Soft tissue tumor imaging in adults: whole-body staging in sarcoma, non-malignant entities requiring special algorithms, pitfalls and special imaging aspects. Guidelines 2024 from the European Society of Musculoskeletal Radiology (ESSR).

OBJECTIVES: The revised European Society of Musculoskeletal Radiology (ESSR) consensus guidelines on soft tissue tumor imaging represent an update of 2015 after technical advancements, further insights into specific entities, and revised World Health Organization (2020) and AJCC (2017) classifications. This second of three papers covers algorithms once histology is confirmed: (1) standardized whole-body staging, (2) special algorithms for non-malignant entities, and (3) multiplicity, genetic tumor syndromes, and pitfalls. MATERIALS AND METHODS: A validated Delphi method based on peer-reviewed literature was used to derive consensus among a panel of 46 specialized musculoskeletal radiologists from 12 European countries. Statements that had undergone interdisciplinary revision were scored online by the level of agreement (0 to 10) during two iterative rounds, that could result in 'group consensus', 'group agreement', or 'lack of agreement'. RESULTS: The three sections contain 24 statements with comments. Group consensus was reached in 95.8% and group agreement in 4.2%. For whole-body staging, pulmonary MDCT should be performed in all high-grade sarcomas. Whole-body MRI is preferred for staging bone metastasis, with [18F]FDG-PET/CT as an alternative modality in PET-avid tumors. Patients with alveolar soft part sarcoma, clear cell sarcoma, and angiosarcoma should be screened for brain metastases. Special algorithms are recommended for entities such as rhabdomyosarcoma, extraskeletal Ewing sarcoma, myxoid liposarcoma, and neurofibromatosis type 1 associated malignant peripheral nerve sheath tumors. Satisfaction of search should be avoided in potential multiplicity. CONCLUSION: Standardized whole-body staging includes pulmonary MDCT in all high-grade sarcomas; entity-dependent modifications and specific algorithms are recommended for sarcomas and non-malignant soft tissue tumors. CLINICAL RELEVANCE STATEMENT: These updated ESSR soft tissue tumor imaging guidelines aim to provide support in decision-making, helping to avoid common pitfalls, by providing general and entity-specific algorithms, techniques, and reporting recommendations for whole-body staging in sarcoma and non-malignant soft tissue tumors. KEY POINTS: An early, accurate, diagnosis is crucial for the prognosis of patients with soft tissue tumors. These updated guidelines provide best practice expert consensus for standardized imaging algorithms, techniques, and reporting. Standardization can improve the comparability examinations and provide databases for large data analysis.

US-Guided Interventional Procedures for Total Hip Arthroplasty.

In patients with total hip arthroplasty (THA) with recurrent pain, symptoms may be caused by several conditions involving not just the joint, but also the surrounding soft tissues including tendons, muscles, bursae, and peripheral nerves. US and US-guided interventional procedures are important tools in the diagnostic work-up of patients with painful THA given that it is possible to reach a prompt diagnosis both directly identifying the pathological changes of periprosthetic structures and indirectly evaluating the response and pain relief to local injection of anesthetics under US monitoring. Then, US guidance can be used for the aspiration of fluid from the joint or periarticular collections, or alternatively to follow the biopsy needle to collect samples for culture analysis in the suspicion of prosthetic joint infection. Furthermore, US-guided percutaneous interventions may be used to treat several conditions with well-established minimally invasive procedures that involve injections of corticosteroid, local anesthetics, and platelet-rich plasma or other autologous products. In this review, we will discuss the clinical and technical applications of US-guided percutaneous interventional procedures in painful THA that can be used in routine daily practice for diagnostic and therapeutic purposes.

MRI of total hip arthroplasty: technical aspects and imaging findings.

