Fat-suppressed Sequences Research Articles

Clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer

ObjectiveTo establish and validate a new clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer.MethodsA total of 593 breast cancer patients who underwent preoperative breast MRI from Jan 2017 to Dec 2020 were enrolled, which were randomly divided into the training (n=474) and test sets (n=119) at the ratio of 8:2. Intratumoral region (ITR) of interest were manually delineated, and peritumoral regions of 3 mm and 5 mm (PTR-3 mm and PTR-5 mm) were automatically obtained by dilating the ITR. Intratumoral and peritumoral radiomics features were extracted from the fat-suppressed T2-weighted images, including first-order statistical features, shape features, texture features, and filtered features. The Mann-Whitney U Test, Z score normalization, K-best method, and least absolute shrinkage and selection operator (LASSO) algorithm were applied to select key features to construct radscores based on ITR, PTR-3 mm, PTR-5 mm, ITR+PTR-3 mm and ITR+ PTR-5 mm. Risk factors were selected by univariate and multivariate logistic regressions and were used to construct a clinical model and a clinical-radiomics model that presented as a nomogram. The performance of models was assessed by sensitivity, specificity, accuracy, the area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA).ResultsITR+PTR-3 mm radsore and histological grade were selected as risk factors. A clinical-radiomics model was constructed by adding ITR+PTR-3mm radscore to the clinical factor, which was presented as a nomogram. The clinical-radiomics nomogram showed the highest AUC (0.873), sensitivity (72.3%), specificity (78.9%) and accuracy (77.0%) in the training set and the highest AUC (0.851), sensitivity (71.4%), specificity (79.8%) and accuracy (77.3%) in the test set. DCA showed that the clinical-radiomics nomogram had the greatest net clinical benefit compared to the other models.ConclusionThe clinical-radiomics nomogram showed promising clinical application value in differentiating luminal and non-luminal breast cancer.

Imaging manifestations and misdiagnosis of liposclerosing myxofibroma fumor

To focus on the imaging features of liposclerosing myxofibrous tumor (LSMFT) and diagnostic challenges to enhance clinical recognition and differential diagnosis accuracy. Retrospective analysis was conducted on the imaging and pathological data from 21 cases diagnosed with LSMFT between January 2014 and November 2022, including 14 males and 7 females (aged from 21 to 73 years; the course of disease ranged from 4 to 48 months). Patient demographics, clinical presentations, and imaging modalities including X-ray, CT, and MRI were reviewed. Pathological findings were correlated with imaging features to delineate diagnostic criteria and identify causes of misdiagnosis. All 21 patients represented primary lesions, located in 14 bilateral femurs [9 on the right and 5 on the left; 12 proximal femurs (intertrochanteric and peripheral) and 2 distal femurs], 2 proximal tibia, 2 proximal humerus, 1 proximal radius, and 1 ilium and 1 calcaneus. On X-ray and CT, all lesions showed cartographical or quasi-circular osteolytic destruction with distinct and sclerotic margins, continuous bone cortex, absence of periosteal reaction and surrounding soft tissue mass. Calcification, bone ridge and varying degrees of fat components were observed in 16 lesions, displaying mixed density;5 lesions showed ground-glass density with minimal bone ridge. On MRI, the signal intensity of the lesion was heterogeneous. T1-weighted imaging showed iso-to slightly high signal intensity, while T2-weighted imaging demonstrated unevenly high signal intensity. Fat-suppressed sequences depicted significantly elevated signal intensity within lesions, with post-contrast enhancement showing uneven patterns. 5 Cases were initially misdiagnosed as fibrous dysplasia. LSMFT is an uncommon benign bone tumor, typically localized in the proximal femur but occasionally found in other skeletal sites. Understanding its distinct imaging characteristics is crucial for accurate diagnosis. Typical cases exhibit identifiable imaging patterns, whereas atypical presentations may lead to misdiagnosis as fibrous dysplasia.

Brain microbleeds resulting from presumed extensive fat emboli in a patient with bone marrow necrosis following a sickle cell disease vaso-occlusive crisis.

