Short Tau Inversion Recovery Sequences Research Articles

Enhanced Myocardial Tissue Visualization: A Comparative Cardiovascular Magnetic Resonance Study of Gradient-Spin Echo-STIR and Conventional STIR Imaging.

This study is aimed at evaluating the efficacy of the gradient-spin echo- (GraSE-) based short tau inversion recovery (STIR) sequence (GraSE-STIR) in cardiovascular magnetic resonance (CMR) imaging compared to the conventional turbo spin echo- (TSE-) based STIR sequence, specifically focusing on image quality, specific absorption rate (SAR), and image acquisition time. In a prospective study, we examined forty-four normal volunteers and seventeen patients referred for CMR imaging using conventional STIR and GraSE-STIR techniques. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), image quality, T2 signal intensity (SI) ratio, SAR, and image acquisition time were compared between both sequences. GraSE-STIR showed significant improvements in image quality (4.15 ± 0.8 vs. 3.34 ± 0.9, p = 0.024) and cardiac motion artifact reduction (7 vs. 18 out of 53, p = 0.038) compared to conventional STIR. Furthermore, the acquisition time (27.17 ± 3.53 vs. 36.9 ± 4.08 seconds, p = 0.041) and the local torso SAR (<13% vs. <17%, p = 0.047) were significantly lower for GraSE-STIR compared to conventional STIR in short-axis plan. However, no significant differences were shown in T2 SI ratio (p = 0.141), SNR (p = 0.093), CNR (p = 0.068), and SAR (p = 0.071) between these two sequences. GraSE-STIR offers notable advantages over conventional STIR sequence, with improved image quality, reduced motion artifacts, and shorter acquisition times. These findings highlight the potential of GraSE-STIR as a valuable technique for routine clinical CMR imaging.

MRI-Based Vertebral Bone Quality Score Can Predict the Imminent New Vertebral Fracture After Vertebral Augmentation.

The incidence of imminent new vertebral fracture (NVF) is notably high after vertebral augmentation (VA), but accurately assessing the imminent risk of NVF remains a great challenge. The aim of this study was to investigate whether the MRI-based vertebral bone quality (VBQ) score can predict the risk of imminent NVF after VA within a 2-year period. A total of 135 patients age 50 years and older who suffered from painful osteoporotic vertebral compression fracture and treated with VA were enrolled in this retrospective study. Each patient's VBQ scores were calculated from T1-weighted, T2-weighted, and short tau inversion recovery sequences of preoperative lumbar MRI. The clinical factors and VBQ score were integrated to create a predictive model by using the logistic regression algorithm and visualize by nomogram. Receiver operating characteristic curve, calibration curve, and decision curve analyses were used to evaluate the predictive performance of the nomogram. The mean VBQ-T1WI and VBQ-T2WI scores of the NVF group were 4.61 ± 0.55 and 0.89 ± 0.14, respectively, which were significantly higher than those of the without NVF group (3.99 ± 0.54 and 0.79 ± 0.12, respectively, P < .001), as well as the VBQ-combined score (0.75 ± 1.30 vs -0.80 ± 1.26, P < .001), which is the combination of VBQ-T1WI and VBQ-T2WI scores. On multivariate analysis, the predictors of imminent NVF included age (odds ratio [OR] = 1.064, 95% CI = 1.009-1.122, P = .022), previous vertebral fracture (OR = 2.089, 95% CI = 0.888-4.915, P = .091), and VBQ-combined score (OR = 2.239, 95% CI = 1.529-3.279, P < .001). The nomogram achieved superior performance with an area under the receiver operating characteristic curve of 0.838 (95% CI: 0.773-0.904) in predicting the imminent NVF compared to the clinical factors or VBQ-combined score alone. The VBQ score obtained from lumbar MRI can be used to assess the VBQ and predict the imminent NVF after VA in patients with osteoporotic vertebral compression fracture.

Evaluation of Infantile Brachial Plexopathy Using 3T MRI and High-Resolution Ultrasound: Experience From a Tertiary Care Centre.