Total hip arthroplasty (THA) is the best surgical approach for treating advanced hip degeneration, providing pain relief, and improved function in most cases. In the past, MR imaging quality has been highly compromised by in-plane distortions, inadequate fat saturation, and other artifacts due to metal components of THA. Technological advancements have made pathologic conditions, which were previously hidden by periprosthetic artifacts, outstanding features due to the optimization of several sequences. To date, several short and long-term complications involving bony and soft-tissue structures may be detected through magnetic resonance imaging (MRI). The use of MRI with adapted sequences and protocols may drastically reduce artifacts thereby providing essential pre-operative elements for planning revision surgery of failed THA. This review has the purpose of conveying new insights to musculoskeletal radiologists about the techniques to suppress metal-related artifacts and the hallmark MRI findings of painful THA. CRITICAL RELEVANCE STATEMENT: Advancements in metal-suppression have given radiologists the opportunity to play an emerging role in THA management. This article provides technical and imaging insights into challenges that can be encountered in cases of THA, which may present complications and characteristic imaging findings. KEY POINTS: Imaging total hip arthroplasty requires adapted MRI protocol and awareness of the common complications. We have reported the available metal-suppression sequences for evaluating total hip arthroplasty. Many structures and conditions should be considered when dealing with painful aseptic or septic arthroplasty.

Artificial intelligence in interventional radiology: state of the art.

Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.Relevance statement Exploring AI's transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.Key points• AI adoption in IR is more complex compared to diagnostic radiology.• Current literature about AI in IR is in its early stages.• AI has the potential to revolutionise every aspect of IR.

High incidence of RAMP lesions and a nonnegligible incidence of anterolateral ligament and posterior oblique ligament rupture in acute ACL injury.

PURPOSE: The purpose of this study was to assess the frequency of medial collateral ligament (MCL), posterior oblique ligament (POL) and anterolateral ligament (ALL) tears and different types of RAMP lesions of patients with verified acute anterior cruciate ligament (ACL) tears by magnetic resonance imaging (MRI). METHODS: MRI was performed on patients with a clinical diagnosis of acute ACL injury. Patients were eligible for inclusion if they had an initially clinically noted ACL tear confirmed on MRI within 30 days of trauma. RESULTS: A total of 146 patients were included in the study, 42 (28.8%) females and 104 (71.2%) males. The mean age at MRI was 27.2 ± 9.4 years, and the mean time from injury to MRI was 15.7 ± 7.8 days. Thirty-four (23.3%) patients had a complete MCL lesion, 32 (21.9%) had a complete POL lesion and 28 (19.2%) had a complete ALL lesion. One hundred and fourteen patients (78.1%) presented with RAMP lesions, while 20 (13.7%) patients reported other meniscal lesions. The mean medial and lateral tibial slopes were 4.0° ± 2.7° and 4.0° ± 3.1°, respectively. Only 10 (6.8%) patients reported no lesions associated with ACL rupture. The most common injuries were isolated RAMP type 3 (18-12.3%) and isolated RAMP type 1 (17-11.6%). Thirteen (8.9%) patients had a combination of MCL, POL and ALL rupture. CONCLUSIONS: Isolated lesions of the ACL are extremely rare. In most cases, a single RAMP lesion should be investigated. In the presence of MCL injury, POL injury should always be suspected as well, while nearly 20% of patients present a rupture of the ALL. About one in 10 patients had three lesions (MCL, ALL and POL), and most of them had a combined RAMP lesion. LEVEL OF EVIDENCE: Level IV.

CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.

OBJECTIVE: To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020. METHODS: A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed. RESULTS: Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies. CONCLUSIONS: Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage. CRITICAL RELEVANCE STATEMENT: An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage. KEY POINTS: • 2021-2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation.

AI applications in musculoskeletal imaging: a narrative review.

This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload.Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice. Key points • AI may potentially assist musculoskeletal radiologists in several interpretative tasks.• AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed.• AI should help radiologists to optimize workflow and augment diagnostic performance.

X-rays radiomics-based machine learning classification of atypical cartilaginous tumour and high-grade chondrosarcoma of long bones.