Acute manifestations of sickle cell disease (SCD) are numerous and multisystemic.Cerebral fat embolism (CFE) is a rare but serious complication of SCD caused by bone marrow necrosis (BMN) during vaso-occlusive crises (VOC). We present the case of a41-year-old man with SCD who developed severe VOC and multi-organ dysfunction.He subsequently experienced neurological deterioration with decreased consciousness and diffuse encephalopathy on serial electroencephalograms. Bone marrow aspiration confirmed BMN. Brain MRI revealed extensive diffuse leukoencephalopathy, vasogenic and cytotoxic edema in the white matter, patchy edema in the cranial vault bone marrow on fat-suppressed FLAIR sequence (a finding consistent with the confirmed BMN), and multiple cerebral microbleeds on susceptibility-weighted imaging consistent with CFE. The management of acute neurological complications of SCD varies depending on the specific complication. Brain MRI plays a crucial role in the accurate diagnosis of these complications to guide appropriate treatment.

The role of sacro-iliac joint magnetic resonance imaging in the diagnosis of axial spondyloarthritis: focus on differential diagnosis in women.

To review the role of sacro-iliac magnetic resonance imaging (MRI) in the diagnosis of axial spondyloarthritis (AxSpA), with a focus on gender differences. The experience of the authors and the results of an informal literature review are reported. Inflammatory changes of the sacro-iliac joint are the hallmark of AxSpA. Early, non-radiographic sacroiliitis may be diagnosed with MRI through the assessment of bone marrow edema (BMO) as well as concomitant structural damage. The MRI protocol should include three necessary sequences, i.e., fat-saturated T2-weighted sequences on two orthogonal planes, T1-weighted semi-coronal sequence, and fat-suppressed T1-weighted semi-coronal sequence. Inflammatory changes comprise required signs (BMO and/or osteitis) and additional signs, including synovitis (better defined as joint space enhancement), enthesitis, and capsulitis. Structural changes consist of erosions, sclerosis, fat metaplasia, and ankylosis. Due to mechanical axial strain, inflammatory changes in the sacro-iliac joint can be found in healthy individuals, runners, and patients with nonspecific low back pain. The prevalence of BMO is higher in women during pregnancy and postpartum, even 12 months after childbirth, but the extent and distribution of MRI findings may help in the differential diagnosis. Other challenges in the MRI diagnosis of sacroiliitis are subchondral T2 hyperintensity during developmental age, periarticular sclerosis in healthy subjects, or osteitis condensans ilii, and several pathological conditions that may mimic AxSpA, some of which are more frequently found in women. The described diagnostic challenges impose a multidisciplinary approach combining imaging findings with clinical and laboratory data.

Assessing Axillary Lymph Node Burden and Prognosis in cT1-T2 Stage Breast Cancer Using Machine Learning Methods: A Retrospective Dual-Institutional MRI Study.

Pathological axillary lymph node (pALN) burden is an important factor for treatment decision-making in clinical T1-T2 (cT1-T2) stage breast cancer. Preoperative assessment of the pALN burden and prognosis aids in the individualized selection of therapeutic approaches. To develop and validate a machine learning (ML) model based on clinicopathological and MRI characteristics for assessing pALN burden and survival in patients with cT1-T2 stage breast cancer. Retrospective. A total of 506 females (range: 24-83 years) with cT1-T2 stage breast cancer from two institutions, forming the training (N = 340), internal validation (N = 85), and external validation cohorts (N = 81), respectively. This study used 1.5-T, axial fat-suppressed T2-weighted turbo spin-echo sequence and axial three-dimensional dynamic contrast-enhanced fat-suppressed T1-weighted gradient echo sequence. Four ML methods (eXtreme Gradient Boosting [XGBoost], Support Vector Machine, k-Nearest Neighbor, Classification and Regression Tree) were employed to develop models based on clinicopathological and MRI characteristics. The performance of these models was evaluated by their discriminative ability. The best-performing model was further analyzed to establish interpretability and used to calculate the pALN score. The relationships between the pALN score and disease-free survival (DFS) were examined. Chi-squared test, Fisher's exact test, univariable logistic regression, area under the curve (AUC), Delong test, net reclassification improvement, integrated discrimination improvement, Hosmer-Lemeshow test, log-rank, Cox regression analyses, and intraclass correlation coefficient were performed. A P-value <0.05 was considered statistically significant. The XGB II model, developed based on the XGBoost algorithm, outperformed the other models with AUCs of 0.805, 0.803, and 0.818 in the three cohorts. The Shapley additive explanation plot indicated that the top variable in the XGB II model was the Node Reporting and Data System score. In multivariable Cox regression analysis, the pALN score was significantly associated with DFS (hazard ratio: 4.013, 95% confidence interval: 1.059-15.207). The XGB II model may allow to evaluate pALN burden and could provide prognostic information in cT1-T2 stage breast cancer patients. 3 TECHNICAL EFFICACY: Stage 2.

