Axial Diffusion-weighted Imaging Research Articles

Radiomics analysis for prediction of lymph node metastasis after neoadjuvant chemotherapy based on pretreatment MRI in patients with locally advanced cervical cancer.

This study aims to develop and validate a pretreatment MRI-based radiomics model to predict lymph node metastasis (LNM) following neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Patients with LACC who underwent NACT from two centers between 2013 and 2022 were enrolled retrospectively. Based on the lymph node (LN) status determined in the pathology reports after radical hysterectomy, patients were categorized as LN positive or negative. The patients from center 1 were assigned as the training set while those from center 2 formed the validation set. Radiomics features were extracted from pretreatment sagittal T2-weighted imaging (Sag-T2WI), axial diffusion-weighted imaging (Ax-DWI), and the delayed phase of dynamic contrast-enhanced sagittal T1-weighted imaging (Sag-T1C) for each patient. The K-best and least absolute shrinkage and selection operator (LASSO) methods were employed to reduce dimensionality, and the radiomics features strongly associated with LNM were selected and used to construct three single-sequence models. Furthermore, clinical variables were incorporated through multivariate regression analysis and fused with the selected radiomics features to construct the clinical-radiomics combined model. The diagnostic performance of the models was assessed using receiver operating characteristic (ROC) curve analysis. The clinical utility of the models was evaluated by the area under the ROC curve (AUC) and decision curve analysis (DCA). A total of 282 patients were included, comprising 171 patients in the training set, and 111 patients in the validation set. Compared to the Sag-T2WI model (AUC, 95%CI, training set, 0.797, 0.722-0.782; validation set, 0.648, 0.521-0.776) and the Sag-T1C model (AUC, 95%CI, training set, 0.802, 0.723-0.882; validation set, 0.630, 0.505-0.756), the Ax-DWI model exhibited the highest diagnostic performance with AUCs of 0.855 (95%CI, 0.791-0.919) in training set, and 0.753 (95%CI, 0.638-0.867) in validation set, respectively. The combined model, integrating selected features from three sequences and FIGO stage, surpassed predictive ability compared to the single-sequence models, with AUC of 0.889 (95%CI, 0.833-0.945) and 0.859 (95%CI, 0.781-0.936) in the training and validation sets, respectively. The pretreatment MRI-based radiomics model, integrating radiomics features from three sequences and clinical variables, exhibited superior performance in predicting LNM following NACT in patients with LACC.

Diffusion-weighted imaging as a potential non-gadolinium alternative for immediate assessing the hyperacute outcome of MRgFUS ablation for uterine fibroids.

The aim of this study was to investigate the value of diffusion-weighted imaging (DWI) as a potential non-gadolinium alternative for promptly assessing the hyperacute outcome of magnetic resonance-guided focused ultrasound (MRgFUS) treatment for uterine fibroids. In this retrospective study we included 65 uterine fibroids from 44 women, who underwent axial DWI (b-value: 800s/mm2) and contrast-enhanced (CE) MR within 15min post-ablation. Two blinded observers independently reviewed the DWI findings of ablated necrotic lesions and measured their volumes on DWI and CE images. The post-ablation DWI images revealed clear depiction of ablative necrotic lesions in all fibroids, which were classified into two types: the bull's eye sign (type 1) and the bright patch sign (type 2). The inter-observer intraclass correlation coefficient for classifying DWI signal types was 0.804 (p < 0.001). Volumetric analysis of ablated necrosis using DWI and CE T1-weighted imaging showed no significant variance, nor did the non-perfused volume ratios (all p > 0.05). Bland-Altman analysis revealed a mean difference of 2.38% and 1.71% in non-perfused volume ratios between DWI and CE, with 95% limits of agreement from - 19.06 to 23.82% and - 18.40 to 21.82%, respectively. The findings of this study support the potential of DWI as a viable non-gadolinium alternative for evaluating the hyperacute outcomes of MRgFUS ablation in uterine fibroids.

Coronal Diffusion-Weighted Imaging Does Not Improve Detection of Posterior Fossa Infarcts Compared With Conventional Axial Diffusion-Weighted Imaging.