Currently, clinical assessment is the main tool for the evaluation of brachial plexus injury, complemented by electrophysiologic studies (EPS), and imaging studies whenever available. Imaging plays an important role as it enables the differentiation of pre-ganglionic and postganglionic injuries, and adds objectivity to presurgical evaluation. The primary objective was to evaluate the utility of magnetic resonance imaging (MRI) and high-resolution ultrasonography (USG) in the localization and characterization of brachial plexus injury in infants. In this prospective study, 34 infants with signs and symptoms of brachial plexus injury were evaluated by clinical examination, EPS, MRI, and USG. Imaging findings were correlated with intraoperative findings in infants who underwent surgical management. The association between EPS and MRI findings, and USG and MRI findings were assessed using Fisher's exact test. Semi-quantitative subjective analysis of various MRI sequences was done as well. The most common findings of preganglionic injury and postganglionic injury, in our study, were pseudomeningocele and nerve thickening, respectively. MRI detection of injuries had a significant association with EPS findings. All MRI-detected injuries had a muscle power of grade 3 or less. muscle. Three-dimensional (3D) short tau inversion recovery (STIR) sequence was found to be superior for detecting postganglionic injuries (P < 0.05). Imaging studies enable localization of the site of injury, determining the extent, and nature/morphology of injury. The gamut of findings obtained from MRI is far wider compared to that from USG. USG can be used as the first-line screening investigation.

Improved 3D DESS MR neurography of the lumbosacral plexus with deep learning and geometric image combination reconstruction.

To evaluate the impact of deep learning (DL) reconstruction in enhancing image quality and nerve conspicuity in LSP MRN using DESS sequences. Additionally, a geometric image combination (GIC) method to improve DESS signals' combination was proposed. Adult patients undergoing 3.0 Tesla LSP MRN with DESS were prospectively enrolled. The 3D DESS echoes were separately reconstructed with and without DL and DL-GIC combined reconstructions. In a subset of patients, 3D T2-weighted short tau inversion recovery (STIR-T2w) sequences were also acquired. Three radiologists rated 4 image stacks ('DESS S2', 'DESS S2 DL', 'DESS GIC DL' and 'STIR-T2w DL') for bulk motion, vascular suppression, nerve fascicular architecture, and overall nerve conspicuity. Relative SNR, nerve-to-muscle, -fat, and -vessel contrast ratios were measured. Statistical analysis included ANOVA and Wilcoxon signed-rank tests. p < 0.05 was considered statistically significant. Forty patients (22 females; mean age = 48.6 ± 18.5years) were enrolled. Quantitatively, 'DESS GIC DL' demonstrated superior relative SNR (p < 0.001), while 'DESS S2 DL' exhibited superior nerve-to-background contrast ratio (p value range: 0.002 to < 0.001). Qualitatively, DESS provided superior vascular suppression and depiction of sciatic nerve fascicular architecture but more bulk motion as compared to 'STIR-T2w DL'. 'DESS GIC DL' demonstrated better nerve visualization for several smaller, distal nerve segments than 'DESS S2 DL' and 'STIR-T2w DL'. Application of a DL reconstruction with geometric image combination in DESS MRN improves nerve conspicuity of the LSP, especially for its smaller branch nerves.

Prediction of the activity of early ankylosing spondylitis using radiomics texture analysis on STIR.

The study aimed to explore the value of texture analysis of radiomics based on the short tau inversion recovery (STIR) sequence to evaluate the activity of bone marrow oedema of sacroiliac joints in early AS. 43 patients with early AS whose data were randomly divided into the training cohort (n=116) and verification cohort (n=56) according to the ratio of 7:3. The optimal feature subsets were obtained by Mann-Whitney U-test, the minimum-Redundancy Maximum-Relevancy (mRMR), and then least absolute shrinkage and selection operator (LASSO) using these texture feature parameters, which were used to construct the final prediction model and obtained the Radscore. The ROC curve was performed to evaluate the performance of the model. The Spearman correlation test was used to analyse the correlation of various indicators. In the training cohort, to differentiate early AS sacroiliac joint bone marrow oedema between the active and stable groups, the AUCs of the Radscore, SPARCC and ADC were 0.81, 0.91, 0.78, respectively. In the validation cohort, the AUCs were 0.87, 0.89, 0.85. In the two cohorts, there were no significant differences in AUCs between values of the Radscore and SPARCC, ADC (p>0.05). There was a significant difference in AUC between SPARCC and ADC in the training cohort (p<0.05), with no statistical significance in the validation cohort (p>0.05). The correlations were all low between the Radscore values and the values of ESR, CRP, tI, ASDAS-ESR and ASDAS-CRP (p<0.05). Radiomics analysis based on STIR texture analysis has a good prediction for the evaluation of bone marrow oedema activity of sacroiliac joints in AS. It can be a new non-invasive and objective evaluation method for AS activity.

