Automated selection of mid-height intervertebral disc slice in traverse lumbar spine MRI using a combination of deep learning feature and machine learning classifier

Abnormalities and defects that can cause lumbar spinal stenosis often occur in the Intervertebral Disc (IVD) of the patient’s lumbar spine. Their automatic detection and classification require an application of an image analysis algorithm on suitable images, such as mid-sagittal images or traverse mid-height intervertebral disc slices, as inputs. Hence the process of selecting and separating these images from other medical images in the patient’s set of scans is necessary. However, the technological progress in making this process automated is still lagging behind other areas in medical image classification research. In this paper, we report the result of our investigation on the suitability and performance of different approaches of machine learning to automatically select the best traverse plane that cuts closest to the half-height of an IVD from a database of lumbar spine MRI images. This study considers images features extracted using eleven different pre-trained Deep Convolution Neural Network (DCNN) models. We investigate the effectiveness of three dimensionality-reduction techniques and three feature-selection techniques on the classification performance. We also investigate the performance of five different Machine Learning (ML) algorithms and three Fully Connected (FC) neural network learning optimizers which are used to train an image classifier with hyperparameter optimization using a wide range of hyperparameter options and values. The different combinations of methods are tested on a publicly available lumbar spine MRI dataset consisting of MRI studies of 515 patients with symptomatic back pain. Our experiment shows that applying the Support Vector Machine algorithm with a short Gaussian kernel on full-length image features extracted using a pre-trained DenseNet201 model is the best approach to use. This approach gives the minimum per-class classification performance of around 0.88 when measured using the precision and recall metrics. The median performance measured using the precision metric ranges from 0.95 to 0.99 whereas that using the recall metric ranges from 0.93 to 1.0. When only considering the L3/L4, L4/L5, and L5/S1 classes, the minimum F1-Scores range between 0.93 to 0.95, whereas the median F1-Scores range between 0.97 to 0.99.

MRI in Axial Spondyloarthritis: Position Statements from the Hong Kong Society of Rheumatology

The Hong Kong Society of Rheumatology (HKSR) has developed evidence-based position statements on the use of magnetic resonance imaging (MRI) in axial spondyloarthritis (axSpA). A special interest group (SIG) developed the statements based on the literature review of the following research questions: 1) In whom should SI joint MRI be done to diagnose axSpA? 2) Should spine MRI be performed for diagnosis of axSpA? 3) Should spine and SI joint MRI be used in the assessment and monitoring of disease activity? 4) Which MRI sequence(s) should be used in axSpA? These statements have at least 75% agreement from voting rheumatology fellows of HKSR, and are developed to serve as local practice guidelines for rheumatologists and musculoskeletal radiologists in the management of axSpA.

Proton Density Fat Fraction Spine MRI for Differentiation of Erosive Vertebral Endplate Degeneration and Infectious Spondylitis

Vertebral Modic type 1 (MT1) degeneration may mimic infectious disease on conventional spine magnetic resonance imaging (MRI), potentially leading to additional costly and invasive investigations. This study evaluated the diagnostic performance of the proton density fat fraction (PDFF) for distinguishing MT1 degenerative endplate changes from infectious spondylitis. A total of 31 and 22 patients with equivocal diagnosis of MT1 degeneration and infectious spondylitis, respectively, were retrospectively enrolled in this IRB-approved retrospective study and examined with a chemical-shift encoding (CSE)-based water-fat 3D six-echo modified Dixon sequence in addition to routine clinical spine MRI. Diagnostic reference standard was established according to histopathology or clinical and imaging follow-up. Intravertebral PDFF [%] and PDFFratio (i.e., vertebral endplate PDFF/normal vertebrae PDFF) were calculated voxel-wise within the single most prominent edematous bone marrow lesion per patient and examined for differences between MT1 degeneration and infectious spondylitis. Mean PDFF and PDFFratio of infectious spondylitis were significantly lower compared to MT1 degenerative changes (mean PDFF, 4.28 ± 3.12% vs. 35.29 ± 17.15% [p < 0.001]; PDFFratio, 0.09 ± 0.06 vs. 0.67 ± 0.37 [p < 0.001]). The areas under the curve (AUC) and diagnostic accuracies were 0.977 (p < 0.001) and 98.1% (cut-off at 12.9%) for PDFF and 0.971 (p < 0.001) and 98.1% (cut-off at 0.27) for PDFFratio. Our data suggest that quantitative evaluation of vertebral PDFF can provide a high diagnostic accuracy for differentiating erosive MT1 endplate changes from infectious spondylitis.

Prevalence and clinical potential of extraspinal incidental findings in lumbosacral spine MRI of patients with suspected disc diseases

BACKGROUND Incidental findings could be observed in organs close to the spine while reporting lumbosacral spine magnetic resonance imaging (MRI). This study aimed to report the prevalence and clinical potential of extraspinal incidental findings in lumbosacral MRI of patients with suspected disc diseases. METHODS This single-centered cross-sectional study was carried out on 420 consecutive adult patients who underwent lumbar spine MRI for suspected disc disease from January to July 2019. The MRI was checked for the presence of extraspinal incidental findings, and each finding was categorized according to the body organ and its clinical significance. Each MRI plane that best displayed the findings was recorded, and the association between the findings and patient's age and sex was determined. RESULTS Of 420 samples, 135 cases showed extraspinal findings (32.1%), and 7.6% of the patients displayed suspicious lesions. The urinary tract was the most common system (18.6%) to display both clinically significant (5.0%) and benign findings (13.6%), and the axial MRI section was the plane which showed most of the incidental findings. Incidental findings in any body system were rarely found in the younger patients. Females had significantly higher benign findings than males (p = 0.002). CONCLUSIONS The prevalence of extraspinal findings in lumbosacral spine MRI is high, and some are significant. Most findings are related to the urinary tract and best displayed in the axial plane.