Recent publications have suggested incorporating coronal diffusion-weighted imaging (DWI) sequences and axial DWI sequences to enhance the detection of posterior fossa infarcts (PFIs). This study evaluated the utility of coronal DWIs compared with axial DWIs for assessing PFIs in the emergency department (ED). A retrospective, institutional review board-approved study was conducted at a level I stroke center, including 118 patients who presented to the ED between 2016 and 2023 with suspected PFI. Inclusion criteria involved patients who underwent emergent 1.5 T magnetic resonance imaging (MRI) and had axial and coronal DWI sequences. Two neuroradiologists independently evaluated the DWI sequences for PFI detection in 2 rounds, with a 4-week interval between rounds. The neuroradiologists assessed the quality of axial and coronal DWIs using a 5-point Likert scale. Descriptive statistics, interrater reliability, and marginal homogeneity tests were performed. Among the 118 MRI scans, 23 (19%) showed PFI on axial and coronal DWI sequences. All 23 cases were identified on axial DWI, whereas 8 cases of PFI (35%) were not detected on coronal DWI (P value = 0.013). No PFIs were observed on coronal DWI that was not identified on axial DWI. The quality scores for both raters were significantly higher for axial DWIs than coronal DWIs (P value <0.00001). Despite recent recommendations advocating for the inclusion of coronal DWI in PFI detection, this study's findings indicate no improvement in PFI detection or image quality using coronal DWI. Further research is necessary to validate these results and explore the potential benefits of incorporating coronal DWI in assessing posterior fossa strokes.

A machine learning radiomics model based on bpMRI to predict bone metastasis in newly diagnosed prostate cancer patients.

ObjectivesTo develop and evaluate a machine learning radiomics model based on bpMRI to predict bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients. MethodsWe retrospectively analyzed biparametric magnetic resonance imaging MRI (bpMRI) scans of PCa patients from multiple centers between January 2016 and October 2021. 348 PCa patients were recruited from two institutions for this study. The first institution contributed 284 patients, stratified and randomly divided into training and internal validation cohorts at a 7:3 ratio. The remaining 64 patients were sourced from the second institution and comprised the external validation cohort. Radiomics features were extracted from axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) tumor regions. We developed the radiomics prediction model for BM in the training cohort and validated it in the internal and external validation cohorts. As a benchmark, we trained the logistic regression model with lasso feature reduction (LFR-LRM) in the training cohort and further compared it with Naive Bayes, eXtreme Gradient Boosting (XGboost), Random Forest (RF), GBDT, SVM, Adaboost, and KNN algorithms and validated in both the internal and external cohorts. The performance of several predictive models was assessed by receiver operating characteristic (ROC). ResultsThe LFR-LRM model achieved an area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI: 0.822–0.974) and an accuracy of 0.828 (95% CI: 0.713–0.911). The AUC and accuracy in external validation were 0.866 (95% CI: 0.784–0.948) and 0.769 (95% CI: 0.648–0.864), respectively. The RF and XGBoost models outperformed the LFR-LRM, with AUCs of 0.907 (95% CI: 0.863–0.949) and 0.928 (95% CI: 0.882–0.974) and accuracies of 0.831 (95% CI: 0.727–0.907) and 0.884 (95% CI: 0.792–0.946). External validation for these models yielded AUCs and accuracies of 0.911 (95% CI: 0.861–0.966), 0.921 (95% CI: 0.889–0.953), and 0.846 (95% CI: 0.735–0.923) and 0.876 (95% CI: 0.771–0.945), respectively. ConclusionsThe XGboost machine learning model is more accurate than LFR-LRM for predicting BM in patients with newly confirmed PCa.

Peritumoral MRI Radiomics Features Increase the Evaluation Efficiency for Response to Chemotherapy in Patients With Epithelial Ovarian Cancer.

It remains unclear whether extracting peritumoral volume (PTV) radiomics features are useful tools for evaluating response to chemotherapy of epithelial ovarian cancer (EOC). To evaluate MRI radiomics signatures (RS) capturing subtle changes of PTV and their added evaluation performance to whole tumor volume (WTV) for response to chemotherapy in patients with EOC. Retrospective. 219 patients aged from 15 to 79 years were enrolled. 3.0 or 1.5T, axial fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), and contrast enhanced T1-weighted imaging (CE-T1WI). MRI features were extracted from the four axial sequences and six different volumes of interest (VOIs) (WTV and WTV + PTV (WPTV)) with different peritumor sizes (PS) ranging from 1 to 5 mm. Those features underwent preprocessing, and the most informative features were selected using minimum redundancy maximum relevance and least absolute shrinkage and selection operator to construct the RS. The optimal RS, with the highest area under the curve (AUC) of receiver operating characteristic was then integrated with independent clinical characteristics through multivariable logistic regression to construct the radiomics-clinical model (RCM). Mann-Whitney U test, chi-squared test, DeLong test, log-rank test. P < 0.05 indicated a significant difference. All the RSs constructed on WPTV exhibited higher AUCs (0.720-0.756) than WTV (0.671). Of which, RS with PS = 2 mm displayed a significantly better performance (AUC = 0.756). International Federation of Gynecology and Obstetrics (FIGO) stage was identified as the exclusive independent clinical evaluation characteristic, and the RCM demonstrated higher AUC (0.790) than the RS, but without statistical significance (P = 0.261). The radiomics features extracted from PTV could increase the efficiency of WTV radiomics for evaluating the chemotherapy response of EOC. The cut-off of 2 mm PTV was a reasonable value to obtain effective evaluation efficiency. 4 TECHNICAL EFFICACY: Stage 2.