Management of Spontaneous Spinal CSF Leaks Using Noninvasive Dynamic MR Neurography: A Case Series

AbstractSpontaneous spinal cerebrospinal fluid (CSF) leak presents with orthostatic headache and may lead to formation of subdural collection. Invasive magnetic resonance imaging or computed tomography myelography is routinely used to detect these leaks. Noninvasive dynamic magnetic resonance neurography can also detect these leaks and confirm its resolution post treatment. It includes three-dimensional NerveView, a high-resolution short-tau inversion recovery sequence for better visualization of spinal nerves and leak sites. This article showcases the diagnostic and therapeutic journey of three patients with spontaneous spinal CSF leak all of whom were diagnosed with dynamic magnetic resonance neurography and treated successfully with autologous targeted epidural blood patch. In one case, epidural blood patch was repeated after 2 months for recurrence of the leak.

Can Short Tau Inversion Recovery (STIR) Imaging Be Used as a Stand-Alone Sequence To Assess a Perianal Fistulous Tract on MRI? A Retrospective Cohort Study Comparing STIR and T1-Post Contrast Imaging.

Perianal fistulas demand precise preoperative assessment for optimal surgical outcomes. MRI, using Short Tau Inversion Recovery (STIR) and T1-post contrast sequences, plays a crucial role in this evaluation.This retrospective cohort study compared STIR imaging's diagnostic efficacy with T1-post contrast sequences in identifying perianal fistulous tracts. The study investigated whether STIR imaging could serve as the sole diagnostic sequence, simplifying clinical practice. In a tertiary care hospital, 100 patients underwent pelvic MRI for suspected perianal fistulas. Radiologists independently evaluated STIR and T1-post contrast sequences for internal openings, tract extent, distinction, abscess presence, and tract type. Sensitivity, specificity, area under the curve (AUC), and Cohen's kappa analysis were used for diagnostic assessment. STIR imaging showed notable sensitivity (79.8-97.9%) and specificity (100%) for identifying internal openings and tracts. Combined with T1-post contrast, diagnostic accuracy improved significantly, with near-perfect AUC values. Kappa values indicated moderate to substantial agreement between radiological assessments and clinical diagnosis. The combined sequences achieved 100% sensitivity and specificity for tract visualization. STIR imaging presents promise as a singular diagnostic tool for perianal fistulas, especially when combined with T1-post contrast sequences. While offering potential clinical diagnosis simplifications, further studies are warranted to validate its utility and ensure comprehensive diagnostic accuracy.

Characterization of bone marrow lesions in axial spondyloarthritis using quantitative T1 mapping MRI.

Conventional magnetic resonance imaging (MRI) uses T1-weighted and short-tau inversion recovery (STIR) sequences to characterize bone marrow in axial spondyloarthritis. However, quantification is restricted to estimating the extent of lesions because signal intensities are highly variable both within individuals and across patients and MRI scanners. This study evaluates the performance of quantitative T1 mapping for distinguishing different types of bone marrow lesions of the sacroiliac joints. In this prospective study, 62 patients underwent computed tomography (CT) and MRI of the sacroiliac joints including T1, STIR, and T1 mapping. Bone marrow lesions were characterized by three readers and assigned to one of four groups: sclerosis, osteitis, fat lesions, and mixed marrow lesions. Relaxation times on T1 maps were compared using generalized estimating equations and receiver operating characteristics (ROC) analysis. A total of 119 lesions were selected (sclerosis: 38, osteitis: 27, fat lesions: 40; mixed lesions: 14). T1 maps showed highly significant differences between the lesions with the lowest values for sclerosis (1516±220 ms), followed by osteitis (1909±75 ms), and fat lesions (2391±200 ms); p<0.001. T1 mapping differentiated lesions with areas under the ROC curve of 99% (sclerosis vs. osteitis) and 100% (other comparisons). T1 mapping allows accurate characterization of sclerosis, osteitis, and fat lesions at the sacroiliac joint but only for homogeneous, non-mixed lesions. Thus, further sequence development is needed before implementation in clinical routine.

A deep neural network for MRI spinal inflammation in axial spondyloarthritis.

