Search Results (57)

Background: Vascular dysfunction is increasingly recognized as a shared contributor to both cognitive impairment and late-life depression (LLD). However, the combined diagnostic value of cerebral hemodynamics, neuroimaging markers, and neuropsychological outcomes remains underexplored. This study aimed to investigate the associations be-tween transcranial Doppler (TCD) ultrasound parameters, cognitive performance, and depressive symptoms in older adults with mild cognitive impairment (MCI) and LLD. Importantly, we evaluated the integrative value of TCD-derived indices alongside MRI-confirmed white matter lesions (WMLs) and standardized neurocognitive and affective assessments. Methods: In this cross-sectional study, 96 older adults were enrolled including 78 cognitively unimpaired individuals and 18 with MCI. All participants underwent structured clinical, neuropsychological, and imaging evaluations including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS-15), MRI-based Fazekas scoring of WMLs, and TCD ultrasonography of the middle cerebral artery. Hemodynamic variables included mean blood flow velocity (MBFV), end-diastolic velocity (EDV), pulsatility index (PI), and resistive index (RI). Logistic regression and receiver operating characteristic (ROC) analyses were used to identify independent predictors of MCI. Results: Participants with MCI showed significantly lower MBFV and EDV, and higher PI and RI (p < 0.05 for all) compared with cognitively unimpaired participants. In multivariate analysis, lower MBFV (OR = 0.64, p = 0.02) and EDV (OR = 0.70, p = 0.03), and higher PI (OR = 3.2, p < 0.01) and RI (OR = 1.9, p < 0.01) remained independently associated with MCI. ROC analysis revealed excellent discriminative performance for RI (AUC = 0.919) and MBFV (AUC = 0.879). Furthermore, PI correlated positively with depressive symptom severity, while RI was inversely related to the GDS-15 scores. Conclusions: Our findings underscore the diagnostic utility of TCD-derived hemodynamic parameters—particularly RI and MBFV—in identifying early vascular contributions to cognitive and affective dysfunction in older adults. The integration of TCD with MRI-confirmed WML assessment and standardized cognitive/mood measures represents a novel and clinically practical multi-modal approach for neurovascular profiling in aging populations. Full article

Background: Femoroacetabular impingement (FAI) is a complex hip disorder characterized by abnormal contact between the femoral head and acetabulum, often leading to joint damage, chronic pain, and early-onset osteoarthritis. Despite MRI being the imaging modality of choice, diagnosis remains challenging due to subjective interpretation, lack of standardized imaging criteria, and difficulty differentiating symptomatic from asymptomatic cases. This study aimed to develop and externally validate radiomics-based machine learning (ML) models capable of classifying healthy, asymptomatic, and symptomatic FAI cases with high diagnostic accuracy and generalizability. Methods: A total of 82 hip MRI datasets (31 symptomatic, 31 asymptomatic, 20 healthy) from a single center were used for training and cross-validation. Radiomic features were extracted from four segmented anatomical regions (femur, acetabulum, gluteus medius, gluteus maximus). A four-step feature selection pipeline was implemented, followed by training 16 ML classifiers. External validation was conducted on a separate multi-center cohort of 185 symptomatic FAI cases acquired with heterogeneous MRI protocols. Results: The best-performing models achieved a cross-validation accuracy of up to 90.9% in distinguishing among healthy, asymptomatic, and symptomatic hips. External validation on the independent multi-center cohort demonstrated 100% accuracy in identifying symptomatic FAI cases. Since this metric reflects performance on symptomatic cases only, it should be interpreted as a detection rate (true positive rate) rather than overall multi-class accuracy. Gini index-based feature selection consistently outperformed F-statistic-based methods across all the models. Conclusions: This is the first study to systematically integrate radiomics and multiple ML models for FAI classification for these three phenotypes, trained on a single-center dataset and externally validated on multi-institutional MRI data. The demonstrated robustness and generalizability of radiomic features support their use in clinical workflows and future large-scale studies targeting standardized, data-driven FAI diagnosis. Full article

