Conventional Diffusion-weighted Imaging Research Articles

Clinical comparison of single-shot EPI, readout-segmented EPI and TGSE-BLADE for diffusion-weighted imaging of cerebellopontine angle tumors on 3 tesla

ObjectiveThe complex anatomical structures of cerebellopontine angle (CPA) pose a unique challenge to diffusion weighted imaging (DWI). This study aimed to compare the clinical utility of the prototypic 2D turbo gradient- and spin echo-BLADE-DWI (TGSE-BLADE-DWI) with that of readout-segmented echo-planar DWI (RESOLVE-DWI) and single-shot echo-planar DWI (SS-EPI-DWI) to visualize CPA anatomic structures and identify CPA tumors. MethodsA total of 8 volunteers and 36 patients with pathological CPA tumors were enrolled to perform the three DWI sequences at 3 T. Scan time of TGSE-BLADE-DWI, RESOLVE-DWI and SS-EPI-DWI was 5 min 51 s, 5 min 15 s and 1 min 22 s, respectively. Subjective analysis, including visualization of anatomical structures, geometric distortion, ghosting artifacts, lesion conspicuity, diagnostic confidence, and overall image quality of the three DWI sequences were scored and assessed. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) of CPA tumors were measured and compared. ResultsA total of 39 lesions were identified, TGSE-BLADE-DWI detected all of them, RESOLVE-DWI 36 and SS-EPI-DWI 27. Significant differences were found in all the subjective parameters among the three DWI sequences (all p < 0.001). TGSE-BLADE-DWI was significantly better than RESOLVE-DWI in visualization of CPA anatomical structures, geometric distortion, ghosting artifacts, lesion conspicuity, diagnostic confidence, and overall image quality (all p < 0.01), and RESOLVE-DWI showed significantly superior performance than SS-EPI-DWI in all parameters (all p < 0.001). CNRs and ADCs were not significantly different among the three DWI sequences (p = 0.355, p = 0.590, respectively). No significant differences were detected between TGSE-BLADE-DWI SNR and RESOLVE-DWI SNR (p = 0.058), or TGSE-BLADE-DWI SNR and SS-EPI-DWI SNR (p = 0.155). ConclusionCompared with RESOLVE-DWI and SS-EPI-DWI, TGSE-BLADE-DWI minimized geometric distortions and ghosting artifacts and demonstrated an improved ability for depicting CPA tumors with better lesion conspicuity. SummaryGeometric distortions and ghosting artifacts are found at bone-air interfaces using conventional diffusion-weighted imaging (DWI), which is a challenge for imaging cerebellopontine angle (CPA) tumors. Our study validated that geometric distortions and ghosting artifacts were not present on 2D turbo gradient- and spin-echo-BLADE-DWI scans, making this technique useful for visualizing CPA anatomic structures and diagnosing CPA tumors.

Radiomics of diffusion-weighted MRI compared to conventional measurement of apparent diffusion-coefficient for differentiation between benign and malignant soft tissue tumors

Diffusion-weighted imaging (DWI) is proven useful to differentiate benign and malignant soft tissue tumors (STTs). Radiomics utilizing a vast array of extracted imaging features has a potential to uncover disease characteristics. We aim to assess radiomics using DWI can outperform the conventional DWI for STT differentiation. In 151 patients with 80 benign and 71 malignant tumors, ADCmean and ADCmin were measured on solid portion within the mass by two different readers. For radiomics approach, tumors were segmented and 100 original radiomic features were extracted on ADC map. Eight radiomics models were built with training set (n = 105), using combinations of 2 different algorithms—multivariate logistic regression (MLR) and random forest (RF)—and 4 different inputs: radiomics features (R), R + ADCmin (I), R + ADCmean (E), R + ADCmin and ADCmean (A). All models were validated with test set (n = 46), and AUCs of ADCmean, ADCmin, MLR-R, RF-R, MLR-I, RF-I, MLR-E, RF-E, MLR-A and RF-A models were 0.729, 0.753 0.698, 0.700, 0.773, 0.807, 0.762, 0.744, 0.773 and 0.807, respectively, without statistically significant difference. In conclusion, radiomics approach did not add diagnostic value to conventional ADC measurement for differentiating benign and malignant STTs.