BACKGROUND: Atypical cartilaginous tumour (ACT) and high-grade chondrosarcoma (CS) of long bones are respectively managed with active surveillance or curettage and wide resection. Our aim was to determine diagnostic performance of X-rays radiomics-based machine learning for classification of ACT and high-grade CS of long bones. METHODS: This retrospective, IRB-approved study included 150 patients with surgically treated and histology-proven lesions at two tertiary bone sarcoma centres. At centre 1, the dataset was split into training (n = 71 ACT, n = 24 high-grade CS) and internal test (n = 19 ACT, n = 6 high-grade CS) cohorts, respectively, based on the date of surgery. At centre 2, the dataset constituted the external test cohort (n = 12 ACT, n = 18 high-grade CS). Manual segmentation was performed on frontal view X-rays, using MRI or CT for preliminary identification of lesion margins. After image pre-processing, radiomic features were extracted. Dimensionality reduction included stability, coefficient of variation, and mutual information analyses. In the training cohort, after class balancing, a machine learning classifier (Support Vector Machine) was automatically tuned using nested 10-fold cross-validation. Then, it was tested on both the test cohorts and compared to two musculoskeletal radiologists' performance using McNemar's test. FINDINGS: Five radiomic features (3 morphology, 2 texture) passed dimensionality reduction. After tuning on the training cohort (AUC = 0.75), the classifier had 80%, 83%, 79% and 80%, 89%, 67% accuracy, sensitivity, and specificity in the internal (temporally independent) and external (geographically independent) test cohorts, respectively, with no difference compared to the radiologists (p ≥ 0.617). INTERPRETATION: X-rays radiomics-based machine learning accurately differentiates between ACT and high-grade CS of long bones. FUNDING: AIRC Investigator Grant.

Effects of Interobserver Segmentation Variability and Intensity Discretization on MRI-Based Radiomic Feature Reproducibility of Lipoma and Atypical Lipomatous Tumor.

Segmentation and image intensity discretization impact on radiomics workflow. The aim of this study is to investigate the influence of interobserver segmentation variability and intensity discretization methods on the reproducibility of MRI-based radiomic features in lipoma and atypical lipomatous tumor (ALT). Thirty patients with lipoma or ALT were retrospectively included. Three readers independently performed manual contour-focused segmentation on T1-weighted and T2-weighted sequences, including the whole tumor volume. Additionally, a marginal erosion was applied to segmentations to evaluate its influence on feature reproducibility. After image pre-processing, with included intensity discretization employing both fixed bin number and width approaches, 1106 radiomic features were extracted from each sequence. Intraclass correlation coefficient (ICC) 95% confidence interval lower bound ≥ 0.75 defined feature stability. In contour-focused vs. margin shrinkage segmentation, the rates of stable features extracted from T1-weighted and T2-weighted images ranged from 92.68 to 95.21% vs. 90.69 to 95.66% after fixed bin number discretization and from 95.75 to 97.65% vs. 95.39 to 96.47% after fixed bin width discretization, respectively, with no difference between the two segmentation approaches (p ≥ 0.175). Higher stable feature rates and higher feature ICC values were found when implementing discretization with fixed bin width compared to fixed bin number, regardless of the segmentation approach (p < 0.001). In conclusion, MRI radiomic features of lipoma and ALT are reproducible regardless of the segmentation approach and intensity discretization method, although a certain degree of interobserver variability highlights the need for a preliminary reliability analysis in future studies.

Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis.

