Abstract
Background and Purpose: There is a need for new non-invasive quick test that provides physicians accurate information for the early diagnosis of mitochondrial encephalomyopathies (ME). Advances in MR technology have provided new insight in the diagnosis of metabolic and other complex metabolic diseases in children, therefore, we sought to see if new technological MR tools, namely Diffusion-Weighted imaging (DWI) and proton MR spectroscopy (1H-MRS) helped in the early diagnosis of ME. Methods: From a population of 55 patients diagnosed with ME, 10 patients also underwent DW imaging and/or 1HMRS, at 1.5 T, by means of PRESS monovoxel short TE acquisition (TE: 35 ms) or 2D multivoxel long TE acquisition (TE:144 ms). Results: On DW imaging, patients with acute clinical status showed statistically more lesions than on their conventional counterparts. On 1HMRS, all patients with acute clinical status showed brain lactate, which was superior to its detection on serum/CSF. Conclusion: Neuroradiologic information provided for current MR advanced tools is becoming the first para-clinical test in the early tentative diagnosis of ME, in children with acute onset of encephalopatic status.
Keywords: Mitochondrial diseases, Magnetic Resonance Imaging and children diseases, brain, metabolic diseases, MR Spectroscopy, proton MR Spectroscopy, MR imaging and diffusion weighted studies
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
6