To develop a deep neural network for the detection of inflammatory spine in short tau inversion recovery (STIR) sequence of magnetic resonance imaging (MRI) on patients with axial spondyloarthritis (axSpA). A total 330 patients with axSpA were recruited. STIR MRI of the whole spine and clinical data were obtained. Regions of interests (ROIs) were drawn outlining the active inflammatory lesion consisting of bone marrow edema (BME). Spinal inflammation was defined by the presence of an active inflammatory lesion on the STIR sequence. The 'fake-color' images were constructed. Images from 270 and 60 patients were randomly separated into the training/validation and testing sets, respectively. Deep neural network was developed using attention UNet. The neural network performance was compared to the image interpretation by a radiologist blinded to the ground truth. Active inflammatory lesions were identified in 2891 MR images and were absent in 14,590 MR images. The sensitivity and specificity of the derived deep neural network were 0.80 ± 0.03 and 0.88 ± 0.02, respectively. The Dice coefficient of the true positive lesions was 0.55 ± 0.02. The area under the curve of the receiver operating characteristic (AUC-ROC) curve of the deep neural network was 0.87 ± 0.02. The performance of the developed deep neural network was comparable to the interpretation of a radiologist with similar sensitivity and specificity. The developed deep neural network showed similar sensitivity and specificity to a radiologist with four years of experience. The results indicated that the network can provide a reliable and straightforward way of interpreting spinal MRI. The use of this deep neural network has the potential to expand the use of spinal MRI in managing axSpA.

Detection of caries lesions using a water-sensitive STIR sequence in dental MRI

In clinical practice, diagnosis of suspected carious lesions is verified by using conventional dental radiography (DR), including panoramic radiography (OPT), bitewing imaging, and dental X-ray. The aim of this study was to evaluate the use of magnetic resonance imaging (MRI) for caries visualization. Fourteen patients with clinically suspected carious lesions, verified by standardized dental examination including DR and OPT, were imaged with 3D isotropic T2-weighted STIR (short tau inversion recovery) and T1 FFE Black bone sequences. Intensities of dental caries, hard tissue and pulp were measured and calculated as aSNR (apparent signal to noise ratio) and aHTMCNR (apparent hard tissue to muscle contrast to noise ratio) in both sequences. Imaging findings were then correlated to clinical examination results. In STIR as well as in T1 FFE black bone images, aSNR and aHTMCNR was significantly higher in carious lesions than in healthy hard tissue (p < 0.001). Using water-sensitive STIR sequence allowed for detecting significantly lower aSNR and aHTMCNR in carious teeth compared to healthy teeth (p = 0.01). The use of MRI for the detection of caries is a promising imaging technique that may complement clinical exams and traditional imaging.

Deep Learning Algorithm of the SPARCC Scoring System in SI Joint MRI.

The Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system is a sacroiliitis grading system. To develop a deep learning-based pipeline for grading sacroiliitis using the SPARCC scoring system. Prospective. The study included 389 participants (42.2-year-old, 44.6% female, 317/35/37 for training/validation/testing). A pretrained algorithm was used to differentiate image with/without sacroiliitis. 3-T, short tau inversion recovery (STIR) sequence, fast spine echo. The regions of interest as ground truth for models' training were identified by a rheumatologist (HYC, 10-year-experience) and a radiologist (KHL, 6-year-experience) using the Assessment of Spondyloarthritis International Society definition of MRI sacroiliitis independently. Another radiologist (YYL, 4.5-year-experience) solved the discrepancies. The bone marrow edema (BME) and sacroiliac region models were for segmentation. Frangi-filter detected vessels used as intense reference. Deep learning pipeline scored using SPARCC scoring system evaluating presence and features of BMEs. A rheumatologist (SCWC, 6-year-experience) and a radiologist (VWHL, 14-year-experience) scored using the SPARCC scoring system once. The radiologist (YYL) scored twice with 5-day interval. Independent samples t-tests and Chi-squared tests were used. Interobserver and intraobserver reliability by intraclass correlation coefficient (ICC) and Pearson coefficient evaluated consistency between readers and the deep learning pipeline. We evaluated the performance using sensitivity, accuracy, positive predictive value, and Dice coefficient. A P-value <0.05 was considered statistically significant. The ICC and the Pearson coefficient between the SPARCC scores from three readers and the deep learning pipeline were 0.83 and 0.86, respectively. The sensitivity in identifying BME and accuracy of identifying SI joints and blood vessels was 0.83, 0.90, and 0.88, respectively. The dice coefficients were 0.82 (sacrum) and 0.80 (ilium). The high consistency with human readers indicated that deep learning pipeline may provide a SPARCC-informed deep learning approach for scoring of STIR images in spondyloarthritis. 1 TECHNICAL EFFICACY: Stage 2.

Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI.

The purpose of this study is to analyze the texture characteristics of chronic non-bacterial osteomyelitis (CNO) bone lesions, identified as areas of altered signal intensity on short tau inversion recovery (STIR) sequences, and to distinguish them from bone marrow growth-related changes through Machine Learning (ML) and Deep Learning (DL) analysis. We included a group of 66 patients with confirmed diagnosis of CNO and a group of 28 patients with suspected extra-skeletal systemic disease. All examinations were performed on a 1.5 T MRI scanner. Using the opensource 3D Slicer software version 4.10.2, the ROIs on CNO lesions and on the red bone marrow were sampled. Texture analysis (TA) was carried out using Pyradiomics. We applied an optimization search grid algorithm on nine classic ML classifiers and a Deep Learning (DL) Neural Network (NN). The model's performance was evaluated using Accuracy (ACC), AUC-ROC curves, F1-score, Positive Predictive Value (PPV), Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE). Furthermore, we used Shapley additive explanations to gain insight into the behavior of the prediction model. Most predictive characteristics were selected by Boruta algorithm for each combination of ROI sequences for the characterization and classification of the two types of signal hyperintensity. The overall best classification result was obtained by the NN with ACC = 0.91, AUC = 0.93 with 95% CI 0.91-0.94, F1-score = 0.94 and PPV = 93.8%. Between classic ML methods, ensemble learners showed high model performance; specifically, the best-performing classifier was the Stack (ST) with ACC = 0.85, AUC = 0.81 with 95% CI 0.8-0.84, F1-score = 0.9, PPV = 90%. Our results show the potential of ML methods in discerning edema-like lesions, in particular by distinguishing CNO lesions from hematopoietic bone marrow changes in a pediatric population. The Neural Network showed the overall best results, while a Stacking classifier, based on Gradient Boosting and Random Forest as principal estimators and Logistic Regressor as final estimator, achieved the best results between the other ML methods.

Assessment of the Initial Diagnostic Accuracy of a Fragility Fracture of the Sacrum: A Study of 56 Patients.

Retrospective study. To investigate the clinical manifestations of a fragility fracture of the sacrum (FFS) and the factors that may contribute to a misdiagnosis. The number of patients diagnosed with FFS has increased because of extended life expectancy and osteoporosis. Patients with FFS may report nonspecific symptoms, such as back, buttock, groin, and/or leg pain, leading to a misdiagnosis and a delay in definitive diagnosis. Fifty-six patients (13 males and 43 females) with an average age of 80.2±9.2 years admitted to the hospital for FFS between 2006 and 2021 were analyzed retrospectively. The following patient data were collected using medical records: pain regions, a history of trauma, initial diagnoses, and rates of fracture detection using radiography, computed tomography (CT), and magnetic resonance imaging (MRI). Forty-one patients presented with low back and/or buttock pain, nine presented with groin pain, and 17 presented with thigh or leg pain. There was no history of trauma in 18 patients (32%). At the initial visit, 27 patients (48%) were diagnosed with sacral or pelvic fragility fractures. In contrast, 29 patients (52%) were initially misdiagnosed with lumbar spine disease (23 patients), hip joint diseases (three patients), and buttock bruises (three patients). Fracture detection rates for FFS were 2% using radiography, 71% using CT, and 93% using MRI. FFS was diagnosed definitively using an MRI with a coronal short tau inversion recovery (STIR) sequence. Some patients with FFS have leg pain with no history of trauma and are initially misdiagnosed as having lumbar spine disease, hip joint disease, or simple bruises. When these clinical symptoms are reported, we recommend considering FFS as one of the differential diagnoses and performing lumbar or pelvic MRIs, particularly coronal STIR images, to rule out FFS.