OBJECTIVES: To evaluate the methodological quality and diagnostic accuracy of MRI-based radiomic studies predicting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in gliomas. METHODS: PubMed Medline, EMBASE, and Web of Science were searched to identify MRI-based radiomic studies on MGMT methylation in gliomas published until December 31, 2022. Three raters evaluated the study methodological quality with Radiomics Quality Score (RQS, 16 components) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD, 22 items) scales. Risk of bias and applicability concerns were assessed with QUADAS-2 tool. A meta-analysis was performed to estimate the pooled area under the curve (AUC) and to assess inter-study heterogeneity. RESULTS: We included 26 studies, published from 2016. The median RQS total score was 8 out of 36 (22%, range 8-44%). Thirteen studies performed external validation. All studies reported AUC or accuracy, but only 4 (15%) performed calibration and decision curve analysis. No studies performed phantom analysis, cost-effectiveness analysis, and prospective validation. The overall TRIPOD adherence score was between 50% and 70% in 16 studies and below 50% in 10 studies. The pooled AUC was 0.78 (95% CI, 0.73-0.83, I2 = 94.1%) with a high inter-study heterogeneity. Studies with external validation and including only WHO-grade IV gliomas had significantly lower AUC values (0.65; 95% CI, 0.57-0.73, p < 0.01). CONCLUSIONS: Study RQS and adherence to TRIPOD guidelines was generally low. Radiomic prediction of MGMT methylation status showed great heterogeneity of results and lower performances in grade IV gliomas, which hinders its current implementation in clinical practice. CLINICAL RELEVANCE STATEMENT: MGMT promoter methylation status appears to be variably correlated with MRI radiomic features; radiomic models are not sufficiently robust to be integrated into clinical practice to accurately predict MGMT promoter methylation status in patients with glioma before surgery. KEY POINTS: • Adherence to the indications of TRIPOD guidelines was generally low, as was RQS total score. • MGMT promoter methylation status prediction with MRI radiomic features provided heterogeneous diagnostic accuracy results across studies. • Studies that included grade IV glioma only and performed external validation had significantly lower diagnostic accuracy than others.

Clinical Outcomes of Thermal Ablation Re-Treatment of Benign Thyroid Nodules: A Multicenter Study from the Italian Minimally Invasive Treatments of the Thyroid Group.

Background: Thermal ablation (TA) is an established therapeutic option alternative to surgery in patients with solid benign thyroid nodules causing local symptoms. However, a variable part of thyroid nodules remain viable after these nonsurgical treatments, and as many as 15% of nodules treated with TA may require a second treatment over time. This study aimed to evaluate the outcomes of TA re-treatment on symptomatic benign thyroid nodules where the volume decreased by <50% after the first procedure ( = technique inefficacy). Methods: We performed a multicenter retrospective cohort study including patients who underwent re-treatment with TA for benign thyroid nodules, whose volume decreased by <50% after initial treatment. The primary aim was to evaluate volume and volume reduction ratio (VRR) over time and compare the 6- and 12-month VRR after first versus second treatment. The secondary aim was to identify protective or risk factors for technique inefficacy, regrowth, and further treatments, expressed as adjusted hazard ratios (HRs) and confidence interval [CI], after adjustment for sex, age, nodule volume, structure and function, nodule regrowth or symptom relapse, technique used and if the same technique was used for the first and second TA and time between them. Results: We included 135 patients. Re-treatment led to VRR of 50% and 52.2% after 6 and 12 months. VRR after re-treatment was greater than after first treatment in small and medium size nodules (<30 mL), while there were no differences for large nodules (>30 mL). After re-treatment technique inefficacy rate was 51.9%, regrowth rate was 12.6%, and further treatment rate was 15.6%. Radiofrequency ablation (RFA) was protective toward technique inefficacy (HR = 0.40 [CI 0.24-0.65]) and need of further treatments (HR = 0.30 [CI 0.12-0.76]). Large nodule volume (>30 mL) was associated with increased risk of re-treatment (HR = 4.52 [CI 1.38-14.82]). Conclusions: This is the first study evaluating the outcomes of re-treatment on symptomatic benign thyroid nodules with a VRR <50% after the initial TA treatment. Best results were seen in small and medium nodules (<30 mL) and after RFA. Prospective confirmatory studies are needed.

Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI.

The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients' age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8-74.6%) accuracy and 0.572 (0.439-0.705) for AUC. The performances did not change when the patients' age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.