Variation of b Value for Axial DWI Prostate MR Imaging in Cancer: Which b Value is Optimal?

Background: Diffusion Weighted Imaging (DWI) is a technique that visualizes the movement of water molecules from areas of higher to lower concentration. In DWI sequences, the b-value parameter affects the signal intensity in the images. The objective of this study is to describe the differences in anatomical image information between b-value variations of 1500s/mm2, 2000s/mm2, and 2500s/mm2 in MRI prostate case cancer and to determine the most optimal b-value. Method: This study used a pre-exeachimental approach with a 1.5T Philips MRI machine at a private hospital in South Jakarta from March to April 2023. The sample consisted of 12 Prostate MRI images with DWI sequences and different b-values. Visual grading assessment was eachformed by three radiology specialists, and the data were analyzed using the Friedman test in SPSS. Results: The results showed a significant difference in anatomical image information based on the b-value variations used, with a p-value of 0.05 (0.02). The highest mean rank was obtained from the Friedman test for the b-value of 2000s/mm2. Conclusion: The significant difference in anatomical image information occurred due to differences in signal intensity and noise levels. Prostate cancer tissues appeared more hyperintense with lower noise in images with higher b-values, and vice versa. Additionally, the b-value of 2000s/mm2 resulted in the most optimal signal intensity and noise level. In conclusion, the use of b-value variations in axial DWI images of prostate cancer Prostate MRI to differences in anatomical image information, with the most optimal image information observed at a b-value of 2000s/mm2.

Deep Learning for Delineation of the Spinal Canal in Whole-Body Diffusion-Weighted Imaging: Normalising Inter- and Intra-Patient Intensity Signal in Multi-Centre Datasets.

Whole-Body Diffusion-Weighted Imaging (WBDWI) is an established technique for staging and evaluating treatment response in patients with multiple myeloma (MM) and advanced prostate cancer (APC). However, WBDWI scans show inter- and intra-patient intensity signal variability. This variability poses challenges in accurately quantifying bone disease, tracking changes over follow-up scans, and developing automated tools for bone lesion delineation. Here, we propose a novel automated pipeline for inter-station, inter-scan image signal standardisation on WBDWI that utilizes robust segmentation of the spinal canal through deep learning. We trained and validated a supervised 2D U-Net model to automatically delineate the spinal canal (both the spinal cord and surrounding cerebrospinal fluid, CSF) in an initial cohort of 40 patients who underwent WBDWI for treatment response evaluation (80 scans in total). Expert-validated contours were used as the target standard. The algorithm was further semi-quantitatively validated on four additional datasets (three internal, one external, 207 scans total) by comparing the distributions of average apparent diffusion coefficient (ADC) and volume of the spinal cord derived from a two-component Gaussian mixture model of segmented regions. Our pipeline subsequently standardises WBDWI signal intensity through two stages: (i) normalisation of signal between imaging stations within each patient through histogram equalisation of slices acquired on either side of the station gap, and (ii) inter-scan normalisation through histogram equalisation of the signal derived within segmented spinal canal regions. This approach was semi-quantitatively validated in all scans available to the study (N = 287). The test dice score, precision, and recall of the spinal canal segmentation model were all above 0.87 when compared to manual delineation. The average ADC for the spinal cord (1.7 × 10-3 mm2/s) showed no significant difference from the manual contours. Furthermore, no significant differences were found between the average ADC values of the spinal cord across the additional four datasets. The signal-normalised, high-b-value images were visualised using a fixed contrast window level and demonstrated qualitatively better signal homogeneity across scans than scans that were not signal-normalised. Our proposed intensity signal WBDWI normalisation pipeline successfully harmonises intensity values across multi-centre cohorts. The computational time required is less than 10 s, preserving contrast-to-noise and signal-to-noise ratios in axial diffusion-weighted images. Importantly, no changes to the clinical MRI protocol are expected, and there is no need for additional reference MRI data or follow-up scans.

