Conventional MR Research Articles

Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging

To establish a deep learning (DL) model in differentiating borderline ovarian tumor (BOT) from epithelial ovarian cancer (EOC) on conventional MR imaging. We retrospectively enrolled 201 patients of 102 pathologically proven BOTs and 99 EOCs at OB/GYN hospital Fudan University, between January 2015 and December 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) MR images were used for lesion area determination. We trained a U-net++ model with deep supervision to segment the lesion area on MR images. Then, the segmented regions were fed into a classification model based on DL network to categorize ovarian masses automatically. For ovarian lesion segmentation, the mean dice similarity coefficient (DSC) of the trained U-net++ model in the testing dataset achieved 0.73 pm 0.25, 0.76 pm 0.18, and 0.60 pm 0.24 in the sagittal T2WI, coronal T2WI, and axial T1WI images, respectively. The DL model by combined T2WI computerized network could differentiate BOT from EOC with a significantly higher AUC of 0.87, an accuracy of 83.7%, a sensitivity of 75.0% and a specificity of 87.5%. In comparison, the AUC yielded by radiologist was only 0.75, with an accuracy of 75.5%, a sensitivity of 96.0% and specificity of 54.2% (P < 0.001).The trained DL network model derived from routine MR imaging could help to distinguish BOT from EOC with a high accuracy, which was superior to radiologists’ assessment.

Image quality using synthetic brain MRI: an age-stratified study.

Synthetic magnetic resonance imaging (MRI) might replace the conventional MR sequences in brain evaluation to shorten scan time and obtain multiple quantitative parameters. To evaluate the image quality of multiple-delay-multiple-echo (MDME) sequence-derived synthetic brain MR images compared to conventional images by considering a multi-age sample. Image sets of conventional and synthetic MRI of 200 participants were included. On the basis of the presence of intracranial lesions, the participants were divided into a normal group and a pathological group. Two neuroradiologists compared the anonymous and unordered images. Image quality, artifacts, and diagnostic performance were analyzed. In the quantitative analysis, comparing with conventional images, MDME sequence-derived synthetic MRI demonstrated an equal/greater signal-to-noise ratio and contrast-to-noise ratio (CNR) in all age groups. Specifically, for participants aged ≤2 years, synthetic T2-fluid-attenuated inversion recovery imaging showed a significantly higher cerebellum gray/white matter CNR (P < 0.05). In the qualitative and artifact analyses, except for the superior sagittal sinus and cranial nerves, synthetic MRI showed good imaging quality (≥3 points) in all brain structures. On synthetic T1-weighted imaging, high signal intensity within the superior sagittal sinus was found in most of our participants (107/118, 90.7%). No difference was observed between synthetic and conventional MRI in diagnosing the lesions. MDME sequence-derived synthetic MRI showed similar image quality and diagnostic performance with a shorter acquisition time than conventional MRI. However, the high signal intensity within the superior sagittal sinus on synthetic T1-weighted images requires consideration.

MRI-Based Radiomics Combined with Deep Learning for Distinguishing IDH-Mutant WHO Grade 4 Astrocytomas from IDH-Wild-Type Glioblastomas.

This study aimed to investigate the potential of quantitative radiomic data extracted from conventional MR images in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type glioblastomas (GBMs). A cohort of 57 treatment-naïve patients with IDH-mutant grade 4 astrocytomas (n = 23) and IDH-wild-type GBMs (n = 34) underwent anatomical imaging on a 3T MR system with standard parameters. Post-contrast T1-weighted and T2-FLAIR images were co-registered. A semi-automatic segmentation approach was used to generate regions of interest (ROIs) from different tissue components of neoplasms. A total of 1050 radiomic features were extracted from each image. The data were split randomly into training and testing sets. A deep learning-based data augmentation method (CTGAN) was implemented to synthesize 200 datasets from the training sets. A total of 18 classifiers were used to distinguish two genotypes of grade 4 astrocytomas. From generated data using 80% training set, the best discriminatory power was obtained from core tumor regions overlaid on post-contrast T1 using the K-best feature selection algorithm and a Gaussian naïve Bayes classifier (AUC = 0.93, accuracy = 0.92, sensitivity = 1, specificity = 0.86, PR_AUC = 0.92). Similarly, high diagnostic performances were obtained from original and generated data using 50% and 30% training sets. Our findings suggest that conventional MR imaging-based radiomic features combined with machine/deep learning methods may be valuable in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type GBMs.