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

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

Comparison of conventional magnetic resonance imaging and diffusion-weighted imaging in the differentiation of bone plasmacytoma from bone metastasis in the extremities

PurposeTo compare conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) in the differentiation of bone plasmacytoma from bone metastasis in the extremities. Materials and methodsA total of 65 patients with 27 bone plasmacytomas (11 men; mean age, 63.6±8.2 [SD] years) and 38 patients with bone metastases (20 men; mean age, 64.1±11.5 [SD] years) were retrospectively included. Plasmacytomas and metastases were compared for size, peritumoral edema, signal intensity (SI), SI pattern, apparent diffusion coefficient (ADC) values and standard deviation (SD) of ADC. Receiver operating characteristic analysis with area under the curve (AUC) was used to calculate sensitivity, specificity, and accuracy of MRI and DWI for the diagnosis of plasmacytoma according to a defined cut-off value. ResultsOn conventional MRI, plasmacytomas showed less peritumoral edema (22% vs. 71%; P<0.001), were more often hyperintense on T1-weighted image (48% vs. 18%; P=0.022) and more homogeneous on T2-weighted image (78% vs. 26%; P<0.001) and contrast-enhanced T1-weighted images (70% vs. 25%; P=0.001) than bone metastases. Mean ADC value and SD of ADC were significantly lower in bone plasmacytomas (760.1±196.9 [SD] μm2/s and 161.5±62.7 [SD], respectively) than in bone metastases (1214.2±382.6 [SD] μm2/s and 277.0±110.3 [SD], respectively) (P<0.001). Using an ADC value≤908.3μm2/s, DWI yielded 88% sensitivity and 78% specificity for the diagnosis of plasmacytoma. ADC value yielded best area under the curve (AUC=0.913), followed by SD of ADC (AUC=0.814) and homogeneity on T2-weighted images (AUC=0.757). The combination of conventional MRI and DWI (AUC=0.894) showed improved diagnostic performance over conventional MRI alone (AUC= 0.843) for discriminating between plasmacytoma and metastasis. ConclusionConventional MRI in combination with DWI can be useful to discriminate between bone plasmacytoma and bone metastasis in the extremities.

Mapping prostatic microscopic anisotropy using linear and spherical b-tensor encoding: A preliminary study.

Purpose:Tensor-valued diffusion encoding provides more specific information than conventional diffusion-weighted imaging (DWI), but has mainly been applied in neuroimaging studies. This study aimed to assess its potential for the imaging of prostate cancer (PCa).Methods:Seventeen patients with histologically proven PCa were enrolled. DWI of the prostate was performed with linear and spherical tensor encoding using a maximal b-value of 1.5 ms/μm2 and a voxel size of 3 × 3 × 4 mm3. The gamma-distribution model was used to estimate the mean diffusivity (MD), the isotropic kurtosis (MKI), and the anisotropic kurtosis (MKA). Regions of interest were placed in MR-defined cancerous tissues, as well as in apparently healthy tissues in the peripheral and transitional zones (PZs and TZs).Results:DWI with linear and spherical encoding yielded different image contrasts at high b-values, which enabled the estimation of MKA and MKI. Compared with healthy tissue (PZs and TZs combined) the cancers displayed a significantly lower MD (P < .05), higher MKI (P < 10−5), and lower MKA (P < .05). Compared with the TZ, tissue in the PZ showed lower MD (P < 10−3) and higher MKA (P < 10−3). No significant differences were found between cancers of different Gleason scores, possibly because of the limited sample size.Conclusion:Tensor-valued diffusion encoding enabled mapping of MKA and MKI in the prostate. The elevated MKI in PCa compared with normal tissues suggests an elevated heterogeneity in the cancers. Increased in-plane resolution could improve tumor delineation in future studies.