Importance and implementation of radiological modalities in the diagnostics of soft tissue tumors

Malignant soft tissue tumors, in particular, require amultimodal treatment concept involving interdisciplinary cooperation between radiologists, pathologists, surgeons and oncologists at special tumor centers. The foundations of the treatment decision are the imaging diagnostics and the diagnosis confirmation based on tissue samples. The (local) extent and growth behavior of atumor are among the most important findings of imaging as they have adirect influence on the surgical procedure. The most important diagnostic procedure here is magnetic resonance imaging (MRI). The T1-weighted and fat-suppressed sequences after i.v. contrast administration are used to visualize the extent of the tumor. In synopsis with diffusion-weighted and T2-weighted sequences, adifferentiation between vital tumor tissue and tumor necrosis is additionally possible. This also enables targeted sampling from vital tumor parts so that the patient can be assigned to the appropriate treatment concept as quickly as possible.

Artificial intelligence improves the diagnosis of human leukocyte antigen (HLA)-B27-negative axial spondyloarthritis based on multi-sequence magnetic resonance imaging and clinical features.

Axial spondyloarthritis (axSpA) is frequently diagnosed late, particularly in human leukocyte antigen (HLA)-B27-negative patients, resulting in a missed opportunity for optimal treatment. This study aimed to develop an artificial intelligence (AI) tool, termed NegSpA-AI, using sacroiliac joint (SIJ) magnetic resonance imaging (MRI) and clinical SpA features to improve the diagnosis of axSpA in HLA-B27-negative patients. We retrospectively included 454 HLA-B27-negative patients with rheumatologist-diagnosed axSpA or other diseases (non-axSpA) from the Third Affiliated Hospital of Southern Medical University and Nanhai Hospital between January 2010 and August 2021. They were divided into a training set (n=328) for 5-fold cross-validation, an internal test set (n=72), and an independent external test set (n=54). To construct a prospective test set, we further enrolled 87 patients between September 2021 and August 2023 from the Third Affiliated Hospital of Southern Medical University. MRI techniques employed included T1-weighted (T1W), T2-weighted (T2W), and fat-suppressed (FS) sequences. We developed NegSpA-AI using a deep learning (DL) network to differentiate between axSpA and non-axSpA at admission. Furthermore, we conducted a reader study involving 4 radiologists and 2 rheumatologists to evaluate and compare the performance of independent and AI-assisted clinicians. NegSpA-AI demonstrated superior performance compared to the independent junior rheumatologist (≤5 years of experience), achieving areas under the curve (AUCs) of 0.878 [95% confidence interval (CI): 0.786-0.971], 0.870 (95% CI: 0.771-0.970), and 0.815 (95% CI: 0.714-0.915) on the internal, external, and prospective test sets, respectively. The assistance of NegSpA-AI promoted discriminating accuracy, sensitivity, and specificity of independent junior radiologists by 7.4-11.5%, 1.0-13.3%, and 7.4-20.6% across the 3 test sets (all P<0.05). On the prospective test set, AI assistance also improved the diagnostic accuracy, sensitivity, and specificity of independent junior rheumatologists by 7.7%, 7.7%, and 6.9%, respectively (all P<0.01). The proposed NegSpA-AI effectively improves radiologists' interpretations of SIJ MRI and rheumatologists' diagnoses of HLA-B27-negative axSpA.

Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI.

Meniscal injury is a common cause of knee joint pain and a precursor to knee osteoarthritis (KOA). The purpose of this study is to develop an automatic pipeline for meniscal injury classification and localization using fully and weakly supervised networks based on MRI images. In this retrospective study, data were from the osteoarthritis initiative (OAI). The MR images were reconstructed using a sagittal intermediate-weighted fat-suppressed turbo spin-echo sequence. (1) We used 130 knees from the OAI to develop the LGSA-UNet model which fuses the features of adjacent slices and adjusts the blocks in Siam to enable the central slice to obtain rich contextual information. (2) One thousand seven hundred and fifty-six knees from the OAI were included to establish segmentation and classification models. The segmentation model achieved a DICE coefficient ranging from 0.84 to 0.93. The AUC values ranged from 0.85 to 0.95 in the binary models. The accuracy for the three types of menisci (normal, tear, and maceration) ranged from 0.60 to 0.88. Furthermore, 206 knees from the orthopedic hospital were used as an external validation data set to evaluate the performance of the model. The segmentation and classification models still performed well on the external validation set. To compare the diagnostic performances between the deep learning (DL) models and radiologists, the external validation sets were sent to two radiologists. The binary classification model outperformed the diagnostic performance of the junior radiologist (0.82-0.87 versus 0.74-0.88). This study highlights the potential of DL in knee meniscus segmentation and injury classification which can help improve diagnostic efficiency.