While multi-parametric magnetic resonance imaging (mpMRI) is known to be a specific and reliable modality for the diagnosis of non-metastatic prostate cancer (PC), positron emission tomography (PET) using ⁶⁸Ga labeled ligands targeting the prostate-specific membrane antigen (PSMA) is known for its reliable detection of prostate cancer, being the most sensitive modality for the assessment of the extra-prostatic extension of the disease and the establishment of a diagnosis, even before biopsy. Background/Objectives: Here, we compared these modalities in regards to the localization of intraprostatic cancer lesions prior to local HDR brachytherapy. Methods: A cohort of 27 patients received both mpMRI and PSMA-PET/CT. Based on 24 intraprostatic segments, two readers each scored the risk of tumor-like alteration in each imaging modality. The detectability was evaluated using receiver operating characteristic (ROC) analysis. The histopathological findings from biopsy were used as the gold standard in each segment. In addition, we applied a patient-based “congruence” concept to quantify the interobserver and intermodality agreement. Results: For the ROC analysis, we included 447 segments (19 patients), with their respective histological references. The two readers of the MRI reached an AUC of 0.770 and 0.781, respectively, with no significant difference (p = 0.75). The PET/CT readers reached an AUC of 0.684 and 0.608, respectively, with a significant difference (p < 0.001). The segment-wise intermodality comparison showed a significant superiority of MRI (AUC = 0.815) compared to PET/CT (AUC = 0.690) (p = 0.006). Via a patient-based analysis, a superiority of MRI in terms of relative agreement with the biopsy result was observed (n = 19 patients). We found congruence scores of 83% (MRI) and 76% (PET/CT, p = 0.034), respectively. Using an adjusted “near total agreement” score (adjacent segments with positive scores of 4 or 5 counted as congruent), we found an increase in the agreement, with a score of 96.5% for MRI and 92.7% for PET/CT, with significant difference (p = 0.024). Conclusions: This study suggests that in a small collective of low-/intermediate risk prostate cancer, mpMRI is superior for the detection of intraprostatic lesions as compared to PSMA-PET/CT. We also found a higher relative agreement between MRI and biopsy as compared to that for PET/CT. However, further studies including a larger number of patients and readers are necessary to draw solid conclusions. Full article

Accurate preoperative prediction of biochemical recurrence (BCR) in prostate cancer (PCa) is essential for treatment optimization, and demands an explicit focus on tumor microenvironment (TME). To address this, we developed MRMS-CNNFormer, an innovative framework integrating 2D multi-region (intratumoral, peritumoral, and periprostatic) and multi-sequence magnetic resonance imaging (MRI) images (T2-weighted imaging with fat suppression (T2WI-FS) and diffusion-weighted imaging (DWI)) with clinical characteristics. The framework utilizes a CNN-based encoder for imaging feature extraction, followed by a transformer-based encoder for multi-modal feature integration, and ultimately employs a fully connected (FC) layer for final BCR prediction. In this multi-center study (46 BCR-positive cases, 186 BCR-negative cases), patients from centers A and B were allocated to training (n = 146) and validation (n = 36) sets, while center C patients (n = 50) formed the external test set. The multi-region MRI-based model demonstrated superior performance (AUC, 0.825; 95% CI, 0.808–0.852) compared to single-region models. The integration of clinical data further enhanced the model’s predictive capability (AUC 0.835; 95% CI, 0.818–0.869), significantly outperforming the clinical model alone (AUC 0.612; 95% CI, 0.574–0.646). MRMS-CNNFormer provides a robust, non-invasive approach for BCR prediction, offering valuable insights for personalized treatment planning and clinical decision making in PCa management. Full article

This magnetic resonance imaging multimodal fusion study aims to automate the staging of endometrial cancer using deep learning and to compare the diagnostic performance of deep learning with that of radiologists in the staging of endometrial cancer. This study retrospectively investigated 122 patients with pathologically confirmed early EC from January 1, 2025 to December 31, 2021. Of these patients, 68 were in the International Federation of Gynecology and Obstetrics (FIGO) stage IA, and 54 were in FIGO stage IB. Based on the Swin transformer model and its proprietary SW-MSA (shift window multiple self-coherence) module, magnetic resonance imaging (MRI) images in each of the three planes (sagittal, coronal, and transverse) are cropped, enhanced, and classified, and fusion experiments in the three planes are performed simultaneously. Selecting one plane for the experiment, the accuracy of IA and IB classification was 0.988 in the sagittal, 0.96 in the coronal, and 0.94 in the transverse position, and classification accuracy after the fusion of three planes reached 1. Finally, the automatic classification method based on the Swin transformer has an accuracy of 1, a recall of 1, and a specificity of 1 for early EC classification. In this study, the multimodal fusion approach accurately classified early EC. It was comparable to what a radiologist would perform and simpler and more precise than previous methods that required segmenting followed by staging. Full article