Simultaneous multi-slice readout-segmentation of long variable echo-trains for accelerated diffusion-weighted imaging of nasopharyngeal carcinoma: A feasibility and optimization study

ObjectivesWe aimed to evaluate the feasibility of simultaneous multi-slice readout segmentation of long variable echo-trains (SMS-RESOLVE) for diffusion-weighted imaging (DWI) of nasopharyngeal carcinoma (NPC) and determine the optimal acceleration factor of SMS-RESOLVE DWI compared with conventional RESOLVE DWI. Materials and methodsOne hundred and five patients with histologically confirmed NPC were enrolled. DWIs including conventional RESOLVE DWI, SMS-RESOLVE DWI with acceleration factors of 2 and 3 (2 × SMS-RESOLVE and 3 × SMS-RESOLVE) were acquired at b-values of 0 and 800 s/mm2. The subjective image quality, including overall image quality, lesion conspicuity, and lesion distortion scores, and objective image quality, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and contrast in these three protocols, were assessed and compared. The apparent diffusion coefficients (ADCs) of the primary tumor and right masseter muscle and normalized ADCs in the three protocols were measured and compared. ResultsThe 2 × SMS-RESOLVE provided comparable image quality and ADCs with a 48.3% reduction in scan time compared with conventional RESOLVE DWI. The 3 × SMS-RESOLVE showed significantly poorer image quality and the highest ADCs with a 56.9% reduction in scan times compared with conventional RESOLVE DWI. ConclusionThe SMS-RESOLVE DWI with an acceleration factor of 2 yielded the best compromise between image quality and scan time reduction and could be an alternative to conventional RESOLVE DWI for nasopharyngeal carcinoma.

The diagnostic utility of diffusion weighted MRI imaging and ADC ratio to distinguish benign from malignant renal masses: sorting the kittens from the tigers

BackgroundMRI is playing an increasing role in risk stratification and non-invasive diagnosis of the undifferentiated small renal mass. This study was designed to assess the reliability of MRI in diagnostic evaluation of renal masses, specifically characterising lesions with diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) values.MethodsThis is a retrospective analysis of patients undergoing MRI as part of their clinical workup for a renal mass suspicious for renal cell carcinoma (RCC) on CT or ultrasound followed by biopsy and/or surgical excision. All cases were conducted on 3 Tesla MRI, with conventional breath-held sequences, DWI and dynamic contrast enhanced phases. Tumour regions of interest were evaluated on ADC maps and compared with T2 weighted and post-contrast images.ResultsOf the 66 renal tumours included, 33 (50.0%) were Clear Cell RCC, 11 (16.7%) were Oncocytoma, nine (13.6%) were Angiomyolipoma (AML), nine (13.6%) were Papillary RCC and four (6.1%) were Chromophobe RCC. Oncocytoma had the largest ADC values, significantly larger than AMLs and all RCC subtypes (p < 0.001). The average ADC value was also significantly larger in Clear Cell RCCs compared to AMLs, and other RCC subtypes (p < 0.001).ConclusionsMRI with DWI/ADC imaging may aid the differentiation of oncocytomas from RCCs and stratify RCC subtypes, Further studies are required to validate these findings.Trial registration: Not applicable/retrospective study.

Application of magnetic resonance fingerprinting to differentiate grade I transitional and fibrous meningiomas from meningothelial meningiomas

The choice of surgical treatment for meningiomas is affected by the subtype and clinical characteristics. Therefore, an accurate preoperative diagnosis is essential. Current magnetic resonance imaging (MRI) technology is unable to distinguish between meningioma subtypes. In the present study, we compared and evaluated the utility of conventional MRI, magnetic resonance fingerprinting (MRF), and diffusion-weighted imaging (DWI) in differentiating World Health Organization grade I transitional and fibrous meningiomas from meningothelial meningiomas. Forty-six patients with pathologically confirmed meningiomas (15 meningothelial, 18 transitional, and 13 fibrous) were enrolled in the present study. All patients underwent conventional MRI, MRF, and DWI scans before surgery using a 3T scanner. The Jonckheere-Terpstra test was used to analyze differences in the signal and enhancement characteristics of the three groups from T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI). To investigate the difference in quantitative T1 and T2 values derived from MRF and apparent diffusion coefficient (ADC) values between the three groups using the Kruskal-Wallis test, regions of interest (ROIs) were manually drawn on the parenchymal portion of the tumors; P<0.017 was considered statistically significant after Bonferroni correction for multiple comparison. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performances of the different parameters. Meningothelial meningiomas had significantly higher T1 and T2 values than transitional and fibrous meningiomas (all P<0.017). ROC analysis results revealed that the combination of T1 and T2 values had the largest area under the curve (AUC). The AUC for the combination of T1 and T2 values was 0.826 between meningothelial and transitional meningiomas, and the AUC for the combination of T1 and T2 values between meningothelial and fibrous meningiomas was 0.903. No significant differences were found in the T1 and T2 values between transitional and fibrous meningiomas. There were also no statistically significant differences in the conventional MRI (including T1WI, T2WI, and contrast-enhanced T1WI) and ADC values between the three meningioma subtypes (all P>0.05). MRF may provide more quantitative information than either conventional MRI or DWI for differentiating transitional and fibrous meningiomas from meningothelial meningiomas. T1 and T2 values derived from MRF may distinguish transitional and fibrous meningiomas from meningothelial meningiomas, and the combination of T1 and T2 values provides the highest diagnostic efficacy.