Preoperative prediction of histopathological grading in patients with chondrosarcoma using MRI-based radiomics with semantic features

BackgroundDistinguishing high-grade from low-grade chondrosarcoma is extremely vital not only for guiding the development of personalized surgical treatment but also for predicting the prognosis of patients. We aimed to establish and validate a magnetic resonance imaging (MRI)-based nomogram for predicting preoperative grading in patients with chondrosarcoma.MethodsApproximately 114 patients (60 and 54 cases with high-grade and low-grade chondrosarcoma, respectively) were recruited for this retrospective study. All patients were treated via surgery and histopathologically proven, and they were randomly divided into training (n = 80) and validation (n = 34) sets at a ratio of 7:3. Next, radiomics features were extracted from two sequences using the least absolute shrinkage and selection operator (LASSO) algorithms. The rad-scores were calculated and then subjected to logistic regression to develop a radiomics model. A nomogram combining independent predictive semantic features with radiomic by using multivariate logistic regression was established. The performance of each model was assessed by the receiver operating characteristic (ROC) curve analysis and the area under the curve, while clinical efficacy was evaluated via decision curve analysis (DCA).ResultsUltimately, six optimal radiomics signatures were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging with fat suppression (T2WI-FS) sequences to develop the radiomics model. Tumour cartilage abundance, which emerged as an independent predictor, was significantly related to chondrosarcoma grading (p < 0.05). The AUC values of the radiomics model were 0.85 (95% CI, 0.76 to 0.95) in the training sets, and the corresponding AUC values in the validation sets were 0.82 (95% CI, 0.65 to 0.98), which were far superior to the clinical model AUC values of 0.68 (95% CI, 0.58 to 0.79) in the training sets and 0.72 (95% CI, 0.57 to 0.87) in the validation sets. The nomogram demonstrated good performance in the preoperative distinction of chondrosarcoma. The DCA analysis revealed that the nomogram model had a markedly higher clinical usefulness in predicting chondrosarcoma grading preoperatively than either the rad-score or clinical model alone.ConclusionThe nomogram based on MRI radiomics combined with optimal independent factors had better performance for the preoperative differentiation between low-grade and high-grade chondrosarcoma and has potential as a noninvasive preoperative tool for personalizing clinical plans.

Prognostic Value of MRI Assessment of Residual Peritumoral Edema in Breast Cancer Treated With Neoadjuvant Chemotherapy.

Peritumoral edema (PE) identified on T2-weighted breast MRI is a factor for poor prognosis in breast cancer. To assess the prognostic value of residual PE (rPE) in patients with PE positive breast cancer prior to neoadjuvant chemotherapy (NACT) who subsequently underwent curative surgery. Retrospective. In total, 128 patients with nonmetastatic invasive breast cancer who underwent breast MRI before and after NACT. Axial precontrast 2D fast spin echo T2W fat-suppressed sequence. Axial dynamic 3D gradient echo T1W fat-suppressed sequence. PE was diagnosed when a signal intensity as high as water was detected surrounding the tumor on a T2-weighted breast MRI. PE was qualitatively evaluated by three readers with more than 20 years of experience in interpreting breast field imaging findings. Residual cancer burden (RCB) were assessed post-NACT. Recurrence-free survival (RFS) and overall survival (OS) were evaluated as the endpoints of this study. Chi-square test; Kaplan-Meier method, log-rank test, and Cox proportional hazard model. A P-value <0.05 was considered statistically significant. Pre-PE was observed in 64 out of 128 patients. Of these, rPE was observed in 21. In the log-rank test, breast cancer with rPE had significantly worse RFS and OS than that without rPE. Cox proportional hazard analysis identified rPE as a significant prognostic factor for recurrence (hazard ratio, 11.6; 95% confidence interval [CI], 3.05-43.8) and death (hazard ratio, 17.8; 95% CI, 3.30-96.3). Breast cancer with rPE had significant worse RFS and OS than that without rPE in RCB class II, and significant worse OS in pathological complete response, class I and class II in the log-rank test. rPE on a T2-weighted breast MRI was a significant factor for breast cancer recurrence and death in patients with pre-PE-positive breast cancer treated with NACT. Stage 2.