Whole-tumor amide proton transfer-weighted imaging histogram analysis to predict pathological extramural venous invasion in rectal adenocarcinoma: a preliminary study.

To evaluate amide proton transfer-weighted (APTw)-derived whole-tumor histogram analysis parameters in predicting pathological extramural venous invasion (pEMVI) positive status of rectal adenocarcinoma (RA). Preoperative MR including APTw imaging of 125 patients with RA (mean 61.4 ± 11.6years) were retrospectively analyzed. Two radiologists reviewed each case's EMVI status based on the MR-based modified 5-point scale system with conventional MR images. The APTw histogram parameters of primary tumors were obtained automatically using whole-tumor volume histogram analysis. The independent risk factors markedly correlated with pEMVI-positive status were assessed using univariate and multivariate logistic regression analyses. Diagnosis performance was assessed by receiver operating characteristic curve (ROC) analysis. The AUCs were compared using the Delong method. Univariate analysis demonstrated that MR-tumor (T) stage, MR-lymph node (N) stage, APTw-10%, APTw-90%, interquartile range, APTw-minimum, APTw-maximum, APTw-mean, APTw-median, entropy, kurtosis, mean absolute deviation (MAD), and robust MAD were significantly related to pEMVI-positive status (all p < 0.05). Multivariate analysis demonstrated that MR-T stage (OR = 4.864, p = 0.018), MR-N stage (OR = 4.967, p = 0.029), interquartile range (OR = 0.892, p = 0.037), APT-minimum (OR = 1.046, p = 0.031), entropy (OR = 11.604, p = 0.006), and kurtosis (OR = 1.505, p = 0.007) were the independent risk factors enabling prediction of pEMVI-positive status. The AUCs for diagnostic ability of conventional MRI assessment, the APTw histogram model, and the combined model (including APTw histogram and clinical variables) were 0.785, 0.853, and 0.918, respectively. The combined model outperformed the APTw histogram model (p = 0.013) and the conventional MRI assessment (p = 0.006). Whole-tumor histogram analysis of APTw images combined with clinical factors showed better diagnosis efficiency in predicting EMVI involvement in RA. • Rectal adenocarcinomas with pEMVI-positive status are typically associated with higher APTw-SI values. • APTw-minimum, interquartile range, entropy, kurtosis, MR-T stage, and MR-N stage are the independent risk factors for EMVI involvement. • The best prediction for EMVI involvement was obtained with a combined model of APTw histogram and clinical variables (area under the curve, 0.918).

Magnetic resonance imaging radiomics to differentiate ovarian sex cord-stromal tumors and primary epithelial ovarian cancers.

To evaluate the diagnostic ability of magnetic resonance imaging (MRI) based radiomics and traditional characteristics to differentiate between Ovarian sex cord-stromal tumors (SCSTs) and epithelial ovarian cancers (EOCs). We consecutively included a total of 148 patients with 173 tumors (81 SCSTs in 73 patients and 92 EOCs in 75 patients), who were randomly divided into development and testing cohorts at a ratio of 8:2. Radiomics features were extracted from each tumor, 5-fold cross-validation was conducted for the selection of stable features based on development cohort, and we built radiomics model based on these selected features. Univariate and multivariate analyses were used to identify the independent predictors in clinical features and conventional MR parameters for differentiating SCSTs and EOCs. And nomogram was used to visualized the ultimately predictive models. All models were constructed based on the logistic regression (LR) classifier. The performance of each model was evaluated by the receiver operating characteristic (ROC) curve. Calibration and decision curves analysis (DCA) were used to evaluate the performance of models. The final radiomics model was constructed by nine radiomics features, which exhibited superior predictive ability with AUCs of 0.915 (95%CI: 0.869-0.962) and 0.867 (95%CI: 0.732-1.000) in the development and testing cohorts, respectively. The mixed model which combining the radiomics signatures and traditional parameters achieved the best performance, with AUCs of 0.934 (95%CI: 0.892-0.976) and 0.875 (95%CI: 0.743-1.000) in the development and testing cohorts, respectively. We believe that the radiomics approach could be a more objective and accurate way to distinguish between SCSTs and EOCs, and the mixed model developed in our study could provide a comprehensive, effective method for clinicians to develop an appropriate management strategy.

