Radiological features accompanying peroneus brevis split rupture revealed on magnetic resonance imaging - a cohort study.

BACKGROUND: Peroneal split tears are an underrated cause of ankle pain. While magnetic resonance imaging (MRI) is useful for diagnosis, split tears are challenging to identify. The aim of the study was to investigate the association of peroneus brevis split rupture with abnormalities of the superior peroneal retinaculum (SPR), anterior talofibular ligament (ATFL), calcaneofibular ligament (CFL), joint effusion, morphology of the malleolar groove, presence of the bone marrow oedema and prominent peroneal tuberculum. METHODS: Ankle MRI cases were assessed by independent observers retrospectively in two groups: one with peroneus brevis split tears (n = 80) and one without (control group, n = 115). Two observers evaluated the soft tissue lesions, and three graded the bone lesions. Fisher's exact test and Pearson correlation were used for analysis. The Bonferroni-Holm method (B-H) was used to adjust for multiple comparisons. RESULTS: Only bone marrow edema in the posterior part of the lateral malleolus was significantly (p < 0.05) more common in the split tear group after applying B-H. SPR total rupture was seen only in the experimental group. No differences in incidence of ATFL and CFL lesions or other SPR lesions were noticed (p < 0.05). CONCLUSION: Bone marrow edema in the posterior part of the lateral malleolus is associated with peroneus split tears on MRI.

Intraobserver and Interobserver Agreement between Six Radiologists Describing mpMRI Features of Prostate Cancer Using a PI-RADS 2.1 Structured Reporting Scheme.

Clinical practice has revealed ambiguities in PI-RADS v2.1 scoring, but a limited number of studies are available that validate the interreader and intrareader reproducibility of the mpMRI PI-RADS lexicon. We decomposed the PI-RADS rules into a set of common data elements to evaluate the inter- and intraobserver agreement in assessing the individual features included in the PI-RADS lexicon. Six radiologists (three highly experienced, three less experienced) in two sessions independently read thirty-two lesions in the peripheral and transition zone using the structured reporting tool, blinded to clinical MRI indication. The highest agreement between radiologists was observed for the abnormality detection, the evaluation of the type of signal intensity, and the characteristic of benign prostatic hyperplasia. Moderate agreement was reported for dynamic contrast-enhanced images. This resulted in a decrease in abnormality detection (PA = 76.5%) and enhancement indication (PA = 77.3%). The lowest agreement was observed for highly subjective features: shape, signal intensity level, and type of lesion margins. The results indicate the limitations of the PI-RADS v2.1 lexicon in relation to interreader and intrareader reproducibility. We have demonstrated that it is possible to develop structured reporting systems standardized according to the PI-RADS lexicon.

Anatomical variants of the acromioclavicular joint influence its visibility in the standard MRI protocol in patients aged 18-31 years.

PURPOSE: Visualization of a structure in orthogonal planes is essential for correct radiological assessment. The aim was to assess the utility of the standard MRI protocol for the shoulder in the assessment of the acromioclavicular joint (ACJ). METHODS: A total of 204 MRI scans of the shoulder were re-reviewed. Visibility of the ACJ in orthogonal planes was assessed, and the type of acromion and the angle between the ACJ and the glenoid cavity were assessed by two observers. RESULTS: Agreement in the assessment of ACJ visibility was moderate to substantial. The ACJ was visible in the three anatomical views in 48% (confidence interval [CI] 95% = [41-54%]) of the examinations, and no significant difference regarding gender or age was noticed. The mean angle between the ACJ and the glenoid cavity was 41.12 deg. CI95% = (39.72, 42.53) in the axial plane, 33.39 deg. CI95% = (31.33, 35.45) in the coronal plane and 52.49 deg. CI95% = (50.10, 54.86) in the sagittal plane. When the ACJ was visible in the sagittal and axial planes, significant differences were noticed in the remaining planes (p < .05). CONCLUSION: Anatomical variations of the ACJ influence its visibility in the standard MRI protocol for examining the shoulder, making this protocol insufficient for ACJ assessment in the examined population.

Effect of domain knowledge encoding in CNN model architecture-a prostate cancer study using mpMRI images.

BACKGROUND: Prostate cancer is one of the most common cancers worldwide. Currently, convolution neural networks (CNNs) are achieving remarkable success in various computer vision tasks, and in medical imaging research. Various CNN architectures and methodologies have been applied in the field of prostate cancer diagnosis. In this work, we evaluate the impact of the adaptation of a state-of-the-art CNN architecture on domain knowledge related to problems in the diagnosis of prostate cancer. The architecture of the final CNN model was optimised on the basis of the Prostate Imaging Reporting and Data System (PI-RADS) standard, which is currently the best available indicator in the acquisition, interpretation, and reporting of prostate multi-parametric magnetic resonance imaging (mpMRI) examinations. METHODS: A dataset containing 330 suspicious findings identified using mpMRI was used. Two CNN models were subjected to comparative analysis. Both implement the concept of decision-level fusion for mpMRI data, providing a separate network for each multi-parametric series. The first model implements a simple fusion of multi-parametric features to formulate the final decision. The architecture of the second model reflects the diagnostic pathway of PI-RADS methodology, using information about a lesion's primary anatomic location within the prostate gland. Both networks were experimentally tuned to successfully classify prostate cancer changes. RESULTS: The optimised knowledge-encoded model achieved slightly better classification results compared with the traditional model architecture (AUC = 0.84 vs. AUC = 0.82). We found the proposed model to achieve convergence significantly faster. CONCLUSIONS: The final knowledge-encoded CNN model provided more stable learning performance and faster convergence to optimal diagnostic accuracy. The results fail to demonstrate that PI-RADS-based modelling of CNN architecture can significantly improve performance of prostate cancer recognition using mpMRI.

Hypodensity extractor: a phantom study.

We report on the extraction procedures of low-contrast symptomatic hypodensity optimized for a computed tomography-based diagnosis. The specific application is brain imaging with enhanced perception of hypodense areas which are direct symptoms of acute ischemia. A standard low-contrast phantom, as commonly employed in dosimetry and imaging quality evaluation, was used to derive numeric criteria for assessing the extraction effectiveness. Our proposed procedure is based on multiscale analysis of the image data expanded over the frames of wavelets, curvelets or complex wavelets, followed by nonlinear approximation of the symptom signatures. Apparent subtle density changes in the phantom were evaluated using computational metrics and subjective ratings. We discuss the advantages and disadvantages of our proposed optimized hypodensity extraction procedures.