Quantification of visceral adipose tissue by computed tomography and magnetic resonance imaging: reproducibility and accuracy / Quantificação do tecido adiposo visceral por tomografia computadorizada e ressonância magnética: reprodutibilidade e acurácia

Abstract Objective: To evaluate the feasibility of quantifying visceral adipose tissue (VAT) on computed tomography (CT) and magnetic resonance imaging (MRI) scans, using freeware, as well as calculating intraobserver and interobserver reproducibility. Materials and Methods: We quantified VAT in patients who underwent abdominal CT and MRI at our institution between 2010 and 2015, with a maximum of three months between the two examinations. A slice acquired at the level of the umbilicus was selected. Segmentation was performed with the region growing algorithm of the freeware employed. Intraobserver and interobserver reproducibility were evaluated, as was the accuracy of MRI in relation to that of CT. Results: Thirty-one patients (14 males and 17 females; mean age of 57 ± 15 years) underwent CT and MRI (mean interval between the examinations, 28 ± 12 days). The interobserver reproducibility was 82% for CT (bias = 1.52 cm2; p = 0.488), 86% for T1-weighted MRI (bias = −4.36 cm2; p = 0.006), and 88% for T2-weighted MRI (bias = −0.52 cm2; p = 0.735). The intraobserver reproducibility was 90% for CT (bias = 0.14 cm2; p = 0.912), 92% for T1-weighted MRI (bias = −3,4 cm2; p = 0.035), and 90% for T2-weighted MRI (bias = −0.30 cm2; p = 0.887). The reproducibility between T1-weighted MRI and T2-weighted MRI was 87% (bias = −0.11 cm2; p = 0.957). In comparison with the accuracy of CT, that of T1-weighted and T2-weighted MRI was 89% and 91%, respectively. Conclusion: The program employed can be used in order to quantify VAT on CT, T1-weighted MRI, and T2-weighted MRI scans. Overall, the accuracy of MRI (in comparison with that of CT) appears to be high, as do intraobserver and interobserver reproducibility. However, the quantification of VAT seems to be less reproducible in T1-weighted sequences.

Resumo Objetivo: Avaliar a viabilidade da quantificação do tecido adiposo visceral (TAV) pela tomografia computadorizada (TC) e ressonância magnética (RM) usando um software freeware, e também calcular a reprodutibilidade intraobservador e interobservador. Materiais e Métodos: Foi quantificado o TAV em pacientes submetidos a TC e RM de abdome em nossa instituição, entre 2010 e 2015, com um intervalo máximo de três meses entre os dois exames. Selecionou-se um corte adquirido ao nível da cicatriz umbilical. A segmentação foi realizada com o algoritmo de crescimento de região do freeware utilizado. As reprodutibilidades intraobservador e interobservador foram avaliadas, assim como a acurácia da RM em relação à TC. Resultados: Trinta e um pacientes (14 homens e 17 mulheres; média de idade: 57 ± 15 anos) realizaram TC e RM (intervalo médio entre os exames: 28 ± 12 dias). A reprodutibilidade interobservador foi 82% para TC (viés = 1,52 cm2; p = 0,488), 86% para RM ponderada em T1 (viés = −4,36 cm2; p = 0,006) e 88% para RM ponderada em T2 (viés = −0,52 cm2; p = 0,735). A reprodutibilidade intraobservador foi 90% para TC (viés = 0,14 cm2; p = 0,912), 92% para RM ponderada em T1 (viés = −3,4 cm2; p = 0,035) e 90% para RM ponderada em T2 (viés = −0,30 cm2, p = 0,887). A reprodutibilidade entre a RM ponderada em T1 e a RM ponderada em T2 foi 87% (viés = −0,11 cm2; p = 0,957). Em comparação com a TC, a acurácia da RM ponderada em T1 e T2 foi 89% e 91%, respectivamente. Conclusão: O programa utilizado pode ser usado para quantificar o TAV na TC, na RM ponderada em T1 e na RM ponderada em T2. No geral, a acurácia da RM (em comparação com a TC) parece ser alta, assim como a reprodutibilidade intraobservador e interobservador. No entanto, a quantificação do TAV parece ser menos reprodutível nas sequências ponderadas em T1.