Comparison of reduced field-of-view diffusion-weighted imaging (DWI) and conventional DWI techniques in the assessment of Cervical carcinoma at 3.0T: Image quality and FIGO staging

ObjectiveTo evaluate imaging quality (IQ) and International Federation of Gynecology and Obstetrics (FIGO) staging of reduced field-of-view (r-FOV) diffusion-weighted imaging (DWI) in cervical carcinoma (CC). Materials and methodsSixty patients with pathologically proven CC who underwent both pre-treatment r-FOV DWI and full field-of-view (f-FOV) DWI on a 3.0T MRI scanner were retrospectively reviewed. The subjective qualitative image scores were compared using the Wilcoxon signed-rank test. Objective quality values and apparent diffusion coefficient (ADC) were estimated by paired t-test or Wilcoxon signed-rank test for the two DWI sequences according to Normality test. Spearman rank correlation analysis was used to evaluate the relationship between pathological results and mean ADC value. ResultsThe subjective IQ scores for r-FOV DWI were significantly higher than those for f-FOV DWI (P < 0.001). Similarly, the contrast-to-noise (CNR) value of r-FOV DWI was superior to that of f-FOV DWI (10.30 ± 3.676, 8.91 ± 3.008, P = 0.021). However, the signal-to-noise ratio (SNR) value of r-FOV DWI was considerably lower than that of f-FOV DWI (27.80 ± 6.056, 33.67 ± 7.833, P＜0.001). No significant difference was found between mean ADC values of f-FOV DWI and r-FOV DWI. There was a significant tendency for a negative correlation between the ADC values and FIGO stages of CC for both two sequences (r=−0. 436, P＜0.01; r=−0.470, P＜0.01, respectively). ConclusionsThe rFOV DWI sequence provided significantly better IQ and lesion conspicuity than the fFOV DWI sequence. In addition, rFOV sequences can be used in evaluation of FIGO staging of cervical cancer.

Prediction of muscle invasion of bladder cancer: A comparison between DKI and conventional DWI

ObjectivesTo prospectively compare the diagnostic efficacy of conventional diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in differentiating between muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC). MethodsMultiple b value DWIs were performed using a 3-T magnetic resonance (MR) imaging unit in fifty-one patients with bladder cancer including MIBC and NMIBC confirmed by histopathological findings. DWI data were postprocessed using mono-exponential and DKI models to calculate the apparent diffusion coefficient (ADC), apparent diffusional kurtosis (Kapp), and kurtosis-corrected diffusion coefficient (Dapp). Receiver-operating characteristic (ROC) analysis was performed to compare the diagnostic efficacy of all diffusion parameters. ResultsAll parameters differed significantly between MIBC and NIMBC including increased Kapp, decreased Dapp and ADC (all p < 0.001). Only the combination of Dapp and Kapp was significantly higher than ADC (p < 0.05), whilst Dapp and Kapp were not statistically different from ADC. ConclusionsBoth conventional DWI and DKI models are beneficial in differentiating between MIBC and NMIBC, whilst the combination of Dapp and Kapp can produce a more robust value than conventional ADC value in evaluating aggressiveness of bladder cancer.