Imaging-Based Deep Learning for Predicting Desmoid Tumor Progression.

Desmoid tumors (DTs) are non-metastasizing and locally aggressive soft-tissue mesenchymal neoplasms. Those that become enlarged often become locally invasive and cause significant morbidity. DTs have a varied pattern of clinical presentation, with up to 50-60% not growing after diagnosis and 20-30% shrinking or even disappearing after initial progression. Enlarging tumors are considered unstable and progressive. The management of symptomatic and enlarging DTs is challenging, and primarily consists of chemotherapy. Despite wide surgical resection, DTs carry a rate of local recurrence as high as 50%. There is a consensus that contrast-enhanced magnetic resonance imaging (MRI) or, alternatively, computerized tomography (CT) is the preferred modality for monitoring DTs. Each uses Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1), which measures the largest diameter on axial, sagittal, or coronal series. This approach, however, reportedly lacks accuracy in detecting response to therapy and fails to detect tumor progression, thus calling for more sophisticated methods. The objective of this study was to detect unique features identified by deep learning that correlate with the future clinical course of the disease. Between 2006 and 2019, 51 patients (mean age 41.22 ± 15.5 years) who had a tissue diagnosis of DT were included in this retrospective single-center study. Each had undergone at least three MRI examinations (including a pretreatment baseline study), and each was followed by orthopedic oncology specialists for a median of 38.83 months (IQR 44.38). Tumor segmentations were performed on a T2 fat-suppressed treatment-naive MRI sequence, after which the segmented lesion was extracted to a three-dimensional file together with its DICOM file and run through deep learning software. The results of the algorithm were then compared to clinical data collected from the patients' medical files. There were 28 males (13 stable) and 23 females (15 stable) whose ages ranged from 19.07 to 83.33 years. The model was able to independently predict clinical progression as measured from the baseline MRI with an overall accuracy of 93% (93 ± 0.04) and ROC of 0.89 ± 0.08. Artificial intelligence may contribute to risk stratification and clinical decision-making in patients with DT by predicting which patients are likely to progress.

Effect of contrast agent on T2-weighted fat-suppressed imaging and diffusion-weighted imaging in the diagnosis of breast tumors.

Although previous studies have shown that the injection of contrast agents can improve image quality, the specific impact of this on T2-weighted fat-suppressed (T2 FS) and diffusion-weighted imaging (DWI) sequences in the diagnosis of breast cancer remains incompletely understood. In particular, there is insufficient research on how contrast agents affect the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values within these sequences, and how these changes influence the diagnosis of benign and malignant breast tumors. Breast magnetic resonance images (MRI) were obtained from 178 consecutive patients on a 3T scanner. The SNR and CNR of lesions on T2 FS sequence were calculated before and after contrast agent injection and compared. Differences between pre- and post-contrast ADC in identifying different tumor types were compared using the Kruskal-Wallis H-test and the paired comparison test. The accuracy of ADC values between pre- and post-contrast in distinguishing benign and malignant breast masses was assessed using receiver operating characteristic (ROC) curves. The SNR and CNR of T2 FS sequence increased after contrast injection, and especially for invasive cancer and benign tumor, the increase was significant. For DWI, there was a slight increase or decrease of ADC values after contrast injection, but the ADC values before and after contrast had a similar effect in identifying different types of tumors. In the ROC curve analysis for assessing benign and malignant breast tumors, the area under the curve (AUC) before and after contrast showed similar results. Contrast agent injection can improve the SNR and CNR of T2 FS sequence, thus providing higher quality images for the diagnosis of breast lesions. Furthermore, injection of contrast agent had little effect on the ability of ADC values to identify different types of lesions and both ADC values before and after the contrast agent were able to distinguish between benign and malignant tumors with almost the same accuracy.

