SVM achieved an accuracy of 79% and 74% respectively, not statistically different from that of the radiologist (81–83%, p = 0.39–1) and outperforming the baseline reference (p = 0.001–0.02).ConclusionsA ML model solely based on radiomics features demonstrated high accuracy for EPE detection and good generalizability in a multicenter setting. Paired to qualitative EPE assessment, this approach could aid radiologists in this challenging task.Key Points• Predicting the presence of EPE in prostate cancer patients is a challenging task for radiologists.• A support vector machine algorithm achieved high diagnostic accuracy for EPE detection, with good generalizability when tested on multiple external datasets.• The performance of the algorithm was not significantly different from that of an experienced radiologist.

Texture analysis in the characterization of parotid salivary gland lesions: A study on MR diffusion weighted imaging

Background and PurposeParotid lesions show overlaps of morphological findings, apparent diffusion coefficient (ADC) values and types of time/intensity curve. This research aimed to evaluate the role of diffusion weighted imaging texture analysis in differentiating between benign and malignant parotid lesions and in characterizing pleomorphic adenoma (PA), Warthin tumor (WT), epithelial malignancy (EM), and lymphoma (LY). MethodsTexture analysis of 54 parotid lesions (19 PA, 14 WT, 14 EM, and 7 LY) was performed on ADC map images. An ANOVA test was used to estimate both the difference between benign and malignant lesions and the texture feature differences among PA, WT, EM, and LY. A P-value≤0.01 was considered to be statistically significant. A cut-off value defined by ROC curve analysis was found for each statistically significant texture parameter. The diagnostic accuracy was obtained for each texture parameter with AUC ≥ 0.5. The agreement between each texture parameter and histology was calculated using the Cohen's kappa coefficient. ResultsThe mean kappa values were 0.61, 0.34, 0.26, 0.17, and 0.48 for LY, EM, WT, PA, and benign vs. malignant lesions respectively. Long zone emphasis cut-off values >1.870 indicated EM with an accuracy of 81 % and values >2.630 revealed LY with an accuracy of 93 %. Long run emphasis values >1.050 and >1.070 indicated EM and LY with a diagnostic accuracy of 79% and 93% respectively. ConclusionsLong zone emphasis and long run emphasis texture parameters allowed the identification of LY and the differentiation between benign and malignant lesions. WT and PA were not accurately recognized.

Automated segmentation of prostate zonal anatomy on T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images using U-Nets.

Accurate regional segmentation of the prostate boundaries on magnetic resonance (MR) images is a fundamental requirement before automated prostate cancer diagnosis can be achieved. In this paper, we describe a novel methodology to segment prostate whole gland (WG), central gland (CG), and peripheral zone (PZ), where PZ+CG=WG, from T2W and apparent diffusion coefficient (ADC) map prostate MR images. We designed two similar models each made up of two U-Nets to delineate the WG, CG, and PZ from T2W and ADC map MR images, separately. The U-Net, which is a modified version of a fully convolutional neural network, includes contracting and expanding paths with convolutional, pooling, and upsampling layers. Pooling and upsampling layers help to capture and localize image features with a high spatial consistency. We used a dataset consisting of 225 patients (combining 153 and 72 patients with and without clinically significant prostate cancer) imaged with multiparametric MRI at 3 Tesla. Our proposed model for prostate zonal segmentation from T2W was trained and tested using 1154 and 1587 slices of 100 and 125 patients, respectively. Median of Dice similarity coefficient (DSC) on test dataset for prostate WG, CG, and PZ were 95.33±7.77%, 93.75±8.91%, and 86.78±3.72%, respectively. Designed model for regional prostate delineation from ADC map images was trained and validated using 812 and 917 slices from 100 and 125 patients. This model yielded a median DSC of 92.09±8.89%, 89.89±10.69%, and 86.1±9.56% for prostate WG, CG, and PZ on test samples, respectively. Further investigation indicated that the proposed algorithm reported high DSC for prostate WG segmentation from both T2W and ADC map MR images irrespective of WG size. In addition, segmentation accuracy in terms of DSC does not significantly vary among patients with or without significant tumors. We describe a method for automated prostate zonal segmentation using T2W and ADC map MR images independent of prostate size and the presence or absence of tumor. Our results are important in terms of clinical perspective as fully automated methods for ADC map images, which are considered as one of the most important sequences for prostate cancer detection in the PZ and CG, have not been reported previously.