MRI AND BLOOD-BASED BIOMARKERS ASSOCIATED WITH SURGICAL MANAGEMENT IN CHILDREN AND ADULTS WITH SMALL BOWEL CROHN'S DISEASE

Abstract BACKGROUND Despite advances in medical therapy, many children and adults with ileal Crohn’s Disease (CD) progress to fibrostenosis requiring surgery. Prior studies have identified circulating and imaging biomarkers associated with strictures, although their associations with need for surgery are not well-established. PURPOSE We aimed to identify MRI and circulating biomarkers associated with the need for surgical management in chidlren and adults with ileal CD. METHODS This prospective, multi-center study included pediatric and adult CD cases undergoing ileal resection (n=50), and CD controls receiving medical therapy (n=83). Noncontrast research MRI examinations measured bowel wall three-dimensional magnetization transfer ratio normalized to skeletal muscle (normalized 3D MTR), modified Look-Locker inversion recovery (MOLLI) T1 relaxation, intravoxel incoherent motion (IVIM) diffusion-weighted imaging metrics, and the simplified magnetic resonance index of activity (sMaRIA) (Figure 1). Circulating biomarkers included CD64, ECM1, and GM-CSF autoantibodies (Ab). Clinical variables, including demographics, anthropometrics, and routine clinical laboratory data (e.g., CRP, fecal calprotectin), also were obtained at the research visit. Associations between MRI and circulating biomarkers and need for ileal resection were tested using univariate and multivariable LASSO regression. RESULTS Mean participant age was 23.9±13.1 years. Disease duration and treatment exposures did not vary between the groups. Univariate biomarker associations with ileal resection included log GM-CSF Ab (odds ratio [OR]=2.87; p=0.0009), normalized 3D MTR (OR=1.05; p=0.002), log MOLLI T1 (OR=0.01; p=0.02), log IVIM perfusion fraction (f) (OR=0.38; p=0.04), and IVIM apparent diffusion coefficient (ADC) (OR=0.3; p=0.001). MRI and circulating biomarker Tukey box plots are presented in Figure 2. The multivariable model for surgery based upon AICc criteria included age (OR=1.03; p=0.29), BMI (OR=0.91; p=0.09), log GM-CSF Ab (OR=3.37; p=0.01), normalized 3D MTR (OR=1.07; p=0.007), sMaRIA (OR=1.14; p=0.61), luminal narrowing (OR=10.19; p=0.003), log CRP (normalized) (OR=2.75; p=0.10), and hematocrit (OR=0.90; p=0.13). CONCLUSION After accounting for clinical and MRI measures of severity, normalized 3D MTR and GM-CSF Ab are associated with need for surgery in ileal CD. GM-CSF autoantibodies and MRI biomarker sequences may be useful for guiding management decisions in CD patients. Figure 1 Research MRI images from a surgical Crohn’s disease patient with stricturing behavior involving the terminal ileum. A and B. Coronal and axial fat-saturated T2-weighted single-shot fast spin-echo (SSFSE) images show wall thickening and luminal narrowing of the terminal ileum (arrows) with more proximal bowel dilation measuring greater than 3 cm. C. Axial 3D magnetization transfer images – MT pulse on (bottom image, arrow), MT pulse off (top image, arrow). D. Axial modified Look-Locker inversion recovery (MOLLI) T1 relaxation image (arrow). E. Representative axial intravoxel incoherent motion diffusion-weighted images with increasing b-values (left to right, arrows). Figure 2 Variation in circulating and imaging biomarkers in Crohn’s disease patients with medical and surgical management. Ctrl: non-IBD healthy controls; Med: CD patients with nonsurgical medical management; Surg: CD patients with surgical management.

MRI-based automatic identification and segmentation of extrahepatic cholangiocarcinoma using deep learning network

BackgroundAccurate identification of extrahepatic cholangiocarcinoma (ECC) from an image is challenging because of the small size and complex background structure. Therefore, considering the limitation of manual delineation, it’s necessary to develop automated identification and segmentation methods for ECC. The aim of this study was to develop a deep learning approach for automatic identification and segmentation of ECC using MRI.MethodsWe recruited 137 ECC patients from our hospital as the main dataset (C1) and an additional 40 patients from other hospitals as the external validation set (C2). All patients underwent axial T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Manual delineations were performed and served as the ground truth. Next, we used 3D VB-Net to establish single-mode automatic identification and segmentation models based on T1WI (model 1), T2WI (model 2), and DWI (model 3) in the training cohort (80% of C1), and compared them with the combined model (model 4). Subsequently, the generalization capability of the best models was evaluated using the testing set (20% of C1) and the external validation set (C2). Finally, the performance of the developed models was further evaluated.ResultsModel 3 showed the best identification performance in the training, testing, and external validation cohorts with success rates of 0.980, 0.786, and 0.725, respectively. Furthermore, model 3 yielded an average Dice similarity coefficient (DSC) of 0.922, 0.495, and 0.466 to segment ECC automatically in the training, testing, and external validation cohorts, respectively.ConclusionThe DWI-based model performed better in automatically identifying and segmenting ECC compared to T1WI and T2WI, which may guide clinical decisions and help determine prognosis.