NIMG-07. LONG-TERM FOLLOW-UP OF SCHWANNOMA GROWTH BEHAVIOR IN ADULT SCHWANNOMATOSIS PATIENTS USING WHOLE-BODY MRI

Abstract BACKGROUND The schwannomatoses include NF2-related schwannomatosis (NF2-SWN, formerly known as “NF2”) and non-NF2-related schwannomatosis (e.g. SMARCB1- and LZTR1-related schwannomatosis) and are caused by distinct germline pathogenic variants on chromosome 22. Patients may develop cranial, peripheral, and/or spinal nerve schwannomas. Except for vestibular schwannomas, the long-term growth behavior of schwannomas is unknown but this is important to guide patient surveillance and selection for treatment. Whole-body MRI (WBMRI) can detect whole-body schwannoma burden. METHODS 16 NF2-SWN and 12 non-NF2-SWN patients with WBMRI acquired between 2007-2010 underwent a repeat WBMRI between 2018-2022. Non-vestibular schwannomas were segmented on short tau inversion recovery (STIR) sequences. Tumor volume was calculated using a 3D tumor quantification software (3DQI). Tumor growth and shrinkage were defined as a volume change ³20% over the entire study period. RESULTS Median time between scans was 10.3 years. Median age was 43.5 years at baseline and 54 years at follow-up. Excluding resected tumors, 102 schwannomas (49 NF2-SWN, 53 non-NF2-SWN) were analyzed. 62/102 (61%) tumors grew &amp;gt;20% (65% NF2-SWN, 57% non-NF2-SWN); median growth was 96% in NF2-SWN and 94% in non-NF2-SWN. 24/102 (24%) tumors shrank &amp;gt;20% (20% NF2-SWN, 26% non-NF2-SWN); median shrinkage was 49% in NF2-SWN and 40% in non-NF2-SWN. 29 new tumors developed between scans (16 NF2-SWN, 13 non-NF2-SWN). Of 32 NF2-SWN tumors exposed to systemic or radiation therapy, 63% grew and 31% shrank. Of 7 non-NF2-SWN tumors exposed to treatment, 29% grew and 42% shrank. 11 non-NF2-SWN tumors shrank spontaneously without treatment. Spontaneous tumor shrinkage was not observed in NF2-SWN. CONCLUSIONS About 60% of SWN-associated schwannomas grow significantly over a decade. Spontaneous shrinkage is only observed in non-NF2-SWN tumors whereas, in NF2-SWN patients, about 60% of tumors grow despite treatment, suggesting a more aggressive tumor phenotype in NF2-SWN. Correlation of MRI findings with patient-reported outcomes and hormone exposure history are ongoing.

Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis.

Magnetic resonance imaging (MRI) is important for the early detection of axial spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting sacroiliitis in patients with axSpA using MRI. This study included MRI examinations of patients who underwent semi-coronal MRI scans of the sacroiliac joints owing to chronic back pain with short tau inversion recovery (STIR) sequences between January 2010 and December 2021. Sacroiliitis was defined as a positive MRI finding according to the ASAS classification criteria for axSpA. We developed a two-stage framework. First, the Faster R-CNN network extracted regions of interest (ROIs) to localize the sacroiliac joints. Maximum intensity projection (MIP) of three consecutive slices was used to mimic the reading of two adjacent slices. Second, the VGG-19 network determined the presence of sacroiliitis in localized ROIs. We augmented the positive dataset six-fold. The sacroiliitis classification performance was measured using the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The prediction models were evaluated using three-round three-fold cross-validation. A total of 296 participants with 4,746 MRI slices were included in the study. Sacroiliitis was identified in 864 MRI slices of 119 participants. The mean sensitivity, specificity, and AUROC for the detection of sacroiliitis were 0.725 (95% CI, 0.705-0.745), 0.936 (95% CI, 0.924-0.947), and 0.830 (95%CI, 0.792-0.868), respectively, at the image level and 0.947 (95% CI, 0.912-0.982), 0.691 (95% CI, 0.603-0.779), and 0.816 (95% CI, 0.776-0.856), respectively, at the patient level. In the original model, without using MIP and dataset augmentation, the mean sensitivity, specificity, and AUROC were 0.517 (95% CI, 0.493-0.780), 0.944 (95% CI, 0.933-0.955), and 0.731 (95% CI, 0.681-0.780), respectively, at the image level and 0.806 (95% CI, 0.729-0.883), 0.617 (95% CI, 0.523-0.711), and 0.711 (95% CI, 0.660-0.763), respectively, at the patient level. The performance was improved by MIP techniques and data augmentation. An AI model was developed for the detection of sacroiliitis using MRI, compatible with the ASAS criteria for axSpA, with the potential to aid MRI application in a wider clinical setting.

MRI-detected intraosseous bone marrow edema recedes after effective therapy of periodontitis.