MRI of unruptured infectious intracranial aneurysms in infective endocarditis. A case-control study

PurposeTo evaluate the usefulness of T2* and FLAIR sequences in the detection of unruptured infectious intracranial aneurysms (UIIAs) in infective endocarditis (IE) including the relationships between the lesion patterns within subarachnoid spaces and the presence of UIIA. MethodsRetrospective review of 15 consecutive patients with definite IE undergoing MR imaging (FLAIR, T2*, DWI, CE-MRA, 3D-T1, CE-3DT1 sequences), in whom DSA detected infectious intracranial aneurysms (IIA). Aneurysmal features (diameter, location, morphology on DSA) and signal patterns onT2*, FLAIR and conventional MR sequences at the site of the UIIA, follow-up MRI and IE background, were analyzed.A control-group of 15 IE-patients without IIA at DSA served for comparison. ResultsAmong 17 UIIAs studied, T2* sequence displayed a susceptibility vessel sign in 15/17 (88.2%), both distal and proximal, which matched with the IIA visualized on DSA. Three patterns of hyposignal areas were identified: (a) signet-ring or target-sign appearance (n = 7), (b) homogeneous, round-, oval- or pear-shaped area (n = 4), and (c) heterogeneous area (n = 4). A FLAIR hyperintensity of the lumen and of the adjacent cortex was present in 6 (35.3%) and 9 (53%) UIIAs, respectively. On T1 (12 UIIAs) a rounded hyposignal (n = 2), within the UIIA lumen matched with the FLAIR hypersignal.Using both T2* and FLAIR had an incremental value with 100% sensitivity and specificity. ConclusionThe susceptibility vessel sign is an MR imaging pattern frequently observed at the site of UIIAs in IE-patients.Both T2* and FLAIR may have the potential to depict UIIAs, regardless of their location and shape.

Considerations for hyperpolarized 13 C MR at reduced field: Comparing 1.5T versus 3T.

In contrast to conventional MR, signal-to-noise ratio (SNR) is not linearly dependent on field strength in hyperpolarized MR, as polarization is generated outside the MR system. Moreover, field inhomogeneity-induced artifacts and other practical limitations associated with field strengths 3T are alleviated at lower fields. The potential of hyperpolarized C spectroscopy and imaging at 1.5T versus 3T is demonstrated in silico, in vitro, and in vivo for applications on clinical MR systems. Theoretical noise and SNR behavior at different field strengths are investigated based on simulations. A thorough field comparison between 1.5T and 3T is performed using thermal and hyperpolarized C spectroscopy and imaging. Cardiac in vivo data is obtained in pigs using hyperpolarized [1- C]pyruvate spectroscopy and imaging at 1.5T and 3T. Based on theoretical considerations and simulations, the SNR of hyperpolarized MR at identical acquisition bandwidths is independent of the field strength for typical coil setups, while adaptively changing the acquisition bandwidth proportional to the static magnetic field allows for net SNR gains of up to 40% at 1.5T compared to 3T. In vitro C data verified these considerations with less than 7% deviation. In vivo feasibility of hyperpolarized [1- C]pyruvate dynamic metabolic spectroscopy and imaging at 1.5T is demonstrated in the pig heart with comparable SNR between 1.5T and 3T while B artifacts are noticeably reduced at 1.5T. Hyperpolarized C MR at lower field strengths is favorable in terms of SNR and off-resonance effects, which makes 1.5T a promising alternative to 3T, especially for clinical cardiac metabolic imaging.