Deep learning-based k-space-to-image reconstruction and super resolution for diffusion-weighted imaging in whole-spine MRI

PurposeTo assess the feasibility of deep learning (DL)-based k-space-to-image reconstruction and super resolution for whole-spine diffusion-weighted imaging (DWI). MethodThis retrospective study included 97 consecutive patients with hematologic and/or oncologic diseases who underwent DL-processed whole-spine MRI from July 2022 to March 2023. For each patient, conventional (CONV) axial single-shot echo-planar DWI (b = 50, 800 s/mm2) was performed, followed by DL reconstruction and super resolution processing. The presence of malignant lesions and qualitative (overall image quality and diagnostic confidence) and quantitative (nonuniformity [NU], lesion contrast, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and ADC values) parameters were assessed for DL and CONV DWI. ResultsUltimately, 67 patients (mean age, 63.0 years; 35 females) were analyzed. The proportions of vertebrae with malignant lesions for both protocols were not significantly different (P: [0.55–0.99]). The overall image quality and diagnostic confidence scores were higher for DL DWI (all P ≤ 0.002) than CONV DWI. The NU, lesion contrast, SNR, and CNR of each vertebral segment (P ≤ 0.04) but not the NU of the sacral segment (P = 0.51) showed significant differences between protocols. For DL DWI, the NU was lower, and lesion contrast, SNR, and CNR were higher than those of CONV DWI (median values of all segments; 19.8 vs. 22.2, 5.4 vs. 4.3, 7.3 vs. 5.5, and 0.8 vs. 0.7). Mean ADC values of the lesions did not significantly differ between the protocols (P: [0.16–0.89]). ConclusionsDL reconstruction can improve the image quality of whole-spine diffusion imaging.

Diagnostic performance of short noncontrast biparametric 3-T MRI for tonsillar infections: comparison with a full protocol including contrast-enhanced sequences

BackgroundWe investigated whether a short, 5-min magnetic resonance imaging (MRI) protocol consisting of only axial T2-weighted and diffusion-weighted imaging (DWI) sequences can discriminate between tonsillar infections, peritonsillar abscesses and deeply extending abscesses in a retrospective, blinded, multireader setting.MethodsWe included patients sent by emergency physicians with suspected pharyngotonsillar infections who underwent emergency neck 3-T MRI from April 1 2013 to December 31 2018. Three radiologists (with 10−16 years of experience) reviewed the images for abscesses and their extension into deep neck spaces. Data were reviewed first using only axial T2-weighted Dixon images and DWI (short protocol) and second including other sequences and contrast-enhanced T1-weighted Dixon images (full protocol). Diagnostic accuracy, interobserver agreement, and reader confidence were measured. Surgical findings and clinical course served as standard of reference.ResultsThe final sample consisted of 52 patients: 13 acute tonsillitis with no abscesses, 19 peritonsillar abscesses, and 20 deeply extending abscesses. Using the short protocol, diagnostic accuracy for abscesses across all readers was good-to-excellent: sensitivity 0.93 (95% confidence interval 0.87−0.97), specificity 0.85 (0.70−0.93), accuracy 0.91 (0.85−0.95). Using the full protocol, respective values were 0.98 (0.93−1.00), 0.85 (0.70−0.93), and 0.95 (0.90−0.97), not significantly different compared with the short protocol. Similar trends were seen with detecting deep extension. Interobserver agreement was similar between protocols. However, readers had higher confidence in diagnosing abscesses using the full protocol.ConclusionsShort MRI protocol showed good-to-excellent accuracy for tonsillar abscesses. Contrast-enhanced images improved reader confidence but did not affect diagnostic accuracy or interobserver agreement.Relevance statementShort protocol consisting only of T2-weighted Dixon and DWI sequences can accurately image tonsillar abscesses, which may improve feasibility of emergency neck MRI.Key points• The short 3-T MRI protocol (T2-weighted images and DWI) was faster (5 min) than the full protocol including T1-weighted contrast-enhanced images (24 min).• The short 3-T MRI protocol showed good diagnostic accuracy for pharyngotonsillar abscesses.• Contrast-enhanced sequences improved reader confidence but did not impact diagnostic accuracy or interobserver agreement.Graphical

Quality assessment of routine brain imaging at 0.55 T: initial experience in a clinical workflow.