T2 STIR MRI sequences can detect preclinical changes associated with periodontal inflammation, i.e. intraosseous edema in the tooth-supporting bone. In this study, we assessed whether MRI can be used for monitoring periodontal disease. In a prospective cohort study, we examined 35 patients with periodontitis between 10/2018 and 04/2019 by using 3D isotropic T2-weighted short tau inversion recovery (STIR) and Fast Field Echo T1-weighted Black bone sequences. All patients received standardized clinical exams before and three months after non-surgical periodontal therapy. Bone marrow edema extent was quantified in the STIR sequence at 922 sites before and after treatment. Results were compared with standard clinical findings. Non-parametric statistical analysis was performed. Non-surgical periodontal treatment caused significant improvement in mean probing depth (p < 0.001) and frequency of bleeding on probing (p < 0.001). The mean depth of osseous edema per site was reduced from a median [IQR] of 2 [1, 3] mm at baseline to 1 [0, 3] mm, (p < 0.001). Periodontal treatment reduced the frequency of sites with edema from 35 to 24% (p < 0.01). The decrease of periodontal bone marrow edema, as observed with T2 STIR MR imaging, is indicative of successful periodontal healing. T2 STIR hyperintense bone marrow edema in the periodontal bone decreases after treatment and can therefore be used to evaluate treatment success. Furthermore, MRI reveals new options to depict hidden aspects of periodontitis. • T2 STIR hyperintense periodontal intraosseous edema was prospectively investigated in 35 patients with periodontitis before and after treatment and compared to clinical outcomes. • The frequency of affected sites was reduced from 35 to 24% (p < 0.001), and mean edema depth was reduced from a median [IQR] of 2 [1, 3] mm at baseline to 1 [0, 3] mm 3 months after treatment. (p < 0.001). • T2 STIR sequences can be used to monitor the posttreatment course of periodontitis.

Multimodal imaging-based material mass density estimation for proton therapy using supervised deep learning.

Mapping CT number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, MRI, and advanced dual-energy CT (DECT) to derive accurate patient mass density maps. Seven tissue substitute MRI phantoms were used for validation including adipose, brain, muscle, liver, skin, spongiosa, 45% hydroxyapatite (HA) bone. MRI images were acquired using T1 weighted Dixon and T2 weighted short tau inversion recovery sequences. Training inputs are from MRI and twin-beam dual-energy images acquired at 120 kVp with gold/tin filters. The feasibility investigation included an empirical model and four residual networks (ResNet) derived from different training inputs and strategies by PDMI framework. PRN-MR-DE and RN-MR-DE denote ResNet (RN) trained with and without a physics constraint (P) using MRI (MR) and DECT (DE) images. PRN-DE stands for RN trained with a physics constraint using only DE images. A retrospective study using institutional patient data was also conducted to investigate the feasibility of the proposed framework. For the tissue surrogate study, PRN-MR-DE, PRN-DE, and RN-MR-DE result in mean mass density errors: -0.72%/2.62%/-3.58% for adipose; -0.03%/-0.61%/-0.18% for muscle; -0.58%/-1.36%/-4.86% for 45% HA bone. The retrospective patient study indicated that PRN-MR-DE predicted the densities of soft tissue and bone within expected intervals based on the literature survey, while PRN-DE generated large density deviations. The proposed PDMI framework can generate accurate mass density maps using MRI and DECT images. The supervised learning can further enhance model efficacy, making PRN-MR-DE outperform RN-MR-DE. The patient investigation also shows that the framework can potentially improve proton range uncertainty with accurate patient mass density maps. PDMI framework is proposed for the first time to inform deep learning models by physics insights and leverage the information from MRI to derive accurate mass density maps.

Quantitative MRI Analysis of Brachial Plexus and Limb-Girdle Muscles in Upper Extremity Onset Amyotrophic Lateral Sclerosis.