ROLE OF DIFFUSION WEIGHTED IMAGING AND ADC VALUES IN CHARACTERISING BREAST LESIONS ON MR MAMMOGRAPHY

Background- Dynamic contrast enhanced MRI (DCE MR) is an important imaging modality to detect and evaluate the breast lesions with high sensitivity. Aim of study was to characterize breast lesions detect Objective- ed on MR mammography by diffusion weighted imaging and quantify ADC values to differentiate benign and malignant mass and non mass lesions. Methods- This prospective study was performed in thirty female patients on 1.5T GE Healthcare HDXT MRI machine. Thirty three breast lesions were included in this study. Final diagnosis was correlated with histopathological diagnosis. Out of total thirty three lesions, mean ADC value Results- of malignant lesions was 1.01 ± 0.24 x 10-3mm2/sec and for benign lesions was 1.48 ± 0.29 x 10-3 mm2/sec. The calculated ADC cut off value of 1.22x 10-3 mm2/sec gave a sensitivity of 86.96% and specicity of 90% to differentiate malignant and benign breast lesions. As a Conclusion- conclusion Diffusion weighted imaging and quantitative analysis with ADC values can be used with conventional DCE MR to differentiate benign and malignant breast lesions with high sensitivity and specicity

A Deep Learning Model for Preoperative Differentiation of Glioblastoma, Brain Metastasis, and Primary Central Nervous System Lymphoma: An External Validation Study

(1) Background: Neuroimaging differentiation of glioblastoma, primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (BM) represents a diagnostic and therapeutic challenge in neurosurgical practice, expanding the burden of care and exposing patients to additional risks related to further invasive procedures and treatment delays. In addition, atypical cases and overlapping features have not been entirely addressed by modern diagnostic research. The aim of this study was to validate a previously designed and internally validated ResNet101 deep learning model to differentiate glioblastomas, PCNSLs and BMs. (2) Methods: We enrolled 126 patients (glioblastoma: n = 64; PCNSL: n = 27; BM: n = 35) with preoperative T1Gd-MRI scans and histopathological confirmation. Each lesion was segmented, and all regions of interest were exported in a DICOM dataset. A pre-trained ResNet101 deep neural network model implemented in a previous work on 121 patients was externally validated on the current cohort to differentiate glioblastomas, PCNSLs and BMs on T1Gd-MRI scans. (3) Results: The model achieved optimal classification performance in distinguishing PCNSLs (AUC: 0.73; 95%CI: 0.62–0.85), glioblastomas (AUC: 0.78; 95%CI: 0.71–0.87) and moderate to low ability in differentiating BMs (AUC: 0.63; 95%CI: 0.52–0.76). The performance of expert neuro-radiologists on conventional plus advanced MR imaging, assessed by retrospectively reviewing the diagnostic reports of the selected cohort of patients, was found superior in accuracy for BMs (89.69%) and not inferior for PCNSL (82.90%) and glioblastomas (84.09%). (4) Conclusions: We investigated whether the previously published deep learning model was generalizable to an external population recruited at a different institution—this validation confirmed the consistency of the model and laid the groundwork for future clinical applications in brain tumour classification. This artificial intelligence-based model might represent a valuable educational resource and, if largely replicated on prospective data, help physicians differentiate glioblastomas, PCNSL and solitary BMs, especially in settings with limited resources.

Zero Echo Time vs. T1-Weighted MRI for Assessment of Cortical and Medullary Bone Morphology Abnormalities Using CT as the Reference Standard.

Conventional MR pulse sequences result in poor signal from low T2 cortical bone because the minimum achievable echo time is limited. A sequence resulting in improved bone contrast is desirable. To evaluate the image quality and diagnostic performance of grayscale inversion zero echo time imaging (GI-ZTE) and grayscale inversion T1-weighted imaging (GI-T1WI) compared with computed tomography (CT). Prospective. A total of 50 patients with musculoskeletal tumors or tumor-like diseases of the lower extremities having MRI and CT studies. GI-T1WI and GI-ZTE sequences at 1.5T. Assessed cortical and medullary bone morphology abnormalities using CT as the reference standard. Three radiologists scored the images quality and recorded nine metrics to assess the diagnostic performance. Differences in image quality were calculated using the Wilcoxon signed-rank test. The intraclass correlation coefficient (ICC) was used to analyze the agreement of quantitative lesion parameters between CT and MR sequences, as well as the interobserver reliability. A P value <0.05 was considered statistically significant. Image quality score was significantly higher for CT images than GI-TIWI images. Except for radiologist 3 [4(0) vs 4 (1)], there was no significant difference in scores between CT and GI-ZTE [radiologist 1: 4 (0) vs 4 (0), P=0.133; radiologist 2: 4 (0) vs 4 (0), P=0.085]. There was good-excellent agreement between both MR sequences and CT for size, lesion number, location, sclerotic rim, expanded shell, destruction pattern, and matrix mineralization for all radiologists (ICC: 0.636-1.000). The consistency of periosteal reaction and penetration of the cortex was fair to good (0.481-0.729) between GI-T1WI and CT and good to excellent between GI-ZTE and CT (0.682-0.852). GI-ZTE images had superior intermodality agreement with CT images and allowed visualization of more cortical bone detail than GI-T1WI images. 1. Stage 2.