A Noninvasive Assessment of Tumor Proliferation in Lung cancer Patients using Intravoxel Incoherent Motion Magnetic Resonance Imaging

Ki-67 is a nuclear antigen widely used in routine pathologic analyses as a tumor cell proliferation marker for lung cancer. However, Ki-67 expression analyses using immunohistochemistry (IHC) are invasive and frequently influenced by tissue sampling quality. In this study, we assessed the feasibility of noninvasive magnetic resonance imaging (MRI) in predicting the Ki-67 labeling indices (LIs). A total of 51 lung cancer patients, including 42 non-small cell lung cancer (NSCLC) cases and nine small cell lung cancer (SCLC) cases, were enrolled in this study. Quantitative MRI parameters from conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) were obtained, and their correlations with tumor tissue Ki-67 expression were analyzed. We found that the true diffusion coefficient (D value) from IVIM was negatively correlated with Ki-67 expression (Spearman r = -0.76, P < 0.001). The D values in the high Ki-67 group were significantly lower than those in the low Ki-67 group (0.90 ± 0.21 × 10-3 mm2/s vs. 1.22 ± 0.30 × 10-3 mm2/s). Among three MRI techniques used, D values from IVIM showed the best performance for distinguishing the high Ki-67 group from low Ki-67 group in receiver operating characteristic (ROC) analysis with an area under the ROC curve (AUROC) of 0.85 (95% CI: 0.73-0.97, P < 0.05). Moreover, D values performed well for differentiating SCLC from NSCLC with an AUROC of 0.82 (95% CI: 0.68-0.90), Youden index of 0.72, and F1 score of 0.81. In conclusion, D values were negatively correlated with Ki-67 expression in lung cancer tissues and can be used to distinguish high from low proliferation statuses, as well as SCLC from NSCLC.

Role of Quantitative Diffusion-Weighted Imaging in Differentiating Benign and Malignant Orbital Masses.

Aim To determine the role of diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) values in differentiating benign and malignant orbital masses. Materials and Methods After obtaining institutional ethical board approval and informed consent from all patients, an observational study was done for a period of 24 months in the radiology department of a tertiary care hospital in South India. Conventional magnetic resonance imaging and DWI using a 3T scanner was done for all patients with suspected orbital mass lesion. ADC value and clinicohistopathological correlation were studied for every patient. Chi-square test was used to compare the signal characteristics of DWI and ADC maps between benign and malignant lesions. A comparison of mean ADC values for benign and malignant masses was performed using Student’s t -test for independent samples. The cut-off value for ADC was obtained using the receiver operating characteristic (ROC) curve. Results Of 44 patients with orbital lesions, 70% were benign and 30% were malignant. There was a significant difference in the mean ADC values of benign and malignant orbital masses. Using ROC curve analysis, an optimal ADC threshold of 1.26 × 10 −3 mm 2 /s was calculated for the prediction of malignancy with 100% sensitivity, 80.65% specificity, and 86.36% accuracy (95% confidence interval: 0.872, 1.00, p < 0.0001). Two ADC thresholds were used to characterize the orbital masses with more than 90% confidence. Conclusion Quantitative assessment of ADC is a useful noninvasive diagnostic tool for differentiating benign and malignant orbital masses. Malignant orbital lesions demonstrate significantly lower ADC values as compared with benign lesions.

Diffusion-weighted double-echo steady-state with a three-dimensional cones trajectory for non-contrast-enhanced breast MRI.

The image quality limitations of echo-planar diffusion-weighted imaging (DWI) are an obstacle to its widespread adoption in the breast. Steady-state DWI is an alternative DWI method with more robust image quality but its contrast for imaging breast cancer is not well-understood. The aim of this study was to develop and evaluate diffusion-weighted double-echo steady-state imaging with a three-dimensional cones trajectory (DW-DESS-Cones) as an alternative to conventional DWI for non-contrast-enhanced MRI in the breast. This prospective study included 28 women undergoing clinically indicated breast MRI and six asymptomatic volunteers. In vivo studies were performed at 3 T and included DW-DESS-Cones, DW-DESS-Cartesian, DWI, and CE-MRI acquisitions. Phantom experiments (diffusion phantom, High Precision Devices) and simulations were performed to establish framework for contrast of DW-DESS-Cones in comparison to DWI in the breast. Motion artifacts of DW-DESS-Cones were measured with artifact-to-noise ratio in volunteers and patients. Lesion-to-fibroglandular tissue signal ratios were measured, lesions were categorized as hyperintense or hypointense, and an image quality observer study was performed in DW-DESS-Cones and DWI in patients. Effect of DW-DESS-Cones method on motion artifacts was tested by mixed-effects generalized linear model. Effect of DW-DESS-Cones on signal in phantom was tested by quadratic regression. Correlation was calculated between DW-DESS-Cones and DWI lesion-to-fibroglandular tissue signal ratios. Inter-observer agreement was assessed with Gwet's AC. Simulations predicted hyperintensity of lesions with DW-DESS-Cones but at a 3% to 67% lower degree than with DWI. Motion artifacts were reduced with DW-DESS-Cones versus DW-DESS-Cartesian (p < 0.05). Lesion-to-fibroglandular tissue signal ratios were not correlated between DW-DESS-Cones and DWI (r = 0.25, p = 0.38). Concordant hyperintensity/hypointensity was observed between DW-DESS-Cones and DWI in 11/14 lesions. DW-DESS-Cones improved sharpness, distortion, and overall image quality versus DWI. DW-DESS-Cones may be able to eliminate motion artifacts in the breast allowing for investigation of higher degrees of steady-state diffusion weighting. Malignant breast lesions in DW-DESS-Cones demonstrated hyperintensity with respect to surrounding tissue without an injection of contrast. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 1.

