Machine learning-augmented and microspectroscopy-informed multiparametric MRI for the non-invasive prediction of articular cartilage composition.

BACKGROUND: Articular cartilage degeneration is the hallmark change of osteoarthritis, a severely disabling disease with high prevalence and considerable socioeconomic and individual burden. Early, potentially reversible cartilage degeneration is characterized by distinct changes in cartilage composition and ultrastructure, while the tissue's morphology remains largely unaltered. Hence, early degenerative changes may not be diagnosed by clinical standard diagnostic tools. METHODS: Against this background, this study introduces a novel method to determine the tissue composition non-invasively. Our method involves quantitative MRI parameters (i.e., T1, T1ρ, T2 and [Formula: see text] maps), compositional reference measurements (i.e., microspectroscopically determined local proteoglycan [PG] and collagen [CO] contents) and machine learning techniques (i.e., artificial neural networks [ANNs] and multivariate linear models [MLMs]) on 17 histologically grossly intact human cartilage samples. RESULTS: Accuracy and precision were higher in ANN-based predictions than in MLM-based predictions and moderate-to-strong correlations were found between measured and predicted compositional parameters. CONCLUSION: Once trained for the clinical setting, advanced machine learning techniques, in particular ANNs, may be used to non-invasively determine compositional features of cartilage based on quantitative MRI parameters with potential implications for the diagnosis of (early) degeneration and for the monitoring of therapeutic outcomes.

Automated classification of celiac disease during upper endoscopy: Status quo and quo vadis.

A large amount of digital image material is routinely captured during esophagogastroduodenoscopies but, for the most part, is not used for confirming the diagnosis process of celiac disease which is primarily based on histological examination of biopsies. Recently, considerable effort has been undertaken to make use of image material by developing semi- or fully-automated systems to improve the diagnostic workup. Recently, focus was especially laid on developing state-of-the-art deep learning architectures, exploiting the endoscopist's expert knowledge and on making systems fully automated and thereby completely observer independent. In this work, we summarize recent trends in the field of computer-aided celiac disease diagnosis based on upper endoscopy and discuss about recent progress, remaining challenges, limitations currently prohibiting a deployment in clinical practice and future efforts to tackle them.

3D cross sectional view to investigate the morphology of internal carotid artery plaques. Is 3D ultrasound superior to 2D ultrasound?

PURPOSE: Previous studies have demonstrated that plaques from the internal carotid artery with lower median grayscale values are associated with higher complication rates in the perinterventional course. A repeatedly cited limitation of ultrasound is that the single plane used to calculate the median grayscale value is only two dimensional. The goal of this study was to compare the median grayscale value as determined using two dimensional cuts versus three dimensional data sets. MATERIALS AND METHODS: Seventy-one cuts of 24 thromboendarterectomy samples from 19 patients were analyzed using 3D data sets. The ultrasound data sets were obtained using a 10 MHz probe in 3D mode in a special chamber and were evaluated by two investigators. Additionally, a longitudinal view of the samples was made using B mode according to a standardized protocol. RESULTS: There was a significant correlation between the 2D and 3D data as assessed by two observers (p < 0.001, intraclass correlation [ICC] > 0.895) and at different times (p < 0,001, ICC > 0.935). Comparison of the 3D transverse cuts with the longitudinal B mode cuts also showed a highly significant association between the two methods (p < 0.001, R = 0.800). 97.2 % of the measured values were within the limits of agreement, reflecting the concordance of the both methods. CONCLUSION: The superiority of three dimensional ultrasound with respect to two dimensional ultrasound was not able to be demonstrated using this standardized in vitro procedure to examine the echolucency in extracranial internal carotid artery plaques.

Correction of susceptibility artifacts in diffusion tensor data using non-linear registration.

Diffusion tensor imaging can be used to localize major white matter tracts within the human brain. For surgery of tumors near eloquent brain areas such as the pyramidal tract this information is of importance to achieve an optimal resection while avoiding post-operative neurological deficits. However, due to the small bandwidth of echo planar imaging, diffusion tensor images suffer from susceptibility artifacts resulting in positional shifts and distortion. As a consequence, the fiber tracts computed from echo planar imaging data are spatially distorted. We present an approach based on non-linear registration using Bézier functions to efficiently correct distortions due to susceptibility artifacts. The approach makes extensive use of graphics hardware to accelerate the non-linear registration procedure. An improvement presented in this paper is a more robust and efficient optimization strategy based on simultaneous perturbation stochastic approximation (SPSA). Since the accuracy of non-linear registration is crucial for the value of the presented correction method, two techniques were applied in order to prove the quality of the proposed framework. First, the registration accuracy was evaluated by recovering a known transformation with non-linear registration. Second, landmark-based evaluation of the registration method for anatomical and diffusion tensor data was performed. The registration was then applied to patients with lesions adjacent to the pyramidal tract in order to compensate for susceptibility artifacts. The effect of the correction on the pyramidal tract was then quantified by measuring the position of the tract before and after registration. As a result, the distortions observed in phase encoding direction were most prominent at the cortex and the brainstem. The presented approach allows correcting fiber tract distortions which is an important prerequisite when tractography data are integrated into a stereotactic setup for intra-operative guidance.