The utility of ADC measurement techniques for differentiation of low- and high-grade clear cell RCC.

PurposeTo evaluate the diffusion properties of clear cell renal cell carcinoma (ccRCC) on magnetic resonance imaging (MRI) concerning their Fuhrman nuclear grades and sizes, and to compare the diagnostic performance of two ROI placement techniques for apparent diffusion coefficient (ADC) measurement (entire mass vs. only the darkest region of the mass).Material and methodsFifty-one ccRCC were enrolled in the study and grouped into low-grade ccRCC (Fuhrman grade 1 and 2, n = 37) and high-grade ccRCC (Fuhrman grade 3 and 4, n = 14). Selective ADC (Sel-ADC) measurement was performed by placing a circular ROI that included the darkest region of the tumour on ADC map images. Extensive ADC (Ext-ADC) measurement was performed by drawing an ROI that covered the entire tumour.ResultsThe Sel-ADC value was lower in high-grade ccRCC (p = 0.019), whereas the Ext-ADC value did not show a statistically significant difference (p = 0.42). Sel-ADC value of a ≤ 1.405 mm2/s has a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy value of 78.6, 72.2, 73.87, 77.13, and 75.4, respectively, to differentiate high-grade from low-grade ccRCC. The size and Fuhrman grade of the ccRCC were inversely correlated with the Sel-ADC value; however, the correlations were weak (r = –0.322, p = 0.021 and r = –0.376, p = 0.006, respectively). There was no difference between ADC values of small (≤ 4 cm) and large (> 4 cm) ccRCCs.ConclusionsThe ADC value of the darkest region in solid part of the ccRCC may play a role in predicting the nuclear grade of ccRCC.

Prominent hypointense veins on susceptibility weighted image in the cat brain with acute infarction: DWI, SWI, and PWI

The multiple prominent hypointense veins on susceptibility-weighted imaging (SWI) have been found in the ischemic territory of patients with acute ischemic stroke. Venous side is the unknown area in the hemodynamics of brain infarction. To evaluate the venous aspect in acute brain infarction through an animal study. The acute infarction in cat brains was induced with a bolus infusion of 0.25 mL of triolein through one side of the common carotid artery. The magnetic resonance (MR) images, including diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) map, SW, and perfusion-weighted (PWI) images, were obtained serially at 2 h (n = 17), 1 day (n = 11), and 4 days (n = 4) after triolein infusion. The obtained MR images were evaluated qualitatively and quantitatively. For qualitative assessment, the signal intensity of the serial MR images was evaluated. The presence or absence and the location with serial changes of infarction were identified on DWI and ADC map images. The presence or absence of prominent hypointense veins and the serial changes of cortical veins were also evaluated on SWI. Quantitative assessment was performed by comparing the relative cerebral blood volume (rCBV), cerebral blood flow (rCBF), and mean transit times (MTT) of the lesions with those of the contralateral normal side calculated on PWI. The serial changes of rCBV, rCBF, and MTT ratio were also evaluated. Acute infarction in the first and second medial gyrus of lesion hemisphere was found by qualitative evaluation of DWI and ADC map images. On the serial evaluation of SWI, the cortical veins of cat brain with infarction were obscured at 2 h and then re-appeared at 1 day. The hemorrhage transformation and prominent hypointense veins were seen at 4 days on SWI. The quantitative evaluation revealed increased MTT ratios and decreased rCBV and rCBF ratios on PWIs in the acute infarction of cat brain. The prominent hypointense veins on SWI were seen in the half of the acute infarction at 4 days. The prominent hypointense veins on SWI may have good agreement with the increased MTT ratio.