Deep Relational Reasoning for the Prediction of Language Impairment and Postoperative Seizure Outcome Using Preoperative DWI Connectome Data of Children With Focal Epilepsy.

Prolonged seizures in children with focal epilepsy (FE) may impair language functions and often reoccur after surgical intervention. This study is aimed at developing a novel deep relational reasoning network to investigate whether conventional diffusion-weighted imaging connectome analysis can be improved when predicting expressive and receptive scores of preoperative language impairments and classifying postoperative seizure outcomes (seizure freedom or recurrence) in individual FE children. To deeply reason the dependencies of axonal connections that are sparsely distributed in the whole brain, this study proposes the "dilated CNN + RN", a dilated convolutional neural network (CNN) combined with a relation network (RN). The performance of the dilated CNN + RN was evaluated using whole brain connectome data from 51 FE children. It was found that when compared with other state-of-the-art algorithms, the dilated CNN + RN led to an average improvement of 90.2% and 97.3% in predicting expressive and receptive language scores, and 2.2% and 4% improvement in classifying seizure freedom and seizure recurrence, respectively. These improvements were independent of the prefixed connectome densities. Also, the dilated CNN + RN could provide an explainable artificial intelligence (AI) model by computing gradient-based regression/classification activation maps. This mapping analysis revealed left superior-medial frontal cortex, bilateral hippocampi, and cerebellum as crucial hubs, facilitating important connections that were most predictive of language function and seizure refractoriness after surgery.

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models.

Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer. To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo. Retrospective. Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer. 3T multishell diffusion-weighted sequence. Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps. Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models. The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm2 /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order. The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.

Analysis of correlation between magnetic resonance imaging features and prognosis of invasive pituitary adenoma

Objective: To explore the application of conventional nuclear magnetic resonance imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging in invasive pituitary adenoma, and to study the correlation between its imaging features and prognosis. Methods: The imaging manifestations (including T1WI, T2WI, ADC and dynamic contrast-enhanced MR), clinical features and pathological data of 47 cases of invasive pituitary adenomas (IPA) in Tianjin Huanhu Hospital from June 2017 to June 2018 were retrospectively analyzed, and compared with 16 cases of non-invasive pituitary adenoma (NIPA). The differences in tumor/white matter signal intensity ratio on T1WI and T2WI images, MR signs, ADC values and time signal-curve type, peak enhancement time and average enhancement rate between the two groups were observed. Postoperative MR enhanced scan and related hormone results were reviewed to evaluate the prognosis of all patients. Logistic stepwise regression analysis was used to analyze the relationship between IPA imaging characteristics and prognosis. Results: The location, shape, size, Knosp classification and ADC value of tumors were significantly different between IPA and NIPA groups (χ(2) values were 41.344, 21.818, 28.596 respectively, t values were 5.869, 11.720 respectively, all P<0.05). The majority of T1WI, T2WI and enhanced heterogeneous signals occurred in IPA group (P<0.05). IPA showed two-stage enhancement characteristics of rapid enhancement and fast subsidence phase. There were significant differences in peak enhancement time between intrasellar tumors and invasive cavernous sinus tumors. There were no significant differences in signal intensity of T1 and diffusion limitation and two-stage enhancement characteristics between the two groups (all P>0.05). There was a significant correlation between the ratio of tumor body/white matter signal on T2WI, ADC value and the constitution of invasive pituitary tumors (all P<0.05). In IPA, 9 cases(19%) were totally resected, 25 cases(53%) subtotally resected and 13 cases(28%) partially resected. Postoperative symptoms were improved in 29 cases(62%), alleviated in 6 cases(13%), unhealed in 3 cases(6%), assisted drug therapy in 7 cases and radiotherapy in 15 cases. All cases were followed up for 7 to 16 months, with a median time of 12.5 months. Eight cases recurred, with a recurrence rate of 17%. There were significant differences in the total/subtotal resection rate, the improvement rate of symptoms and the need for adjuvant treatment among IPA with different texture (all P<0.05).Knosp grade, tumor size and the ratio of tumor body to white matter on T2WI were associated with IPA recurrence (all P<0.05). Conclusion: It is helpful to diagnose IPA by analyzing the distribution, shape, size, ADC value and signal difference of tumors in sella turcica region through MR and DWI signs. Combined with dynamic contrast enhancement features, the invasion of IPA and its surrounding tissues can be early judged, and the texture of the tumors can be assessed by imaging signs, so as to better evaluate the therapeutic effect and prognosis of IPA.