The purpose of this study was to assess the quality of clinical brain imaging in healthy subjects and patients on an FDA-approved commercial 0.55 T MRI scanner, and to provide information about the feasibility of using this scanner in a clinical workflow. In this IRB-approved study, brain examinations on the scanner were prospectively performed in 10 healthy subjects (February-April 2022) and retrospectively derived from 44 patients (February-July 2022). Images collected using the following pulse sequences were available for assessment: axial DWI (diffusion-weighted imaging), apparent diffusion coefficient maps, 2D axial fluid-attenuated inversion recovery images, axial susceptibility-weighted images (both magnitude and phase), sagittal T1 -weighted (T1w) Sampling Perfection with Application Optimized Contrast images, sagittal T1w MPRAGE (magnetization prepared rapid gradient echo) with contrast enhancement, axial T1w turbo spin echo (TSE) with and without contrast enhancement, and axial T2 -weighted TSE. Two readers retrospectively and independently evaluated image quality and specific anatomical features in a blinded fashion on a four-point Likert scale, with a score of 1 being unacceptable and 4 being excellent, and determined the ability to answer the clinical question in patients. For each category of image sequences, the mean, standard deviation, and percentage of unacceptable quality images (<2) were calculated. Acceptable (rating ≥ 2) image quality was achieved at 0.55 T in all sequences for patients and 85% of the sequences for healthy subjects. Radiologists were able to answer the clinical question in all patients scanned. In total, 50% of the sequences used in patients and about 60% of the sequences used in healthy subjects exhibited good (rating ≥ 3) image quality. Based on these findings, we conclude that diagnostic quality clinical brain images can be successfully collected on this commercial 0.55 T scanner, indicating that the routine brain imaging protocol may be deployed on this system in the clinical workflow.

The Effect of Postnatal Cytomegalovirus Infection on (Micro)structural Cerebral Development in Very Preterm Infants at Term-Equivalent Age

Introduction: There are some data indicating a negative impact of postnatal cytomegalovirus (CMV) infection on long-term neurodevelopmental outcome of preterm infants. So far, there is only little knowledge about a cerebral imaging correlate of these neurodevelopmental alterations induced by postnatal CMV infection in preterm infants. The aim of the current study was to investigate the effect of postnatal CMV infection on the incidence of brain injury and on microstructural brain maturation in very preterm infants at term-equivalent age. Methods: Infants <32 gestational weeks (02/2011–11/2018) received cerebral MRI including axial diffusion-weighted images at term-equivalent age. All infants were screened for CMV infection using urine/saliva samples, and infection was regarded as acquired postnatal if a sample became positive >5 postnatal days. We compared brain injury as well as fractional anisotropy and apparent diffusion coefficient in 14 defined cerebral regions between infants with and without postnatal CMV infection. Results: 401 infants were eligible, of whom 18 (4.5%) infants had a postnatal CMV infection. There were no significant differences in rates of brain injury or in microstructural brain development between both groups. This applied equally to the subgroup of infants <28 gestational weeks. Conclusion: Although infants with postnatal CMV infection were born more immature and more frequently suffered from complications related to immaturity, we neither observed a higher rate of preterm brain injury nor disadvantageous alterations in microstructural brain maturation at term-equivalent age.

3.0 T prostate MRI: Visual assessment of 2D and 3D T2-weighted imaging sequences using PI-QUAL score

PurposeComparing 2D and 3D T2 weighted sequences in terms of image quality in 3.0 T MRI with readers of varied experiences, using PI-QUAL inspired criteria. Methods91 male patients with suspected prostate cancer (PCa) underwent diagnostic prostate MRI on a 3.0 T MR system using a 32-channel phased-array torso coil before prostate biopsy. MRI protocol included 3D T2w images, axial 2D T2w images, axial diffusion-weighted images (DWI) with the corresponding ADC apparent diffusion coefficient maps, and axial dynamic contrast enhanced images. 3D T2w and 2D T2w imaging were compared by 4 radiologists using a Likert scale for image quality (overall anatomy, delineation of capsule, seminal vesicles, ejaculatory ducts, sphincter muscle, artifacts), tumor delimitation and conspicuity. ResultsNo significant differences in terms of overall quality between 3D and 2D T2w images were found. However 2D T2w demonstrated higher rating than 3D T2w images as for the image quality of the external capsule, sphincter muscle and ejaculatory ducts delineation (p < 0.05). Conclusion3D T2w sequence can’t replace 2D T2w sequence, despite good quality images but it remains more prone to artifacts. Quality of 2D T2w sequences was substantially superior to 3D sequences for delineation of key structures as external capsule, sphincter muscle. The use of PI-QUAL criteria allows reproducible analysis of the quality of T2 weighted images.

Intraoperative MRI Assessment of the Tissue Damage during Laser Ablation of Hypothalamic Hamartoma.