Venous thrombosis: a mimic of musculoskeletal injury on MR imaging.

Clinical signs and symptoms of venous thrombosis and musculoskeletal pathologies frequently overlap. Sometimes, patients with venous thrombosis undergo MR examinations under an equivocal suspicion of muscle, tendon or articular injury. A low pretest clinical suspicion and lack of familiarity with the conventional MR imaging signs of venous thrombosis may result in failure to diagnose venous thrombosis, delaying treatment and raising morbimortality. In MR imaging, thrombosis presents as venous ectasia with intraluminal heterogeneous content. Small vein thrombosis is often identified as having a branching aspect. Perivenous edema and inflammatory soft tissue changes may be the most prominent findings. The purpose of this paper is to illustrate MR findings of venous thrombosis in patients who underwent MR examinations due to suspected musculoskeletal pathologies. Cases of venous thrombosis in different sites of the body diagnosed through MR are presented.

Accelerated three-dimensional susceptibility weighted imaging of the whole spine of fetus at 3T

ObjectivesTo investigate the image quality and capability of generalized auto-calibrating partially parallel acquisition (GRAPPA) accelerated Three-dimensional (3D) susceptibility weighted imaging (SWI) of the whole spine at 3T. MethodsA total of 37 pregnant women (gestation age 22 to 39 weeks, average 29 ± 3 weeks) with suspected fetal vertebral anomalies by ultrasound (US) screening underwent 3.0T MR imaging with 3D SWI, conventional two-dimensional (2D) half-flourier acquisition single-shot turbo spin-echo (HASTE) and 3D true fast imaging with steady-state precession (True FISP). The acquisition time of each protocol was recorded. Signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) were determined in representative interest regions of fetal thoracic vertebrae and compared among three pulse sequences. Two radiologists rated image quality independently in random order on a 5-point scale. Kappa coefficients were computed to assess inter-observer reliability. Receiver operating characteristic curves were generated, and the area under the curve (AUC) was used to compare the diagnostic performance of each protocol in vertebral deformities. ResultsThe acquisition time was 15 s for 3D-SWI and 17 s for 3D True FISP, significantly shorter than conventional HASTE (37 s; both P < 0.01).Of the three protocols, The SNR was highest on 3D True FISP, while the CNR was highest on 3D SWI. Visualization of all segments of the whole spine by 3D SWI was comparable with 3D True FISP. In contrast, 3D SWI and 3D True FISP depicted cervical and sacrococcygeal vertebrae better than HASTE. The weighted kappa statistic was 0.70–0.89 to evaluate the image quality of all segments of the whole spine, indicating good to excellent interobserver agreement. 3D SWI had the highest diagnostic performance for detecting fetal vertebral anomalies (AUC = 0.92). Conclusions3D-SWI is feasible for improved visualization of the whole fetal vertebral column and its congenital malformations with adequate image quality and high accuracy, thereby providing a supplementary method to conventional MR imaging.

MRI Radiomics Signature of Pediatric Medulloblastoma Improves Risk Stratification Beyond Clinical and Conventional MR Imaging Features.