“Fish head” sign on magnetic resonance imaging in knee synovitis – An observational pilot study

Objectives: Synovitis, characterized by joint inflammation, has various causes, including inflammatory, infectious, degenerative, traumatic, hemorrhagic, and neoplastic origins. Early detection is essential, and magnetic resonance imaging (MRI) plays a vital role in identifying synovial diseases before they lead to irreversible joint damage. It provides characterization and non-invasive monitoring of synovial changes. This study introduces and describes the novel “fish head” sign, a unique identifier of early knee synovitis on MRI. Materials and Methods: We conducted a retrospective analysis of 50 MRI cases of patients with rheumatoid arthritis (RA)-related synovitis who were referred to our clinic due to knee pain. The primary aim was to uncover insights into the “fish head” sign, a unique imaging feature observed on sagittal views of the lateral meniscus using proton density (PD)-weighted fat-suppressed sequences. Results: Within our cohort of 50 patients, 20 displayed the fish head sign on MRI, whereas 30 did not exhibit this sign. 17/20 of these patients had mild synovitis, 1/20 patients had moderate synovitis, and 2/20 patients had severe synovitis. Fourteen patients with synovitis did not demonstrate the sign. Conclusion: Our study introduces the “fish head” sign observed in PD MRI images of early knee synovitis among patients with RA. This unique sign has the potential to serve as a diagnostic tool or adjunct for patients with synovitis of the knee, reducing the need for contrast-enhanced imaging. This discovery offers valuable insights into the field of synovitis assessment, imaging, and diagnosis.

Impact of Deep Learning Denoising Algorithm on Diffusion Tensor Imaging of the Growth Plate on Different Spatial Resolutions.

To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions, this IRB-approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. A General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) was employed to acquire two DTI (diffusion tensor imaging) sequences of the left knee on each child at 3T: an in-plane 2.0 × 2.0 mm2 with section thickness of 3.0 mm and a 2 mm3 isovolumetric voxel; neither had an intersection gap. For image acquisition, a multi-band DTI with a fat-suppressed single-shot spin-echo echo-planar sequence (20 non-collinear directions; b-values of 0 and 600 s/mm2) was utilized. The MR vendor-provided a commercially available DL model which was applied with 75% noise reduction settings to the same subject DTI sequences at different spatial resolutions. We compared DTI tract metrics from both DL-reconstructed scans and non-denoised scans for the femur and tibia at each spatial resolution. Differences were evaluated using Wilcoxon-signed ranked test and Bland-Altman plots. When comparing DL versus non-denoised diffusion metrics in femur and tibia using the 2 mm × 2 mm × 3 mm voxel dimension, there were no significant differences between tract count (p = 0.1, p = 0.14) tract volume (p = 0.1, p = 0.29) or tibial tract length (p = 0.16); femur tract length exhibited a significant difference (p < 0.01). All diffusion metrics (tract count, volume, length, and fractional anisotropy (FA)) derived from the DL-reconstructed scans, were significantly different from the non-denoised scan DTI metrics in both the femur and tibial physes using the 2 mm3 voxel size (p < 0.001). DL reconstruction resulted in a significant decrease in femorotibial FA for both voxel dimensions (p < 0.01). Leveraging denoising algorithms could address the drawbacks of lower signal-to-noise ratios (SNRs) associated with smaller voxel volumes and capitalize on their better spatial resolutions, allowing for more accurate quantification of diffusion metrics.

Evaluation of the Efficiency and Effectiveness of MRI in the Diagnosis of Chronic Shoulder Pain

Objective: This study aimed to evaluate MRI efficiency in diagnosing chronic shoulder pain causes. Chronic shoulder pain accounts for 5% of musculoskeletal consultations. Method: Thirty-four chronic shoulder pain patients (18-65 years) underwent shoulder MRI. Sequences included T1-weighted, T2-weighted, proton density, and STIR for comprehensive analysis. All patients were clinically diagnosed with chronic shoulder pain prior to imaging. Finding: One patient (1.96%) had normal MRI findings. Thirty-three patients (98.04%) had abnormalities. Rotator cuff injuries were most prevalent, especially partial supraspinatus tears (18.63%). Other findings included bursitis, joint effusion, AC arthropathy, Hill Sachs deformity, AC joint impingement, rotator cuff fatty atrophy, and biceps tendinopathy. Conclusion: MRI provided excellent visualization of soft tissue pathologies causing chronic shoulder pain, noninvasively without ionizing radiation. Combining MRI sequences accurately diagnosed various shoulder conditions. Fat suppression sequences were key for identifying rotator cuff tears. MRI is the gold standard for diagnosing rotator cuff injuries, the most common chronic shoulder pain cause. Novelty: This study demonstrates MRI's utility for evaluating chronic shoulder pain causes. While no single sequence visualizes all shoulder pathologies, using T1-weighted, T2-weighted, proton density, and STIR sequences together provides a comprehensive analysis to guide appropriate patient treatment. Keywords MRI, Chronic shoulder pain, Rotator cuff, Joint effusion, frozen shoulder