Background/Objectives: This study aimed to create a pipeline for standardized data extraction from lumbar-spine MRI radiology reports using a large language model (LLM) and assess the agreement of the extracted data with research-grade semi-quantitative scoring. Methods: We included a subset of data from a multi-site NIH-funded cohort study of chronic low back pain (cLBP) participants. After initial prompt development, a secure application programming interface (API) deployment of OpenAIs GPT-4 was used to extract different classes of pathology from the clinical radiology report. Unsupervised UMAP and agglomerative clustering of the pathology terms’ embeddings provided insight into model comprehension for optimized prompt design. Model extraction was benchmarked against human extraction (gold standard) with F1 scores and false-positive and false-negative rates (FPR/FNR). Then, an expert MSK radiologist provided comprehensive research-grade scores of the images, and agreement with report-extracted data was calculated using Cohen’s kappa. Results: Data from 230 patients with cLBP were included (mean age 53.2 years, 54% women). The overall model performance for extracting data from clinical reports was excellent, with a mean F1 score of 0.96 across pathologies. The mean FPR was marginally higher than the FNR (5.1% vs. 3.0%). Agreement with comprehensive scoring was moderate (kappa 0.424), and the underreporting of lateral recess stenosis (FNR 63.6%) and overreporting of disc pathology (FPR 42.7%) were noted. Conclusions: LLMs can accurately extract highly detailed information on lumbar spine imaging pathologies from radiology reports. Moderate agreement between the LLM and comprehensive scores underscores the need for less subjective, machine-based data extraction from imaging. Full article

MOGAD is a demyelinating syndrome with the presence of antibodies against myelin oligodendrocyte glycoprotein, which is, next to multiple sclerosis and the neuromyelitis optica spectrum, one of the manifestations of the demyelinating process, more common in the pediatric population. MOGAD can take a variety of clinical forms: acute disseminated encephalomyelitis (ADEM), retrobulbar optic neuritis, often binocular (ON), transverse myelitis (TM), or NMOSD-like course (neuromyelitis optica spectrum disorders), less often encephalopathy. The course may be monophasic (40–50%) or polyphasic (50–60%), especially with persistently positive anti-MOG antibodies. Very rarely, the first manifestation of the disease, preceding the typical symptoms of MOGAD by 8 to 48 months, is focal seizures with secondary generalization, without typical demyelinating changes on MRI of the head. The paper presents a case of a 17-year-old patient whose first symptoms of MOGAD were focal epileptic seizures in the form of turning the head to the right with the elevation of the left upper limb and salivation. Seizures occurred after surgical excision of a tumor of the right adrenal gland (ganglioneuroblastoma). Then, despite a normal MRI of the head and the exclusion of onconeural antibodies in the serum and cerebrospinal fluid after intravenous treatment, a paraneoplastic syndrome was suspected. After intravenous steroid treatment and immunoglobulins, eight plasmapheresis treatments, and the initiation of antiepileptic treatment, the seizures disappeared, and no other neurological symptoms occurred for nine months. Only subsequent relapses of the disease with typical radiological and clinical picture (ADEM, MDEM, recurrent ON) allowed for proper diagnosis and treatment of the patient both during relapses and by initiating supportive treatment. The patient’s case allows us to analyze the multi-phase, clinically diverse course of MOGAD and, above all, indicates the need to expand the diagnosis of epilepsy towards demyelinating diseases: determination of anti-MOG and anti-AQP4 antibodies. Full article