Recent evidence highlights the potential of axonal degeneration as a biomarker for amyotrophic lateral sclerosis (ALS) detection. However, the diagnostic potential of peripheral nerve axon changes in ALS remains unclear. To evaluate the diagnostic performance of quantitative MRI of the brachial plexus and limb-girdle muscles (LGMs) in patients with upper extremity onset of ALS. Retrospective. 47 patients with upper extremity onset of ALS and 20 healthy volunteers. 3-T, three-dimensional sampling perfection with application-optimized contrasts using different flip angle evolutions with short-tau inversion recovery sequences, T2-weighted turbo spin-echo Dixon sequence. The cross-sectional area (CSA) and nerve-muscle T2 signal intensity ratio (nT2) of the bilateral brachial plexus as well as the CSA and fat fraction (FF) of the bilateral LGMs were assessed by two radiologists. Disease severity and clinical stage of ALS patients were assessed by two neurologists. Student's t-test, Wilcoxon rank-sum test, binary logistic regression, interclass correlation coefficient, receiver operating characteristic analysis, and correlation analysis were performed for MRI quantitative metrics and clinical variables. Significance level: P < 0.05. In the affected limbs of patients with ALS, the CSA of the brachial plexus roots, trunks, and cords and the nT2 values of the brachial plexus trunks were significantly smaller than in the healthy controls. In the LGMs, the affected limbs of ALS showed significantly smaller CSA and higher FF than controls. The model containing parameters such as brachial plexus trunk CSA, subscapularis CSA, infraspinatus CSA, and subscapularis FF had excellent diagnostic efficacy for ALS. Additionally, increased subscapularis FF and supraspinatus FF were correlated with disease severity, and subscapularis CSA was negatively correlated with the clinical stage. Brachial plexus thinning, LGM atrophy, and fatty infiltration might serve as MRI-derived biomarkers for ALS with upper extremity onset. 4 TECHNICAL EFFICACY: Stage 2.

Analysis of muscle magnetic resonance imaging of a large cohort of patient with VCP-mediated disease reveals characteristic features useful for diagnosis

BackgroundThe diagnosis of patients with mutations in the VCP gene can be complicated due to their broad phenotypic spectrum including myopathy, motor neuron disease and peripheral neuropathy. Muscle MRI guides the diagnosis in neuromuscular diseases (NMDs); however, comprehensive muscle MRI features for VCP patients have not been reported so far.MethodsWe collected muscle MRIs of 80 of the 255 patients who participated in the “VCP International Study” and reviewed the T1-weighted (T1w) and short tau inversion recovery (STIR) sequences. We identified a series of potential diagnostic MRI based characteristics useful for the diagnosis of VCP disease and validated them in 1089 MRIs from patients with other genetically confirmed NMDs.ResultsFat replacement of at least one muscle was identified in all symptomatic patients. The most common finding was the existence of patchy areas of fat replacement. Although there was a wide variability of muscles affected, we observed a common pattern characterized by the involvement of periscapular, paraspinal, gluteal and quadriceps muscles. STIR signal was enhanced in 67% of the patients, either in the muscle itself or in the surrounding fascia. We identified 10 diagnostic characteristics based on the pattern identified that allowed us to distinguish VCP disease from other neuromuscular diseases with high accuracy.ConclusionsPatients with mutations in the VCP gene had common features on muscle MRI that are helpful for diagnosis purposes, including the presence of patchy fat replacement and a prominent involvement of the periscapular, paraspinal, abdominal and thigh muscles.

Comparison of Short-Tau Inversion Recovery With Short-Tau Inversion Recovery-Slice Encoding for Metal Artifact Correction for Spine Imaging at 1.5 T in the Setting of Metallic Implants.

To compare the image quality of short-tau inversion recovery (STIR) and the STIR-slice encoding for metal artifact correction (SEMAC) sequence for postsurgery spine magnetic resonance imaging (MRI). Twenty-nine patients with metallic spinal implants who underwent spinal 1.5 T MRI with STIR and STIR-SEMAC sequences between July 2016 and November 2020 were retrospectively enrolled. Qualitative assessments were performed using 5-point scales; higher scores indicated better image quality. For screw metal artifact analysis, scores were obtained for artifacts on vertebral bodies and neural foramina, screw artifact widths, and bone marrow signal intensities. For patient-based analysis, scores were obtained for imaging quality and fat suppression quality, signal intensity, and cerebrospinal fluid noise. A paired t test was performed for statistical analyses. We analyzed 163 screws in 29 patients. In the screw metal artifact analysis, the vertebral body and neural foramen scores were significantly higher for the STIR-SEMAC images than for the STIR (all P < 0.001). The artifact width in the STIR-SEMAC images (9.8 ± 3.4 mm) was significantly smaller than that in the STIR images (16.0 ± 4.7 mm, P < 0.001). In patient-based analysis, the fat suppression and imaging quality scores were significantly higher for the STIR-SEMAC images than for the STIR images (all P < 0.001). The cerebrospinal fluid signal intensity, noise, and signal-to-noise ratios were significantly higher for the STIR images (all P < 0.005). Short-tau inversion recovery-SEMAC sequences provide good metallic artifact reduction and fat suppression for postsurgery spine 1.5 T MRI.