DW-MRI of liver lesions: Can a single ADC-value represent the entire lesion?

To evaluate whether focal liver lesions (FLLs) exhibit a homogeneous appearance on apparent diffusion coefficient (ADC) maps and whether there is inter-section variation in the calculated ADC values of FLLs (inter-section range). Eighty-eight patients with 128 FLLs (70 benign, 58 malignant) who underwent abdominal magnetic resonance imaging (MRI) including diffusion-weighted (DW)-MRI were included. Two observers evaluated variation of signal intensity of each FLL within each ADC map image (intra-section) and among different ADC map images through the lesion (inter-section). ADC values of each FLL and neighbouring liver parenchyma were measured on all sections. The inter-section range of FLLs was compared with the neighbouring liver parenchyma. Intra-section inhomogeneity was noted in 39.8% (97/244 sections) and 38.9% (95/244) of benign lesions, and 61% (114/187 sections) and 61.5% (115/187) of malignant lesions, by observer 1 and observer 2, respectively. Inter-section inhomogeneity was noted in 25.7% (18/70) and 27.1% (19/70) of benign lesions, and 51.7% (30/58) and 50% (29/58) of malignant lesions, by observer 1 and observer 2, respectively. The inter-section range for both benign (0.28 × 10(-3) mm(2)/s) and malignant (0.25 × 10(-3) mm(2)/s) FLLs were significantly greater than that of liver parenchyma surrounding benign (0.16 × 10(-3) mm(2)/s, p < 0.001) and malignant (0.14 × 10(-3) mm(2)/s, p = 0.01) FLLs. Due to intra-/inter-section variations in ADC values of benign and malignant FLLs, a single ADC value may not reliably represent the entire lesion.

A study on quantitative analyses before and after injection of contrast medium in spine examinations performed by using diffusion weighted image

This study examined the changes in the signal-to-noise ratio (SNR), the contrast-to-noise ratio (CNR) and the apparent diffusion coefficient (ADC) of metastatic cancer in the lumbar region by using diffusion weighted image taken with a 1.5 T (Tesla) magnetic resonance (MR) scanner before and after injecting a contrast medium. The study enrolled 30 healthy people and 30 patients with metastatic spine cancer from patients who underwent a lumbar MRI scan from January 2011 to October 2012. A 1.5 T MR scanner was used to obtain the diffusion weighted images (DWIs) before and after injecting the contrast medium. In the group with metastatic spine cancer, the SNR and the CNR were measured in three parts among the L1–L5 lumbar vertebrae, which included the part with metastatic spine cancer, the area of the spine with spine cancer, and the area of spine under the region with cancer. In the acquired ADC map image, the SNRs and the ADCs of the three parts were measured in ADC map images. Among the healthy subjects, the measurements were conducted for the lumbar regions of L3–L5. According to the results, in the group with metastatic spine cancer, the SNR in the DWI before the contrast medium had been injected was lowest in the part with spine cancer. In the DWI after the contrast medium had been injected, the SNR and the CNR were increased in all three parts. In the ADC map image after the contrast medium had been injected, the SNR decreased in all three parts compared to the SNR before the contrast had been injected. The ADC after had been injected the contrast medium was decreased in all three parts compared to that before the contrast medium had been injected. In the healthy group, the SNR was increased in the L3–L5 lumbar regions in the DWI. In the ADC map image, the SNR in all the three parts was decreased in the DWI after injecting the contrast medium had been injected. The ADC in the ADC map image was also decreased in all three parts.

