Department of Biochemistry, University of Cambridge, Old Addenbrooke's Site, Cambridge CB2 1GA, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.
Published: March 2019
AI Article Synopsis
Article Abstract
Due to the implication of altered metabolism in a large spectrum of tissue function and disease, assessment of metabolic processes becomes essential in managing health. In this regard, imaging can play a critical role in allowing observation of biochemical and physiological processes. Nuclear imaging methods, in particular positron emission tomography, have been widely employed for imaging metabolism but are mainly limited by the use of ionizing radiation and the sensing of only one parameter at each scanning session. Observations in healthy individuals or longitudinal studies of disease could markedly benefit from non-ionizing, multi-parameter imaging methods. We therefore focus this review on progress with the non-ionizing radiation methods of MRI, hyperpolarized magnetic resonance and magnetic resonance spectroscopy, chemical exchange saturation transfer, and emerging optoacoustic (photoacoustic) imaging. We also briefly discuss the role of nuclear and optical imaging methods for research and clinical protocols.
Diffusion-weighted imaging (DWI) has been frequently used to examine age-related deterioration of white matter microstructure and its relationship to cognitive decline. However, typical tensor-based analytical approaches are often difficult to interpret due to the challenge of decomposing and (mis)interpreting the impact of crossing fibers within a voxel. We hypothesized that a novel analytical approach capable of resolving fiber-specific changes within each voxel (i.
Background And Purpose: Segmentation imperfections (noise) in radiotherapy organ-at-risk segmentation naturally arise from specialist experience and image quality. Using clinical contours can result in sub-optimal convolutional neural network (CNN) training and performance, but manual curation is costly. We address the impact of simulated and clinical segmentation noise on CNN parotid gland (PG) segmentation performance and provide proof-of-concept for an easily implemented auto-curation countermeasure.
Despite the benefits of minimally invasive surgery, interventions such as laparoscopic liver surgery present unique challenges, like the significant anatomical differences between preoperative images and intraoperative scenes due to pneumoperitoneum, patient pose, and organ manipulation by surgical instruments. To address these challenges, a method for intraoperative three-dimensional reconstruction of the surgical scene, including vessels and tumors, without altering the surgical workflow, is proposed. The technique combines neural radiance field reconstructions from tracked laparoscopic videos with ultrasound three-dimensional compounding.
Laparoscopic video tracking primarily focuses on two target types: surgical instruments and anatomy. The former could be used for skill assessment, while the latter is necessary for the projection of virtual overlays. Where instrument and anatomy tracking have often been considered two separate problems, in this article, a method is proposed for joint tracking of all structures simultaneously.
Background: Variants in have been reported to be associated with Leigh syndrome. However, further expansion of the -phenotype and variants spectrum of -related Leigh syndrome are still required.
Methods: Two patients diagnosed with Leigh syndrome were recruited, and whole-exome sequencing was performed to identify the genetic variants responsible for the abnormal gait, dystonia, and bilateral basal ganglia lesions, followed by validation using Sanger sequencing.