Musculoskeletal ultrasound (US) imaging faces challenges such as operator experience, limited spatial flexibility, and high personnel costs. This study introduces an Automated Robotic Ultrasound Scanning (ARUS) system that integrates key technological advancements to automate the ultrasound scanning procedure with the robot, including anatomical target localization, automatic trajectory generation, deep-learning-based segmentation, and 3D reconstruction of musculoskeletal structures. The ARUS system consists of a robotic arm, ultrasound imaging, and stereo vision for precise anatomical area detection. A Graphical User Interface (GUI) facilitates a flexible selection of scanning trajectories, improving user interaction and enabling customized US scans. To handle complex and dynamic curvatures on the skin, together with anatomical area detection, the system employs a hybrid position–force control strategy based on the generated trajectory, ensuring stability and accuracy. Additionally, the utilized RA-UNet model offers multi-label segmentation on the bone and muscle tissues simultaneously, which incorporates residual blocks and attention mechanisms to enhance segmentation accuracy and robustness. A custom musculoskeletal phantom was used for validation. Compared to the reference 3D reconstruction result derived from the MRI scan, ARUS achieved a 3D reconstruction root mean square error (RMSE) of 1.22 mm, with a mean error of 0.94 mm and a standard deviation of 0.77 mm. The ARUS system extends 3D musculoskeletal imaging capacity by enabling both bones and muscles to be segmented and reconstructed into 3D shapes in real time and simultaneously. These features suggest significant potential as a cost-effective and reliable option for musculoskeletal examination and diagnosis in real-time applications. Full article

Introduction: Prostate biopsy results form the mainstay of patient care. However, there is often significant discordance between the biopsied histology and the ‘true’ histology shown on a radical prostatectomy (RP). Discordance in pathology can lead to the mismanagement of patients, potentially missing clinically significant cancer and delaying treatment. There have been many advancements to improve the concordance of pathology and more accurately counsel patients; most notably, the induction of pre-biopsy mpMRIs has become a gold standard to aid in triaging and identifying clinically significant cancers, and also to facilitate ‘targeted’ biopsies. Although there have been multiple reviews on MRI-targeted biopsies, upgrading remains an ongoing phenomenon. Aim: To assess the rates of prostate cancer upgrading and the clinical implication of upgrading on NCCN stratification. Methods: We conducted a retrospective audit of 2994 men with non-metastatic prostate cancer diagnosed between 2010 and 2019 who progressed to a radical prostatectomy within 1 year of diagnosis without alternative cancer treatment from the multi-institutional South Australia Prostate Cancer Clinical Outcomes Collaborative registry. The study compared the histological grading between the biopsies and radical prostatectomies of men with prostate cancer and the varying rates of upgrading and downgrading for patients with and without a pre-biopsy MRI. Data were also obtain on suspected confounding variables; age, PSA, time to RP, T-stage at diagnosis and RP, number of cores, number of positive cores, prostate size, tumour volume and procedure type. The results were assessed through cross tabulation and uni- and multi-variate logistic regression while adjusting for confounders. Results: Upgrading occurred in (926) 30.9% of patients and downgrading in (458) 15.3% of patients. In total, 71% (410/579) of grade group 1 and 24.9% (289/1159) of grade group 2 were upgraded following a radical prostatectomy. By contrast, 33.4% (373/1118) of patients without prebiopsy MRI were upgraded at RP compared to 29.5% (553/1876) of the patients who received a pre-biopsy MRI. When analysed on a uni-variate level, the inclusion of a pre-biopsy MRI demonstrated a statically significant decrease in upgrading of the patient’s pathology and NCCN risk stratification (p = 0.026, OR 0.83, CI 0.71–0.98) (p = 0.049, OR 0.82, CI 0.64–1.01). However, when adjusted for confounders, the use of an MRI did not maintain a statistically significance. Conclusions: When considering the multiple variables associated with tumour upgrading, a pre-biopsy MRI did not show a statistically significant impact. However, upgrading of Gleason Grade Group following a prostatectomy is an ongoing phenomenon which can carry significant treatment implications and should remain a consideration with patients and clinicians when making decisions around treatment pathways. More research is still required to understand and improve biopsy grading to prevent further upgrading from affecting treatment choices. Full article