Comparing the Efficacy of a Combination of Diffusion-Weighted Imaging and T2-STIR (Short Tau Inversion Recovery) Imaging With Contrast-Enhanced MRI in the Evaluation of Perianal Fistula.

Perianal fistula is clinically diagnosed and commonly characterized using magnetic resonance imaging (MRI). Diffusion-weighted imaging (DWI) and T2-weighted imaging are emerging techniques that can obviate the need for contrast injection in cases where contrast administration is not feasible or contraindicated. The main objective of our study was to compare the efficacy of the combination of DWI and T2 STIR (short tau inversion recovery) imaging with contrast-enhanced MRI for the diagnosis and characterization of perianal fistula. Sixty-nine patients with clinical perianal fistula with at least one external opening were evaluated with DWI, T2 STIR, and contrast MRI. A comparative cross-sectional study was conducted in the Department of Radiodiagnosis and Imaging, All India Institute of Medical Sciences, Bhopal, India. The chi-square test was done to find the association between categorical variables. The Kappa test was done to estimate the agreement between two different tests in measuring the outcome. The validity of tests was measured using sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. The combination of DWI and T2 STIR is equivalent to contrast-enhanced MRI in the evaluation of primary and complicated perianal fistula. The combination of DWI and T2 STIR is superior to DWI alone in the classification and characterization of perianal fistula. However, DWI is superior to T2 STIR in differentiating perianal inflammation with abscess from perianal inflammation without abscess and can be used as an alternative to post-contrast fat-suppressed T1-WI in the detection of perianal abscesses and disease activity. DWI can be used as an adjunct to T2 STIR, and the combination of DWI and T2 STIR can replace the post-contrast fat-suppressed T1 MRI sequence in the classification and characterization of perianal fistula.

A deep learning knowledge distillation framework using knee MRI and arthroscopy data for meniscus tear detection.

Purpose: To construct a deep learning knowledge distillation framework exploring the utilization of MRI alone or combing with distilled Arthroscopy information for meniscus tear detection. Methods: A database of 199 paired knee Arthroscopy-MRI exams was used to develop a multimodal teacher network and an MRI-based student network, which used residual neural networks architectures. A knowledge distillation framework comprising the multimodal teacher network T and the monomodal student network S was proposed. We optimized the loss functions of mean squared error (MSE) and cross-entropy (CE) to enable the student network S to learn arthroscopic information from the teacher network T through our deep learning knowledge distillation framework, ultimately resulting in a distilled student network S T. A coronal proton density (PD)-weighted fat-suppressed MRI sequence was used in this study. Fivefold cross-validation was employed, and the accuracy, sensitivity, specificity, F1-score, receiver operating characteristic (ROC) curves and area under the receiver operating characteristic curve (AUC) were used to evaluate the medial and lateral meniscal tears detection performance of the models, including the undistilled student model S, the distilled student model S T and the teacher model T. Results: The AUCs of the undistilled student model S, the distilled student model S T, the teacher model T for medial meniscus (MM) tear detection and lateral meniscus (LM) tear detection are 0.773/0.672, 0.792/0.751 and 0.834/0.746, respectively. The distilled student model S T had higher AUCs than the undistilled model S. After undergoing knowledge distillation processing, the distilled student model demonstrated promising results, with accuracy (0.764/0.734), sensitivity (0.838/0.661), and F1-score (0.680/0.754) for both medial and lateral tear detection better than the undistilled one with accuracy (0.734/0.648), sensitivity (0.733/0.607), and F1-score (0.620/0.673). Conclusion: Through the knowledge distillation framework, the student model S based on MRI benefited from the multimodal teacher model T and achieved an improved meniscus tear detection performance.