Characterization of pediatric head and neck masses with quantitative analysis of diffusion-weighted imaging and measurement of apparent diffusion coefficients

Purpose:Our objective was to investigate the accuracy of quantitative diffusion-weighted imaging (DWI) to determine the histopathologic diagnosis of pediatric head and neck lesions.Materials and Methods:This retrospective study included 100 pediatric patients recently diagnosed with head and neck tumors. All patients underwent preoperative conventional magnetic resonance imaging (MRI) and DWI. Each lesion was evaluated according to signal characteristics, enhancement pattern, and diffusivity. The average apparent diffusion coefficient (ADC) obtained from each tumor was compared to the histological diagnosis of benign, locally malignant, or malignant categories.Results:Our retrospective study showed a significant negative correlation between average ADC and tumor histopathologic diagnosis (P < 0.001, r = -0.54). The mean ADC values of benign, locally malignant lesions, and malignant tumors were 1.65 ± 0.58 × 10–3, 1.43 ± 0.17 × 10–3, and 0.83 ± 0.23 × 10–3 mm2 s-1, respectively. The ADC values of benign and locally malignant lesions were overlapped. We found a cut-off value of ≥1.19 × 10–3 mm2s-1 to differentiate benign from malignant pediatric head and neck masses with a sensitivity of 97.3%, specificity of 80.0%, positive predictive value of 94.7%, and negative predictive value of 88.9%.Conclusion:Diffusion-weighted MRI study is an accurate, fast, noninvasive, and nonenhanced technique that can be used to characterize head and neck lesions. DWI helps to differentiate malignant from benign lesions based on calculated ADC values. Additionally, DWI is helpful to guide biopsy target sites and decrease the rate of unnecessary invasive procedures.

Comparison of multiplexed sensitivity encoding and single-shot echo-planar imaging for diffusion-weighted imaging of the liver

PurposeTo compare multiplexed sensitivity encoding (MUSE) and conventional diffusion-weighted magnetic resonance imaging (cDWI) techniques in liver MRI. MethodsFifty-nine patients who underwent both two-shot echo-planar DWI using MUSE and single-shot echo-planar cDWI at a 3.0-T MRI system were included. Qualitative parameters were independently evaluated by three radiologists, and quantitative parameters were calculated on the basis of region of interest measurements. Receiver operating characteristic curve analysis and McNemar’s test were used to compare solid lesion characterization results and lesion detectability, respectively. ResultsAll reviewers found less image noise, sharper liver contours, milder susceptibility artifacts, and better lesion conspicuity in MUSE-DWI than in cDWI (reader average mean, 4.1–4.5 vs. 3.5–4.0; p < 0.05). The signal-to-noise ratio (SNR) of the liver was significantly higher in MUSE-DWI than in cDWI (right lobe: mean, 9.39 vs. 8.10, p < 0.001; left lobe: mean, 8.34 vs. 7.19, p < 0.001), while the SNR of the lesion (mean, 23.72 vs. 23.88, p = 0.911) and lesion-to-liver contrast-to-noise ratio (mean, 14.65 vs. 15.41, p = 0.527) were comparable between MUSE-DWI and cDWI. Solid lesion characterization results were comparably accurate between MUSE-DWI and cDWI (reader average area under the receiver operating characteristic curve, 0.985 vs. 0.986, p = 0.480). The detectability of lesions was better in MUSE-DWI than in cDWI (reader consensus, 83.7 % [41/49] vs. 67.3 % [33/49], p = 0.021). ConclusionMUSE-DWI can provide multi-shot liver DWI with less noise, fewer distortions, improved SNR of the liver, and better lesion detectability.