Correlations between the MR Diffusion-weighted Image (DWI) and the bone mineral density (BMD) as a function of the soft tissue thickness-focus on phantom and patient

In this study we used lumbar phantoms to determine if the BMD (bone mineral density) changes when only the thickness of soft tissue is increased. Second, we targeted osteoporosis patients to analyze the dependences of the changes in the SNR (signal-to-noise ratio) and the ADC (apparent diffusion coefficient) on changes in T-score. We used a bone mineral densitometer, phantoms such as an aluminum spine phantom (ASP), a Hologic spine phantom (HSP), and a European spine phantom (ESP), five sheets of acrylic panel, and a water bath to study the effects of changes in the thickness of soft tissue. First, we measured the ASP, the HSP and the ESP. For the measurement of the ASP, we filled it with water to increase the height by 0.5 cm starting from the baseline height. We then did three measurements for each height. For the measurements of the HSP and the ESP, we placed an acrylic panel on the phantom and then did three measurements at each height. We used the ASP to calculate the degree of precision of the standard mode and the thick mode at the maximum height of the water bath. To assess the degree of precision in the measurements of the three types of phantoms, we calculated precision errors and analyzed the correlation between the change in the thickness of soft tissue and the variables of the BMD. Using DWIs (diffusion weighted images), we targeted 30 healthy persons without osteoporosis and 30 patients with a finding of osteoporosis and measured the T-scores for the L1 — L4 (lumbar spine) segments of by the spine using the dual-energy X-ray absorptiometry (DXA) before classifying the measurement at each part of the spine as osteopenia or osteoporosis. We measured the signal intensity on all four parts of L1-L4 in the DWIs obtained using a 1.5T MR scanner and measured the ADC in the ADC map image. We compared changes in the SNR and the ADC for each group. The study results confirmed that an increase in the thickness of the soft tissue had a significant correlation with the BMD and that the SNR and the ADC decreased as the T-score in the DWI went down.

Initial experience of 3 tesla apparent diffusion coefficient values in differentiating benign and malignant thyroid nodules

To evaluate the role of diffusion-weighted magnetic resonance imaging (DWMRI) in differentiating benign and malignant thyroid nodules using a 3 Tesla (T) MRI scanner. Twenty-eight nodules in 25 patients and 14 healthy control cases were included in the study. DWMRI was acquired with 6 b values with a 3T MRI scanner. The apparent diffusion coefficient (ADC) values of the nodules were calculated from reconstructed ADC map images and were compared with the final histopathological diagnoses. The mean ADC value of the benign nodules was 1548 ± 353.4 (×10(-6) mm(2) /s), and the mean ADC of the malignant nodules was 814 ± 177.12 (×10(-6) mm(2) /s). The normal thyroid tissue had a mean ADC value of 1323.43 ± 210.35 × 10(-6) mm(2) /s (958-1689 × 10(-6) mm(2) /s) in the healthy control group. The ADC values were significantly different among the three groups (P = 0.001). An ADC value of 905 × 10(-6) mm(2) /s was determined to be the cutoff value for differentiating benign and malignant nodules, with 90% (55.5-98.3) sensitivity and 100% (81.3-100.0) specificity. This study suggests that the ADC values of nodules measured with a 3T MRI scanner could help in differentiating benign thyroid nodules from malignant nodules.

MR Imaging of Polypoid Endometriosis of the Ovary

We report a case of polypoid endometriosis of the ovary and correlate magnetic resonance (MR) and pathological findings. The endometriosis appeared as multiple polypoid areas along the wall of the ovarian cystic lesion with hyperintensity on T₂-weighted images and slight hyperintensity on diffusion-weighted images. However, the polypoid areas did not yield low apparent diffusion coefficient (ADC) values on ADC map images. These MR findings were similar to the signal intensity of the uterine endometrium, reflecting the presence of abundant endometrial glands.