Prognostic evaluation is important for personalized treatment in children with medulloblastoma (MB). Limited data are available for risk stratification using a radiomics-based model. To evaluate the incremental value of an MRI radiomics signature in stratifying the risk of pediatric MB in terms of overall survival (OS). Retrospective. A total of 111 children (mean age 5.82 years) with pathologically confirmed MB divided into training and validation cohorts (77 and 34 children, respectively). A 3 T, contrast-enhanced T1-weighted imaging with inversion recovery. The study endpoint was OS defined as the time between the preoperative MRI study and death or last follow-up. The radiomics signature model and a clinical-MRI model were developed for personalized OS prediction. An integrative model, which combined the radiomics signature and clinical-MRI features, was also built using multivariable Cox regression model. The performance of the three models was evaluated with the C-index. The performance of integrative model was assessed by calibration curve and decision curve analysis (DCA). Independent T-test, Mann-Whitney U test, Fisher's exact tests or chi-square test, logistic regression analysis, Kaplan-Meier survival analysis, C-index, intraclass correlation coefficients (ICC). P < 0.05 was considered statistically significant. The media OS was 2.83 years (3.87 ± 1.85 years). Two clinical and one conventional MR imaging features (remnant, adjuvant treatment, and peritumoral edema) were selected for clinical-MRI model building. The integrative model evaluated OS (C-index 0.823) better than either the radiomics signature (C-index 0.702) or the clinical-MRI model (C-index 0.771). And it also showed good performance in the validation cohort (C-indices: 0.786, 0.756, 0.721), which was validated by the good calibration (P >0.05) and more benefit. This study demonstrated that the integrative model, which combined radiomics signature, clinical, and conventional MRI features, showed best performance in OS evaluation for children with MB. The radiomics signature may confer incremental value over clinical-MRI features. 3. Stage 2.

Value of multimodal MRI radiomics and machine learning in predicting staging liver fibrosis and grading inflammatory activity.

To evaluate the value of radiomics models created based on non-contrast enhanced T1 weighted (T1W) and T2W fat-saturated (T2WFS) images for staging hepatic fibrosis (HF) and grading inflammatory activity. Data of 280 patients with pathologically confirmed HF and 48 healthy volunteers were included. The participants were divided into the training set and the test set at the proportion of 4:1 by the random seed method. We used the Pyradiomics software to extract radiomics features, and then use the least absolute shrinkage and selection operator to select the optimal subset. Finally, we used the stochastic gradient descent classifier to build the prediction models. DeLong test was used to compare the diagnostic performance of the models. Receiver operating characteristics was used to evaluate the prediction ability of the models. The diagnostic efficiency of the models based on T1W & T2WFS images were the highest (all p < 0.05). When discriminating significant fibrosis (≥ F2), there were significant differences in the AUCs between the machine learning models based on T1W and T2WFS images (p < 0.05), but there were no significant differences in area under the receiver operating characteristic curves between the two models in other groups (all p > 0.05). The radiomics models built on T1W and T2WFS images are effective in assessing HF and inflammatory activity. Based on conventional MR sequences that are readily available in the clinic, namely unenhanced T1W and T2W images. Radiomics can be used for diagnosis and differential diagnosis of liver fibrosis staging and inflammatory activity grading.

Ultrashort echo time magnetic resonance imaging of the osteochondral junction.

Osteoarthritis is a common chronic degenerative disease that causes pain and disability with increasing incidence worldwide. The osteochondral junction is a dynamic region of the joint that is associated with the early development and progression of osteoarthritis. Despite the substantial advances achieved in the imaging of cartilage and application to osteoarthritis in recent years, the osteochondral junction has received limited attention. This is primarily related to technical limitations encountered with conventional MR sequences that are relatively insensitive to short T2 tissues and the rapid signal decay that characterizes these tissues. MR sequences with ultrashort echo time (UTE) are of great interest because they can provide images of high resolution and contrast in this region. Here, we briefly review the anatomy and function of cartilage, focusing on the osteochondral junction. We also review basic concepts and recent applications of UTE MR sequences focusing on the osteochondral junction.