Laser ablation for treatment of hypothalamic hamartoma (HH) is a minimally invasive and effective technique used to destroy hamartomatous tissue and disconnect it from the functioning brain. Currently, the gold standard to evaluate the amount of tissue being "burned" is the use of heat maps during the ablation procedure. However, these maps have low spatial resolution and can be misleading in terms of extension of the tissue damage. The aim of this study is to use different MRI sequences immediately after each laser ablation and correlate the extension of signal changes with the volume of malacic changes in a long-term follow-up scan. During the laser ablation procedure, we imaged the hypothalamic region with high-resolution axial diffusion-weighted images (DWI) and T2-weighted images (T2WI) after each ablation. At the end of the procedure, we also added a post-contrast T1-weighted image (T1WI) of the same region. We then correlated the product of the maximum diameters on axial showing signal changes (acute oedema on T2WI, DWI restriction rim, DWI hypointense core and post-contrast T1WI rim) with the product of the maximum diameters on axial T2WI of the malacic changes in the follow-up scan, both as a fraction of the total area of the hamartoma. The area of the hypointense core on DWI acquired immediately after the laser ablation statistically correlated better with the final area of encephalomalacia, while the T2WI, hyperintense oedema, DWI rim and T1WI rim of enhancement tended to overestimate the encephalomalacic damage. In conclusion, the use of intraoperative sequences (in particular DWI) during laser ablation can give surgeons valuable information in real time about the effective heating damage on the hamartomatous tissue, with better spatial resolution in comparison to the thermal maps.

Fast magnetic resonance imaging for diagnosing pulmonary tuberculosis in children: the sub-10-min unenhanced scan.

In this study, we aimed to report the feasibility and quality of fast (unenhanced < 10-min duration) magnetic resonance imaging (MRI) for the detection of lymphadenopathy in non-sedated children with suspected tuberculosis (TB). This was a prospective study that involved children (< 13years of age) hospitalised at Red Cross Children's Hospital with suspected pulmonary TB who were referred for a fast MRI of the chest. The limited short-duration MRI protocol included coronal short tau inversion recovery (STIR) and axial diffusion-weighted imaging (DWI) sequences with additional axial STIR and axial and coronal T2 sequences if the patient was compliant. The scan time was capped at 10min and a study was considered successfully completed when DWI and STIR images were obtained in axial planes. MRI quality was recorded as 'acceptable quality'; 'poor quality, but readable'; and 'non-diagnostic'. Of the 192 fast MRI protocol scans, 166 (86%) were successfully completed within the 10-min allotted scan period. There was no age or sex difference between successful and unsuccessful studies. The mean duration of successful scans was 6.5min (standard deviation = 1.5min, range = 4-10min). Fast (sub-10-min scan) MRI is feasible for diagnosis of lymphadenopathy in non-sedated children in the setting of suspected TB, including those below 6years of age.

Morphological predictors of neurological deterioration in patients with acute isolated pontine infarct.

To investigate morphological predictors of neurological deterioration (ND) in patients with acute isolated pontine infarct. Acute isolated pontine infarct patients within 7days after onset of stroke symptoms were included retrospectively and classified into ND and non-ND groups. Morphological phenotypes (paramedian pontine infarct [PPI], atypical PPI, small deep infarct, and other types), topographical location, and lesion size were evaluated on axial diffusion-weighted imaging. There were 210 eligible patients, of whom 62 patients had ND (29.5%). The proportion of PPI was significantly higher in ND than that in non-ND (62.9% vs 39.6%). ND occurred more frequently in PPI patients than non-PPI patients (39.8% vs 20.5%). PPI located more frequently in lower pontine (20.4% vs 8.0%) and less in upper pontine (17.3% vs 30.4%, P = 0.028), and had larger ventro-dorsal length (13.8 ± 3.8 vs 9.9 ± 3.1) and width (8.3 ± 2.3 vs 6.2 ± 1.8) than non-PPI patients. The morphological phenotype of PPI was an independent risk factor for ND (OR 4.81, 95%CI 1.54-15.07, P = 0.007) in patients with isolated pontine infarct. The ventro-dorsal length of pontine infarct lesion was associated with ND (OR 1.18, 95%CI 1.01, 1.37, P = 0.034) in PPI patients. The morphological phenotype of PPI was a potential predictor for ND in patients with acute isolated pontine infarct. The ventro-dorsal length of pontine infarct lesion was possibly associated with ND in PPI patients.