Diffusion-Weighted Imaging in the Characterization of Focal Liver Lesions

The objective of the study was to assess the value of visual assessment of signal intensities on b800 diffusion-weighted images and apparent diffusion coefficient (ADC) maps in differentiation of benign and malignant focal liver lesions (FLLs). Approval for this retrospective study was obtained from the institutional review board. One hundred forty-three FLLs in 65 patients (38 women, 27 men; mean age, 50.8 years) underwent magnetic resonance (MR) imaging and diffusion-weighted imaging (DWI) with a respiratory-triggered single-shot echo-planar imaging sequence. Focal liver lesions were evaluated visually according to the signal intensities on b800 and ADC map images, and ADC values were also calculated. The conventional MR imaging, follow-up imaging findings, and histopathologic data were regarded as gold standard. Normal distribution was assessed with Kolmogorov-Smirnov test. The accuracies of visual assessment and ADC values in differentiating benign and malignant FLLs were assessed with the Student t test, and threshold values were determined with receiver operating characteristic curve analysis. By using a cutoff value of 1.21 × 10⁻³ mm²/s, ADC had a sensitivity of 100%, a specificity of 89.3%, and an accuracy of 92.3% in the discrimination of malignant FLLs. With the visual assessment of the DWIs and ADC maps, malignant lesions were differentiated from benign ones, with 100% sensitivity, 92.2% specificity, and 94.4% accuracy. Although some benign lesions were interpreted as malignant, no malignant lesion was determined as benign in visual assessment. Most FLLs are benign ones such as hemangiomas and cysts, which can be readily and practically characterized only by using visual assessment of DWIs without requiring time-consuming conventional and dynamic contrast-enhanced imaging sequences. Some benign lesions that are falsely interpreted as malignant can be further characterized by using conventional and contrast-enhanced MR studies.

Evaluation of the Mean and Entropy of Apparent Diffusion Coefficient Values in Chronic Hepatitis C: Correlation with Pathologic Fibrosis Stage and Inflammatory Activity Grade

To determine whether mean and entropy apparent diffusion coefficient (ADC) values obtained at diffusion-weighted (DW) magnetic resonance (MR) imaging can help detect and stage histopathologic liver fibrosis and grade inflammation activity in patients with chronic hepatitis C. This retrospective study was approved by the institutional review board, and the requirement for informed consent was waived. The study included 55 patients with focal hepatic lesions and either chronic hepatitis C (n = 43) or normal hepatic function (control subjects) (n = 12). Mean and entropy of volume histograms were generated in four cubic regions of interest placed in the right hepatic lobe of ADC map images, which were obtained at echo-planar DW MR imaging (gradient factor b values of 0 and 1000 sec/mm(2)). These two parameters (mean and entropy ADC) were compared by using METAVIR histopathologic liver fibrosis and inflammatory activity scores. Statistical analysis was performed with the Kruskal-Wallis test and receiver operating characteristic curves. The mean ADC decreased with an increase in the fibrosis stage or inflammatory activity grade, and the entropy ADC increased with an increase in the fibrosis stage or inflammatory activity grade (P < .001 for all comparisons, Kruskal-Wallis test). The area under the receiver operating characteristic curve (A(z)) for the mean ADC was statistically significant in the differentiation of fibrosis stage or inflammatory activity grade (A(z), 0.807-0.926; P < .001 for all comparisons). Entropy of ADC was helpful for classifying normal from abnormal fibrosis stage or inflammatory activity grade (A(z) for both parameters, 0.937; P < .001). Assessment of a combination of mean ADC and entropy ADC in patients with chronic hepatitis C is more accurate for predicting pathologic hepatic fibrosis stage and inflammatory activity grade and helpful for detecting early fibrotic or inflammatory activity when compared with assessment of mean ADC alone.

