Current Issue : July - September Volume : 2016 Issue Number : 3 Articles : 4 Articles
Arterial spin labelling (ASL) is a noninvasive magnetic resonance imaging (MRI) modality, capable of measuring blood perfusion\nwithout the use of a contrast agent. While ASL implementation for imaging the brain and monitoring cerebral blood flow has\nbeen reviewed in depth, the technique is yet to be widely used for ocular tissue imaging. The human retina is a very thin but\nhighly stratified structure and it is also situated close to the surface of the body which is not ideal for MR imaging. Hence, the\napplication of MR imaging and ASL in particular has been very challenging for ocular tissues and retina. That is despite the fact\nthat almost all of retinal pathologies are accompanied by blood perfusion irregularities. In this review article, we have focused on\nthe technical aspects of the ASL and their implications for its optimum adaptation for retinal blood perfusion monitoring. Retinal\nblood perfusion has been assessed through qualitative or invasive quantitativemethods but the prospect of imaging flow using ASL\nwould increase monitoring and assessment of retinal pathologies.The review provides details of ASL application in human ocular\nblood flow assessment....
Accurate diagnosis of acute appendicitis is a difficult problem in practice especially when the patient is too young or women in\npregnancy. In this paper, we propose a fully automatic appendix extractor from ultrasonography by applying a series of image\nprocessing algorithms and an unsupervised neural learning algorithm, self-organizing map. From the suggestions of clinical\npractitioners, we define four shape patterns of appendix and self-organizing map learns those patterns in pixel clustering phase. In\nthe experiment designed to test the performance for those four frequently found shape patterns, our method is successful in 3 types\n(1 failure out of 45 cases) but leaves a question for one shape pattern (80% correct)....
Technological advances in magnetic resonance imaging (MRI) and computed tomography (CT), including higher spatial and\ntemporal resolution, have made the prospect of performing absolute myocardial perfusion quantification possible, previously\nonly achievable with positron emission tomography (PET). This could facilitate integration of myocardial perfusion biomarkers\ninto the current workup for coronary artery disease (CAD), as MRI and CT systems are more widely available than PET\nscanners. Cardiac PET scanning remains expensive and is restricted by the requirement of a nearby cyclotron. Clinical evidence\nis needed to demonstrate that MRI and CT have similar accuracy for myocardial perfusion quantification as PET. However, lack\nof standardization of acquisition protocols and tracer kinetic model selection complicates comparison between different studies\nand modalities. The aim of this overview is to provide insight into the different tracer kinetic models for quantitative myocardial\nperfusion analysis and to address typical implementation issues in MRI and CT. We compare different models based on their\ntheoretical derivations and present the respective consequences for MRI and CT acquisition parameters, highlighting the interplay\nbetween tracer kinetic modeling and acquisition settings....
Soft tissue images from portable cone beam computed tomography (CBCT) scanners can be used for diagnosis and detection of\ntumor, cancer, intracerebral hemorrhage, and so forth.Due to large field of view, X-ray scattering which is the main cause of artifacts\ndegrades image quality, such as cupping artifacts, CT number inaccuracy, and low contrast, especially on soft tissue images. In this\nwork, we propose the X-ray scatter correction method for improving soft tissue images. The X-ray scatter correction scheme to\nestimate X-ray scatter signals is based on the deconvolution technique using the maximum likelihood estimation maximization\n(MLEM) method. The scatter kernels are obtained by simulating the PMMA sheet on the Monte Carlo simulation (MCS) software.\nIn the experiment, we used the QRM phantom to quantitatively compare with fan-beam CT (FBCT) data in terms of CT number\nvalues, contrast to noise ratio, cupping artifacts, and low contrast detectability. Moreover, the PH3 angiography phantom was also\nused to mimic human soft tissues in the brain. The reconstructed images with our proposed scatter correction show significant\nimprovement on image quality.Thus the proposed scatter correction technique has high potential to detect soft tissues in the brain....
Loading....