Differential imaging features of widening and pseudo-widening of the sacroiliac joints

Summary: A novel technique is proposed in this study to avoid negative illumination and involves only one round of measurements to recover object information, with half the measurements compared to conventional schemes. This method is not limited to Hadamard pattern and may have relevance for the general development of computational ghost imaging.

Summary: This study aimed to assess the performance of rheumatologists in interpreting inflammatory and structural damage to sacroiliac joints in axSpA patients, compared to radiologists. The results showed substantial agreement between senior and junior radiologists, as well as between senior radiologists and senior rheumatologists, while moderate agreement was observed with junior rheumatologists.

Summary: MRI examination of the sacroiliac joints is helpful in identifying early axial spondyloarthritis, but it is important to recognize the potential differential diagnoses that can mimic inflammatory sacroiliitis in clinical practice.

Summary: This study developed a fully automated machine learning algorithm for predicting bone marrow edema on sacroiliac joint MRI. The algorithm automatically locates and segments the regions of interest and showed good performance in cross-validation and test datasets.

Summary: This article discusses whether MRI can replace radiographs for diagnostic evaluation and classification of axial spondyloarthritis, and provides answers to specific questions based on current data.

Summary: This study analyzed the prevalence of anatomical variants of sacroiliac joints (SIJ) using sCT images in 215 patients suspected of sacroiliitis. Over 80% of the evaluated joints had SIJ variants, with the most common being iliosacral complex, bipartite iliac bony plate, and crescent iliac bony plate. The occurrence of these variants was associated with age, sex, and body weight, with iliosacral complex being more frequent in men and associated with higher BMI.

Summary: This article reviews important imaging and clinical features to elucidate causes of lymphadenopathy in HIV-infected patients. Causes of HIV lymphadenopathy can be categorized as inflammatory or reactive, infectious, and neoplastic. Demographic characteristics, clinical presentations, CD4 lymphocyte counts, and radiologic features are important for identifying likely causes of lymphadenopathy.

Summary: This study developed a deep neural network to detect changes in the sacroiliac joints indicative of axial spondyloarthritis on MRI. The results showed that the neural network had high accuracy in detecting inflammatory and structural changes.

Summary: This study evaluates the performance of a fully automated algorithm to detect active inflammation in the form of bone marrow edema (BME) in iliac and sacral bones, depending on the quality of the coronal oblique plane in patients with axial spondyloarthritis (axSpA). The results were assessed based on the technical correctness of MRI examination of the sacroiliac joints (SIJs). A total of 173 patients with suspected axSpA were included in the study. In order to verify the correctness of the MRI, a deviation angle was measured on the slice acquired in the sagittal plane in the T2-weighted sequence. This angle was located between the line drawn between the posterior edges of S1 and S2 vertebrae and the line that marks the actual plane in which the slices were acquired in T1 and STIR sequences. All examinations were divided into quartiles according to the deviation angle measured in degrees as follows: 1st group [0; 2.2], 2nd group (2.2; 5.7], 3rd group (5.7; 10] and 4th group (10; 29.2]. Segmentations of the sacral and iliac bones were acquired manually and automatically using the fully automated algorithm on the T1 sequence. The Dice coefficient for automated bone segmentations with respect to reference manual segmentations was 0.9820 (95% CI [0.9804, 0.9835]). Examinations of BME lesions were assessed using the SPARCC scale (in 68 cases SPARCC > 0). Manual and automatic segmentations of the lesions were performed on STIR sequences and compared. The sensitivity of detection of BME ranged from 0.58 (group 1) to 0.83 (group 2) versus 0.76 (total), while the specificity was equal to 0.97 in each group. The study indicates that the performance of the algorithm is satisfactory regardless of the deviation angle.

Summary: The diagnostic performance of MRI in detecting erosions in the sacroiliac joints is significantly improved when using T1w images with fat suppression compared to images without fat suppression.

Summary: This article investigates MRI features correlated with spondyloarthritis and develops detection models for inflammatory sacroiliitis using different MRI techniques. The results show that the performance of these models is equivalent to that of specialists and can effectively identify inflammatory sacroiliitis.

Summary: In patients with axial spondyloarthritis (axSpA), sex-specific differences were found in the extent of sacroiliac joint (SIJ) bone marrow edema (BME) with HLA-B27 being relevant only in males and postpartum state being important in females. This finding may have implications for the pathogenesis of axSpA.

Summary: Adenosine deaminase 2 deficiency is a rare genetic disorder with diverse neuroimaging features, including brain ischemic and/or hemorrhagic strokes, spinal infarcts, intracranial aneurysms, and cerebral microbleeds. Early clinical presentation, positive family history, inflammatory flares, and systemic abnormalities should raise suspicion of the disease.

Summary: The study evaluated the reliability and accuracy of hepatic periportal space widening and other qualitative imaging features for predicting hepatic fibrosis through liver MR elastography. Periportal space widening showed high sensitivity but limited specificity, while features like surface nodularity had higher specificity but lower sensitivity. The size of periportal space at the main portal vein also showed significant agreement with the qualitative periportal space widening.