Imaging Recommendations for the Diagnosis, Staging, and Management of Adult Brain Tumors

AbstractNeuroimaging plays a pivotal role in the clinical practice of brain tumors aiding in the diagnosis, genotype prediction, preoperative planning, and prognostication. The brain tumors most commonly seen in adults are extra-axial lesions like meningioma, intra-axial lesions like gliomas and lesions of the pituitary gland. Clinical features may be localizing like partial seizures, weakness, and sensory disturbances or nonspecific like a headache. On clinical suspicion of a brain tumor, the primary investigative workup should focus on imaging. Other investigations like fundoscopy and electroencephalography may be performed depending on the clinical presentation. Obtaining a tissue sample after identifying a brain tumor on imaging is crucial for confirming the diagnosis and planning further treatment. Tissue sample may be obtained by techniques such as stereotactic biopsy or upfront surgery. The magnetic resonance (MR) imaging protocol needs to be standardized and includes conventional sequences like T1-weighted (T1W) imaging with and without contrast, T2w imaging, fluid-attenuated axial inversion recovery, diffusion-weighted imaging (DWI), susceptibility-weighted imaging, and advanced imaging sequences like MR perfusion and MR spectroscopy. Various tumor characteristics in each of these sequences can help us narrow down the differential diagnosis and also predict the grade of the tumor. Multidisciplinary co-ordination is needed for proper management and care of brain tumor patients. Treatment protocols need to be adapted and individualized for each patient depending on the age, general condition of the patient, histopathological characteristics, and genotype of the tumor. Treatment options include surgery, radiotherapy, and chemotherapy. Imaging also plays a vital role in post-treatment follow-up. Sequences like DWI, MR perfusion, and MR spectroscopy are useful to distinguish post-treatment effects like radiation necrosis and pseudoprogression from true recurrence. Radiological reporting of brain tumor images should follow a structured format to include all the elements that could have an impact on the treatment decisions in patients.

Exploration of Interstitial Fibrosis in Chronic Kidney Disease by Diffusion-Relaxation Correlation Spectrum MR Imaging: A Preliminary Study.

Renal interstitial fibrosis is one of the most common pathways in the progression of chronic kidney disease (CKD). Noninvasive evaluation of interstitial fibrosis would help monitoring CKD progression and prognosis prediction. To evaluate the severity of renal interstitial fibrosis by diffusion-relaxation correlation spectrum imaging (DR-CSI). Prospective. Forty patients with CKD and 10 healthy controls (average age 49.2 ± 14.8 years, 18 females). 3-T, DR-CSI with 36 axial spin-echo echo-planar diffusion-weighted images (6 b-values, 6 echo times). Interstitial fibrosis level (IFL) was assessed from biopsy results (IFL=1, fibrosis percentage <25%, defined as mild; IFL=2, 25%-50%, moderate; IFL=3, >50%, severe). Estimated glomerular filtration rate (eGFR) was calculated using serum creatinine. The regions of interest included cortex for both kidneys. The diffusivity-T2 spectrum was assessed considering three compartments (threshold: T2 30-40 msec, diffusivity 5-9μm2 /msec, according to visible peaks): A (low diffusivity, short T2), B (low diffusivity, long T2), and C (high diffusivity). Volume fractions Vi (i=A, B, C) were calculated. Intra-class coefficient (ICC, >0.6 as good) to assess inter-reader agreement of DR-CSI Vi . Spearman's correlation to assess relationship of Vi to IFL and eGFR. Receiver operating characteristic analyses with the area under the curve (AUC) to discriminate patients with moderate-severe fibrosis from mild ones. Statistical significance criteria: P-value <0.05. ICCs were good for all Vi . Correlations were found between IFL and VB (r=0.424, significant) and VC (r=-0.400, significant), and between eGFR and VB (r=-0.303, P=0.058) and VC (r=0.487, significant). Regarding VB and VC , the AUCs were 0.903 and 0.824. DR-CSI help distinguish patients with moderate or severe renal interstitial fibrosis from mild ones. 2 Technical Efficacy: Stage 2.

Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases

Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-based diagnosis in patients with uterine sarcomas. Fifteen sequences of MRI for patients (uterine sarcoma group: n = 63; uterine leiomyoma: n = 200) were used to train the models. Six radiologists (three specialists, three practitioners) interpreted the same images for validation. The most important individual sequences for diagnosis were axial T2-weighted imaging (T2WI), sagittal T2WI, and diffusion-weighted imaging. These sequences also represented the most accurate combination (accuracy: 91.3%), achieving diagnostic ability comparable to that of specialists (accuracy: 88.3%) and superior to that of practitioners (accuracy: 80.1%). Moreover, radiologists’ diagnostic accuracy improved when provided with DNN results (specialists: 89.6%; practitioners: 92.3%). Our DNN models are valuable to improve diagnostic accuracy, especially in filling the gap of clinical skills between interpreters. This method can be a universal model for the use of deep learning in the diagnostic imaging of rare tumors.