Dimensional changes of Hoffa’s fat pad related to aging: evaluation by MRI

BackgroundHoffa's fat pad is an intra-capsular extra-sinovial structure of the knee joint that has a significant biomechanical and metabolic role, minimizing the influence of stresses created by loading and generating cytokines. Changes in its size can lead to variations in the homeostasis of the knee in elderly patients. This work intends to assess the dimensional variations of Hoffa's fat pad associated to aging in both sexes, using MRI sagittal sequences acquired from the OAI (Osteoarthritis Initiative) database.MethodsWe examined the Hoffa's fat pad sagittal thickness in 217 men and women with knee osteoarthritis who were grouped into four age groups for the study: 40–49; 50–59; 60–69; and 70–80. 3T sagittal IW 2D TSE Fat-suppressed MRI sequences, taken from the OAI (Osteoarthritis Initiative) database, were examined.ResultsHoffa’s fat pad thickness was shown to differ significantly between groups in both men and women, decreasing in the older individuals' groups (R = − 0.46; p 0.0001). By dividing the patients into ten-year age groups and by sex, the thickness of both the right knee and the left knee was examined. In fact, the average thickness of Hoffa's fat pad of the right knee was reported to be, in males, 33.6+/− 3 mm in subjects aged between 40 and 49 years, 31+/− 2.4 mm for patients aged between 50 and 59, 30.3 ± 1.8 mm in the group between 60 and 69 years and 28.7+/− 1.8 mm between 70 and 80 years. In women the values obtained were the following: 29+/− 1.6 mm between 40 and 49 years; 28.9+/− 2.6 mm in the group between 50 and 59 years, 25.3+/− 1.9 mm for patients aged 60 and 69 years and 26+/− 2 mm between 70 and 80 years. Similar results were obtained for the left knee.ConclusionsHoffa’s fat pad gradually thins with aging in both male and female patients with knee osteoarthritis, and this can be detected by evaluating the thickness of the fat pad on sagittal MRI sequences.

Current MR imaging of cartilage in the context of knee osteoarthritis (part1) : Principles and sequences

Magnetic resonance imaging (MRI) is the clinical method of choice for cartilage imaging in the context of degenerative and nondegenerative joint diseases. The MRI-based definitions of osteoarthritis rely on the detection of osteophytes, cartilage pathologies, bone marrow edema and meniscal lesions but currently ascientific consensus is lacking. In the clinical routine proton density-weighted, fat-suppressed 2D turbo spin echo sequences with echo times of 30-40 ms are predominantly used, which are sufficiently sensitive and specific for the assessment of cartilage. The additionally acquired T1-weighted sequences are primarily used for evaluating other intra-articular and periarticular structures. Diagnostically relevant artifacts include magic angle and chemical shift artifacts, which can lead to artificial signal enhancement in cartilage or incorrect representations of the subchondral lamina and its thickness. Although scientifically validated, high-resolution 3D gradient echo sequences (for cartilage segmentation) and compositional MR sequences (for quantification of physical tissue parameters) are currently reserved for scientific research questions. The future integration of artificial intelligence techniques in areas such as image reconstruction (to reduce scan times while maintaining image quality), image analysis (for automated identification of cartilage defects), and image postprocessing (for automated segmentation of cartilage in terms of volume and thickness) will significantly improve the diagnostic workflow and advance the field further.

Fascial Signal Change on the Cervical MRI of a Patient with Systemic Lupus Erythematosus.

Here, we present a case of a 53-year-old female patient with chronic neck pain and systemic inflammation who was ultimately diagnosed with systemic lupus erythematosus. Notably, applying fat-suppressed T2-weighted MRI sequences was pivotal in detecting structural fascial changes commonly associated with systemic inflammatory diseases. PET-CT scans further revealed systemic inflammation around multiple joints, providing valuable insights into MRI signal alterations. This case underscores the importance of considering systemic autoimmune pathology as a potential underlying cause of chronic musculoskeletal pain. It also highlights the clinical utility of MRI with fat suppression sequences in identifying inflammation-related fascial changes. This case emphasizes the significance of a comprehensive evaluation, particularly in situations where clinical features overlap between autoimmune and degenerative skeletal pathologies. Fat-suppressed MRI can provide information about fascial pathology related to systemic inflammatory diseases. In this context, it is worth noting that PET-CT and fat suppression MRI complement each other by providing complementary information about inflammation and the underlying causes of a patient's pain.