NIMG-05. DYNAMIC SUSCEPTIBILITY CONTRAST MRI IN THE EVALUATION OF BRAIN METASTASES: CURRENT PRACTICE REVIEW AND RATIONALE FOR STUDY OF BASELINE PERFUSION IMAGING PRIOR TO STEREOTACTIC RADIOSURGERY (STARBEAM-X)

Abstract Stereotactic radiosurgery (SRS) is an established focal treatment for brain metastases with high local control rates. An important side-effect of SRS is the development of radionecrosis. On conventional MR imaging, radionecrosis and tumour progression often have similar appearances, but have contrasting management approaches. Perfusion MR imaging is often used in the post-treatment setting in order to help distinguish between the two, but image interpretation can be fraught with challenges.Perfusion MR plays an established role in the baseline and post-treatment evaluation of primary brain tumours and a number of studies have concentrated on the value of perfusion imaging in brain metastases. Of the parameters generated, relative cerebral blood volume is the most widely used variable in terms of its clinical value in differentiating between radionecrosis and tumour progression.Although it has been suggested that the rCBV tends to be elevated in active metastatic disease following treatment with radiosurgery, but not with treatment-related changes, the literature available on interpretation of the ratios provided in the context of defining tumour progression is not consistent.We provide an overview of the role perfusion MRI plays in the assessment of brain metastases and introduces the rationale for the STARBEAM-X study (Study of assessment of radionecrosis in brain metastases using MR perfusion extra imaging), which will prospectively evaluate baseline perfusion imaging in brain metastases. We present here the results of the first three patients recruited to the feasibility phase of this study. We hope this study will allow insight into the vascular appearance of metastases from different primary sites, and aid in the interpretation of post-treatment perfusion imaging.

Risk Factors for Cerebrovascular Events in Moyamoya Angiopathy Using 4D Flow MRI: A Pilot Study.

Little is known about internal carotid artery (ICA) hemodynamics in patients with moyamoya angiopathy (MMA) and its role in cerebrovascular events. To characterize ICA hemodynamics in MMA patients by 4D flow MRI and investigate its relationship with cerebrovascular events. Prospective. Seventy MMA patients (50 years old ± 9, 30 males). Time-resolved three-directional velocity encoded fast field echo sequence (4D flow) MRI, T1-weighted fast field echo sequence, T2 weighted turbo spin echo sequence, diffusion weighted echo planar imaging; T2-weighted fluid-attenuated inversion recovery turbo spin echo sequence, susceptibility weighted fast field echo sequence, and time-of-flight MR angiography fast field echo sequence at 3.0T. ICA hemodynamics (maximum and average velocity [Vmax , Vavg ], average blood flow [Flowavg ], and wall shear stress) were analyzed based on 4D flow data. Cerebral infarction, defined as the occurrence of events, in 124 brain hemispheres was determined according to clinical symptoms and conventional brain MR imaging. The independent-samples T test was used to evaluate differences in ICA hemodynamics between infarcted and non-infarcted hemispheres. Binary logistic regression was performed to investigate the relationship between ICA hemodynamics and events. A P value < 0.05 was considered statistically significant. Sixty-one infarcted hemispheres (eight hemispheres with acute ischemic damage, 30 with chronic ischemic damage, and 23 with chronic hemorrhagic damage) had cerebrovascular events and 63 non-infarcted hemispheres did not. The hemodynamic parameters in the infarcted hemispheres (Vmax : P < 0.001; Vavg : P=0.003; and Flowavg : P=0.004) were significantly lower than those in the non-infarcted hemispheres. However, Vmax (P=0.052), Vavg (P=0.107), and Flowavg (P=0.074) were not significantly different among hemispheres with acute ischemic lesions, chronic ischemic lesions and chronic hemorrhagic lesions. Vmax (odds ratio 3.033, 95% CI: 1.075-8.562) was independently associated with cerebrovascular events. Vmax maybe a higher risk factor of cerebrovascular events in MMA patients. 2 TECHNICAL EFFICACY STAGE: 3.

Value of diffusion-weighted magnetic resonance imaging (DWI) in differentiating orbital lymphoma from idiopathic orbital inflammatory pseudotumor