Pediatric abdominal masses: diagnostic accuracy of diffusion weighted MRI

To retrospectively identify apparent diffusion coefficient (ADC) values of pediatric abdominal mass lesions, to determine whether measured ADC of the lesions and signal intensity on diffusion-weighted (DW) images allow discrimination between benign and malignant mass lesions. Approval for this retrospective study was obtained from the institutional review board. Children with abdominal mass lesions, who were examined by DW magnetic resonance imaging (MRI) were included in this study. DW MR images were obtained in the axial plane by using a non breath-hold single-shot spin-echo sequence on a 1.5-T MR scanner. ADCs were calculated for each lesion. ADC values were compared with Mann-Whitney U test. Receiver operating characteristic curve analysis was performed to determine cut-off values for ADC. The results of visual assessment on b800 images and ADC map images were compared with chi-square test. Thirty-one abdominal mass lesions (16 benign, 15 malignant) in 26 patients (15 girls, 11 boys, ranging from 2 days to 17 years with 6.9 years mean) underwent MRI. Benign lesions had significantly higher ADC values than malignant ones (P < .001). The mean ADCs of malignant lesions were 0.84 +/- 1.7x10(-3) mm2/s, while the mean ADCs of the benign ones were 2.28 +/- 1.00x10(-3) mm2/s. With respect to cutoff values of ADC: 1.11x10(-3) mm2/s, sensitivity and negative predictive values were 100%, specificity was 78.6% and positive predictive value was 83.3%. For b800 and ADC map images, there were statistically significant differences on visual assessment. All malignant lesions had variable degrees of high signal intensity whereas eight of the 16 benign ones had low signal intensities on b800 images (P < .001). On ADC map images, all malignant lesions were hypointense and most of the benign ones (n=11, 68.7%) were hyperintense (P < .001). DW imaging can be used for reliable discrimination of benign and malignant pediatric abdominal mass lesions based on considerable differences in the ADC values and signal intensity changes.

Primary central nervous system lymphoma initially mimicking lymphomatosis cerebri: An autopsy case report

A 59-year-old immunocompetent man was admitted to our hospital because of progressive dementia with concomitant bilateral uveitis. The first brain MRI revealed diffuse hyperintense lesions in the cerebral white matter of both hemispheres on a T2-weighted image and fluid-attenuated inversion recovery image. However, another MRI taken more than 1 month later revealed enhanced cohesive mass lesions in the bilateral thalami, in addition to the white matter lesions. The white matter lesions were slightly hyperintense on a diffusion-weighted image and apparent diffusion coefficient map image, suggesting vasogenic edema. One year after the onset of uveitis, he died of respiratory failure. Pathological diagnosis was diffuse large B-cell lymphoma with perivascular proliferation and diffuse scattered infiltration in the cerebrum and brainstem. Microscopically, cohesive mass lesions in the bilateral thalami were a massive cluster of lymphoma cells. This is a case of primary CNS lymphoma (PCNSL) mimicking 'lymphomatosis cerebri (LC)' at first but later exhibiting typical mass lesions, giving rise to the possibility that cases of LC might unmask features of regular lymphomas in their later course more often than believed thus far.

Wallenberg's lateral medullary syndrome: diffusion‐weighted imaging findings

To investigate the efficacy of diffusion-weighted imaging in patients with Wallenberg's lateral medullary syndrome. Thirteen patients with Wallenberg's lateral medullary syndrome were examined with conventional and echoplanar diffusion-weighted magnetic resonance (MR) imaging in a 1.5 T magnetic resonance unit. MR examinations were obtained in the acute or subacute stage of clinical syndrome, and diffusion-weighted imaging (DWI) was considered to be positive for infarction when an increase in signal was seen on b = 1000 s/mm2 images in the posterolateral medullary localization. DWIs were positive in 12 patients in the acute or subacute stages of this clinical syndrome. A false-negative result was obtained in only one patient examined within the first day, 10 h after onset of the symptoms. In the visual evaluation of the DWI, the contrast between normal and infarcted brainstem area was better in the high b-value images than in the apparent diffusion coefficient map images. DWI is a valuable technique for examining patients presenting with the signs and symptoms of Wallenberg's syndrome and high b-value images can provide complementary data to T2-weighted images. However, because most of our case group were in either the acute or subacute stage, true sensitivity of the method in the hyperacute stage of the syndrome remains unclear.