Introduction. Post-coronavirus disease-2019 (COVID-19) patients may develop cardiac symptoms. We hypothesized that cardiovascular magnetic resonance (CMR) can assess the background of post-COVID-19 cardiac symptoms using multi-parametric evaluation. We aimed to conduct an investigation of symptomatic patients with post-COVID-19 syndrome using CMR (INSPIRE-CMR). Methods. INSIPRE-CMR is a retrospective multicenter study including 174 patients from five centers referred for CMR due to cardiac symptoms. CMR was performed using 3.0 T/1.5 T system (24%/76%, respectively). Myocardial inflammation was determined by the updated Lake Louise criteria. Results. Further, 174 patients with median age of 40 years (IQR: 26–54), 72 (41%) were women, and 17 (9.7%) had a history of autoimmune disease, muscular dystrophy, or cancer. In total, 149 (86%) patients were late gadolinium enhanced (LGE)-positive with a non-ischemic pattern, and of those evaluated with the updated Lake Louise criteria, 141/145 (97%) had ≥1 pathologic T1 index. Based on the T2-criterion, 62/173 (36%) patients had ≥1 pathologic T2 index. Collectively, 48/145 (33%) patients had both positive T1- and T2-criterion. A positive T2-criterion or a combination of a positive T1- and T2-criterion were significantly more common amongst patients with severe COVID-19 [45 (31%) vs. 17 (65%), p = 0.001 and 32 (27%) vs. 16 (64%), p < 0.001, respectively]. During the one-year evaluation, available for 65/174 patients, shortness of breath, chest pain, and arrhythmia were identified in 7 (4%), 15 (8.6%), and 43 (24.7%), respectively. CMR evaluation, available in a minority of them, showed mildly reduced LVEF, while nat T1 mapping and EVC remained at levels higher than the normal values of the local MRI units. Conclusions. The majority of post-COVID-19 patients with cardiac symptoms presented non-ischemic LGE and abnormalities in T1 and T2-based indices. Multi-parametric CMR reveals important information on post-COVID-19 patients, supporting its role in short/long-term evaluation. Full article

Background: Fat infiltration in skeletal muscle is related to declining muscle strength, whereas excess subcutaneous fat is implicated in the development of metabolic diseases. Methods: Using multi-slice axial T2-weighted (T2w) MR images, this retrospective study characterized muscle fat infiltration (MFI) and fat distribution in the lower extremity of 107 subjects (64M/43F, age 11–79 years) with diverse ethnicities (including White, Black, Latino, and Asian subjects). Results: MRI data analysis shows that MFI, evaluated by the relative intensities of the pixel histogram profile in the calf muscle, tends to increase with both age and BMI. However, statistical significance was found only for the age correlation in women (p < 0.002), and the BMI correlation in men (p = 0.04). Sex disparities were also seen in the fat distribution, which was assessed according to subcutaneous fat thickness (SFT) and the fibula bone marrow cross-sectional area (BMA). SFT tends to decrease with age in men (p < 0.01), whereas SFT tends to increase with BMI only in women (p < 0.01). In contrast, BMA tends to increase with age in women (p < 0.01) and with BMI in men (p = 0.04). Additionally, MFI is positively correlated with BMA but not with SFT, suggesting that compromised bone structure may contribute to fat infiltration in the surrounding skeletal muscle. Conclusions: The findings of this study highlight a sex factor affecting MFI and fat distribution, which may offer valuable insights into effective strategies to prevent and treat MFI in women versus men. Full article

Background: Existing methods for MRI brain tumor segmentation often suffer from excessive model parameters and suboptimal performance in delineating tumor boundaries. Methods: For this issue, a lightweight MRI brain tumor segmentation method, enhanced by hierarchical feature fusion (EHFF), is proposed. This method reduces model parameters while improving segmentation performance by integrating hierarchical features. Initially, a fine-grained feature adjustment network is crafted and guided by global contextual information, leading to the establishment of an adaptive feature learning (AFL) module. This module captures the global features of MRI brain tumor images through macro perception and micro focus, adjusting spatial granularity to enhance feature details and reduce computational complexity. Subsequently, a hierarchical feature weighting (HFW) module is constructed. This module extracts multi-scale refined features through multi-level weighting, enhancing the detailed features of spatial positions and alleviating the lack of attention to local position details in macro perception. Finally, a hierarchical feature retention (HFR) module is designed as a supplementary decoder. This module retains, up-samples, and fuses feature maps from each layer, thereby achieving better detail preservation and reconstruction. Results: Experimental results on the BraTS 2021 dataset demonstrate that the proposed method surpasses existing methods. Dice similarity coefficients (DSC) for the three semantic categories ET, TC, and WT are 88.57%, 91.53%, and 93.09%, respectively. Full article