BackgroundDiffusion-weighted MR imaging can provide physiological information complementing morphological findings from conventional MRI. It detects early tissue changes associated with changes in water content, such as changes in the permeability of cell membranes, cell swelling or cell lysis. Areas of diseased tissue are highlighted with increased signal intensity on diffusion-weighted MR imaging. A decrease in the ADC is expected with increased intracellular tissue caused by either cell swelling or increased cellular density. DWI can be performed without the need for the administration of exogenous contrast medium, so it may of use when contrast administration is contraindicated. It yields quantitative and qualitative information that reflects changes at the cellular level and indicates the integrity of cell membranes. The purpose of this study was mainly to assess the diagnostic value of DWI for the discrimination of orbital lymphoma from idiopathic orbital inflammatory pseudotumor.ResultsOf our 53 cases presented with proptosis or visual disturbances, 32 cases (60.4%) had found to be present with idiopathic orbital inflammatory pseudotumor and 21 cases (39.6%) had orbital lymphoma. On conventional MR imaging, ill-defined tumor margin and orbital preseptal space involvement had a significant association with orbital lymphoma, whereas intense post-contrast enhancement of lesion and radiologic evidence of sinusitis were associated with orbital inflammatory pseudotumor. The mean ADC value of orbital lymphoma was significantly lower than those of benign inflammatory pseudotumor, yielding 100% sensitivity, 99% specificity, and 90.5% accuracy for differentiating both entities.ConclusionsDiffusion-weighted MR imaging (DWI) is valuable in discriminating orbital inflammatory pseudotumor from malignant orbital lymphoma that help patients to initial management.

Making the invisible visible-ultrashort echo time magnetic resonance imaging: Technical developments and applications.

Magnetic resonance imaging (MRI) uses a large magnetic field and radio waves to generate images of tissues in the body. Conventional MRI techniques have been developed to image and quantify tissues and fluids with long transverse relaxation times (T2s), such as muscle, cartilage, liver, white matter, gray matter, spinal cord, and cerebrospinal fluid. However, the body also contains many tissues and tissue components such as the osteochondral junction, menisci, ligaments, tendons, bone, lung parenchyma, and myelin, which have short or ultrashort T2s. After radio frequency excitation, their transverse magnetizations typically decay to zero or near zero before the receiving mode is enabled for spatial encoding with conventional MR imaging. As a result, these tissues appear dark, and their MR properties are inaccessible. However, when ultrashort echo times (UTEs) are used, signals can be detected from these tissues before they decay to zero. This review summarizes recent technical developments in UTE MRI of tissues with short and ultrashort T2 relaxation times. A series of UTE MRI techniques for high-resolution morphological and quantitative imaging of these short-T2 tissues are discussed. Applications of UTE imaging in the musculoskeletal, nervous, respiratory, gastrointestinal, and cardiovascular systems of the body are included.

Differentiating between cardiac amyloidosis and hypertrophic cardiomyopathy on non-contrast cine-magnetic resonance images using machine learning-based radiomics.

This study aimed to determine whether texture analysis (TA) and machine learning-based classifications can be applied in differential diagnosis of cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) using non-contrast cine cardiac magnetic resonance (CMR) images. In this institutional review board-approved study, we consecutively enrolled 167 patients with CA (n = 85), HCM (n = 82), and 84 patients with normal CMR served as controls. All cases were randomized into training [119 patients (70%)] and validation [48 patients (30%)] groups. A total of 275 texture features were extracted from cine images. Based on regression analysis with the least absolute shrinkage and selection operator (LASSO), nine machine learning models were established and their diagnostic performance determined. Nineteen radiomics texture features derived from cine images were used to differentiate CA and HCM. In the validation cohort, the support vector machine (SVM), which had an accuracy of 0.85, showed the best performance (MCC = 0.637). Gray level non-uniformity (GLevNonU) was the single most effective feature. The combined model of radiomics texture features and conventional MR metrics had superior discriminatory performance (AUC = 0.89) over conventional MR metrics model (AUC = 0.79). Moreover, results showed that GLevNonU levels in HCM patients were significantly higher compared with levels in CA patients and control groups (P < 0.001). A cut-off of GLevNonU ≥ 25 was shown to differentiate between CA and HCM patients, with an area under the curve (AUC) of 0.86 (CI:0.804-0.920). Multiple comparisons tests showed that GLevNonU was significantly greater in LGE+, relative to LGE-patient groups (CA+ vs. CA- and HCM+ vs. HCM-, P = 0.01, 0.001, respectively). Machine learning-based classifiers can accurately differentiate between CA and HCM on non-contrast cine images. The radiomics-MR combined model can be used to improve the discriminatory performance. TA may be used to assess myocardial microstructure changes that occur during different stages of cardiomyopathies.