Search

Neuroimaging is increasingly being included in clinical trials of Huntington's disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD. [ABSTRACT FROM AUTHOR]

Spondyloarthritis (SpA) is characterized by inflammatory back pain. Magnetic resonance imaging (MRI) was the earlier gold standard technique for detecting early inflammatory change. We reassessed the diagnostic utility of sacroiliac joint/sacrum (SIS) ratios of single-photon emission computed tomography/computed tomography (SPECT/CT) for identifying sacroiliitis. We aimed to investigate of SPECT/CT in diagnosing SpA using a rheumatologist's visual scoring of SIS ratios assessment. We conducted a single-center, medical records review study of patients with lower back pain who underwent bone SPECT/CT from August 2016 to April 2020. We employed semiquantitative visual bone scoring methods of SIS ratio. The uptake of each sacroiliac joint was compared to that of the sacrum (0–2). A score of 2 for the sacroiliac joint of either side was considered diagnostic of sacroiliitis. Of the 443 patients assessed, 40 had axial SpA (axSpA), 24 being radiographic axSpA and 16 being nonradiographic axSpA. The sensitivity, specificity, and positive and negative predictive values of SIS ratio of SPECT/CT for axSpA were 87.5%, 56.5%, 16.6%, and 97.8%, respectively. In receiver operating curve analysis, MRI better diagnosed axSpA than did SIS ratio of SPECT/CT. Although the diagnostic utility of SIS ratio of SPECT/CT was inferior to MRI, visual scoring of SPECT/CT affords high sensitivity and negative predictive value in axSpA. When MRI is inappropriate for certain patients, SIS ratio of SPECT/CT is an alternative tool for identifying axSpA in real practice. [ABSTRACT FROM AUTHOR]

Brain is an amazing organ that controls all activities of a human. Any abnormality in the shape of anatomical regions of the brain needs to be detected as early as possible to reduce the mortality rate. It is also beneficial for treatment planning and therapy. The most crucial task is to isolate abnormal areas from normal tissue regions. To identify abnormalities in the earlier stage, various medical imaging modalities were used by medical practitioners as part of the diagnosis. Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool used for analyzing the internal structures owing to its capability to provide images with high resolution and better contrast for soft tissues. This survey focuses on studies done in brain MRI. Manual segmentation of abnormal tissues is a time-consuming task, and the performance depends on the expert's efficiency. Hence automating tumor segmentation plays a vital role in medical imaging applications. This study aims to provide a comprehensive survey on recent works developed in brain tumor segmentation. In this paper, a systematic literature review is presented to the reader to understand three policies, namely classical scheme, machine learning strategy, and deep learning methodology meant for tumor segmentation. Our primary goal is to include classical methods like atlas-based strategy and statistical-based models employed for segmenting tumors from brain MRI. Few studies that utilized machine learning approaches for the segmentation and classification of brain structures are also discussed. After that, the study provides an overview of deep learning-based segmentation models for quantitative analysis of brain MRI. Deep learning plays a vital role in the automatic segmentation of brain tissues. Presently deep learning technique outshines traditional statistical methods and machine learning approaches. An effort is made to enclose the literature on patch-based and semantic-based tissue segmentation presented by researchers working in the discipline of medical imaging. The manuscript discusses the basic convolutional neural network architecture, Data Sets, and the existing deep learning techniques for tissue segmentation coupled with classification. This paper also attempts to summarize the current works in Convolutional Neural networks and Autoencoders that assist researchers in seeking future directions. Finally, this article is concluded with possible developments and open challenges in brain tumor segmentation. [ABSTRACT FROM AUTHOR]