Zebrafish (Danio rerio) is an important animal model for a wide range of neurodegenerative diseases. However, obtaining the cellular resolution that is essential for studying the zebrafish brain remains challenging as it requires high spatial resolution and signal-to-noise ratios (SNR). In the current study, we present the first MRI results of the zebrafish brain at the state-of-the-art magnetic field strength of 28.2 T. The performance of MRI at 28.2 T was compared to 17.6 T. A 20% improvement in SNR was observed at 28.2 T as compared to 17.6 T. Excellent contrast, resolution, and SNR allowed the identification of several brain structures. The normative T₁ and T₂ relaxation values were established over different zebrafish brain structures at 28.2 T. To zoom into the white matter structures, we applied diffusion tensor imaging (DTI) and obtained axial, radial, and mean diffusivity, as well as fractional anisotropy, at a very high spatial resolution. Visualisation of white matter structures was achieved by short-track track-density imaging by applying the constrained spherical deconvolution method (stTDI CSD). For the first time, an algorithm for stTDI with multi-shell multi-tissue (msmt) CSD was tested on zebrafish brain data. A significant reduction in false-positive tracks from grey matter signals was observed compared to stTDI with single-shell single-tissue (ssst) CSD. This allowed the non-invasive identification of white matter structures at high resolution and contrast. Our results show that ultra-high field DTI and tractography provide reproducible and quantitative maps of fibre organisation from tiny zebrafish brains, which can be implemented in the future for a mechanistic understanding of disease-related microstructural changes in zebrafish models of various brain diseases. Full article

We aim (a) to introduce an easy-to-perform multi-echo gradient-echo sequence (mGRE) for the detection of hemosiderin deposition in the ankle joints of boys with haemophilia (b) to explore the associations between the presence and severity of hemosiderin deposition and the other components of haemophilic arthropathy, the clinical score, and the number and chronicity of joint bleeds. An MRI of 41 ankle joints of 21 haemophilic boys was performed on a 3 T MRI system using an mGRE sequence in addition to the conventional protocol. Conventional MRI and mGRE were separately and independently assessed by three readers, namely, two musculoskeletal radiologists and a general radiologist for joint hemosiderin. We set as a reference the consensus reading of the two musculoskeletal radiologists, who also evaluated the presence of synovial thickening, effusion, and osteochondral changes. Excellent inter-reader agreement was obtained using the mGRE sequence compared to the conventional protocol (ICC: 0.95–0.97 versus 0.48–0.89), with superior sensitivity (90–95% versus 50–85%), specificity (95.2–100% versus 76.2–95.2%), and positive (95–100% versus 71–94.4%) and negative predictive value (91.3–95.5% versus 87–63%). Hemosiderin deposition was associated with osteochondral changes, synovial thickening, clinical score, and the total number of ankle bleeds, while it was inversely related with the time elapsed between the last joint bleed and MRI. (p < 0.05). The application of an mGRE sequence significantly improved hemosiderin detection, even when performed by the less experienced reader. Joint hemosiderin deposition was associated with the other components of haemophilic arthropathy and was mostly apparent in recent joint bleeds. Full article

Background: The aim was to assess the diagnostic accuracy of DECT in diagnosing Achilles tendon tears, using MRI as the reference for diagnosis. Methods: This feasibility study conducted prospectively at a single center included consecutive patients suffering from ankle pain who underwent DECT and MRI between April 2023 and October 2023. A total of three radiologists, blinded to the patient’s clinical data, assessed the images. Achille Tendon injuries were diagnosed in case of thickened and inflamed tendons or in case of a partial or complete tear. Diagnostic accuracy values of DECT were calculated using a multi-reader approach. Inter-observer agreement was calculated using k statistics. Results: The final study population included 22 patients (mean age 48.5 years). At MRI, Achille’s tendon lesion was present in 12 cases (54.5%) with 2 cases of complete rupture, 8 cases of partial tear (5 with tendon retraction), and 2 cases of tendon thickening. The mean thickness of injured tendons was 10 mm. At DECT, R1 was allowed to correctly classify 20/22 cases (90.9%), R2 19/22 cases (86.4%), and R3 18/22 cases (81.8%). At DECT, the mean thickness of the positively scored tendon was 10 mm for R1, 10.2 mm for R2, and 9.8 mm for R3. A very good agreement was achieved with regard to the evaluation of tears (k = 0.94), thickness (k = 0.96), and inflammatory changes (k = 0.82). Overall agreement was very good (k = 0.88). Conclusions: DECT showed a good diagnostic performance in identifying Achille’s tendon tears, with respect to MRI. Full article