Simultaneous multi-slice accelerated diffusion-weighted imaging with higher spatial resolution for patients with liver metastases from neuroendocrine tumours

To evaluate the imaging characteristics of simultaneous multi-slice (SMS) accelerated diffusion-weighted imaging (DWI) with decreased section thickness, with and without motion correction, in comparison to conventional DWI (cDWI) for the detection of lesions in patients with neuroendocrine tumour (NET) liver metastases. Fifteen patients with NET liver metastases underwent cDWI (section thickness [SL]=4 mm) and SMS-DWI (SL=2 mm). Non-linear motion-corrected (Moco)-SMS-DWI was generated in addition to the original series. Qualitative imaging characteristics (five-point Likert scale), the number of high signal lesions, and the detectability and delineation of lesions were evaluated and compared using the Friedman and the Dunn-Bonferroni tests. The test-retest variability (TRV) of the cDWI and SMS-DWI techniques was investigated among 11 healthy volunteers who underwent cDWI (SL=4 mm) and SMS-DWI (SL=4 mm) twice. The Friedman and the Dunn-Bonferroni post-hoc tests were used to compare the mean apparent diffusion coefficient (ADC) and the TRV in different liver regions between the three series. Moco-SMS-DWI demonstrated significantly superior overall image quality (p<0.001) with significantly fewer artefacts (p=0.003) than cDWI. The number of lesions detected by cDWI, SMS-DWI, and Moco-SMS-DWI were 348, 504, and 523, respectively. The detectability and delineation of the lesions and the ADC values were significantly higher on the SMS-DWI and Moco-SMS-DWI images than on the cDWI images (all p<0.001). Moco-SMS-DWI showed significantly higher TRV than cDWI in regions near the liver edge (p=0.018). SMS-DWI achieves higher spatial resolution than cDWI within the same acquisition time, detects more lesions, and provides better lesion delineation. By applying motion correction, the TRV of DWI could be enhanced in regions near the liver edge.

DIFFUSION-WEIGHTED IMAGING FOR DIFFERENTIATING MALIGNANT FROM BENIGN MEDIASTINAL LYMPHADENOPATHY

Introduction. Mediastinal lymphadenopathy can be caused by a wide range of benign and malignant states. Determination of the genesis of lymphadenopathy is crucial for treatment planning and prognosis of the disease.The purpose of the studywas to evaluate the diagnostic accuracy of diffusion weighted imaging (DWI) with apparent diffusion coefficient (ADC) measurements in differentiating malignant versus benign mediastinal lymphadenopathy.Material and Methods. 48 consecutive patients with at least one enlarged mediastinal lymph node revealed on CT-scans were examined on 1,5 T MR-machine with conventional images and respiratory-triggered DWI. In all patients one of the biggest solid lymph nodes was selected for ADC measurements and mean ADCs of each node were recorded. ADCs were correlated with the results of complete diagnostic work-up (including histopathological diagnosis in 41 patients) and follow-up CT. Statistics included Student’s t-test, Mann-Whitney U-test and ROC-curve analysis.Results. 27 lymph nodes were classified as malignant (metastases, lymphoma) and 21 lymph nodes were classified as benign (sarcoidosis, reactive hyperplasia, tuberculosis). Mean ADC of malignant lymph nodes (1,02 ± 0,29×10−3 mm2/s) was significantly lower than that of benign lymph nodes (1,57 ± 0,32×10−3 mm2/s), p&lt;0,0001. The cut-off value of ≤1,3×10−3mm2/s for ADC indicated the malignancy with a sensitivity of 81,5 % and a specificity of 85,7%. The area under the ROC-curve was 0,89 (95 % confidence interval: 0,77, 0,96), p&lt;0,0001.Conclusion. DWI is a promising technique in chest pathology. DWI with ADC measurements could be used as a good complementary tool in the diagnostic work-up of patients with mediastinal lymphadenopathy.