Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:2.7.11.8 (FAST)
 758 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The 53-year-old woman was initially diagnosed with multiple sclerosis, despite the fact that she did not really meet the clinical criteria. Her only symptoms were clumsiness and weakness of the right extremities. Being a veterinary research worker she had been exposed to infectious material. In 1995, she was diagnosed with ELISA as having toxoplasmosis and treated as such. In 2002, after the infectious, flu-like disease, she revealed arthritis and drowsiness, also with memory and language impairment. The patient continued to have symptoms consistent with previously examined clumsiness. She was diagnosed with Lyme via ELISA and PCR, and treated. She made a full recovery from acute symptoms. After a few months, neurological and neuropsychological examinations were performed. On the background of mild cognitive decline apraxia and difficulties of attention were noted as the main problems. A apraxia of the right hand complicated the patient's life and depreciated her quality of life. The patient underwent MRI examination. FSE, FAST and FLAIR sequences were made. The MRI demonstrated the appearance of several small hyperintense lesions in the white matter of the left and right frontal and left parietal lobe. These lesions were typical of the post-inflammatory leucoencephalopathy. Additionally, a ring-shaped, low-intensity lesion in the posterior part of the left parietal lobe was noticed. The lesion was 8 mm in diameter and described to be an old toxoplasmosis lesion. The patient had been treated and the symptoms consistent with Lyme disease resolved. Patient continues to have symptoms consistent with focal destruction of the parietal lobe. Over the past six months, she has not progressed and relapsed in a manner that is consistent with MS.
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PMID:Co-existance of toxoplasmosis and neuroborreliosis - a case report. 1645 90

Multiple sclerosis white matter (WM) lesions can affect brain tissue volume measurements of voxel-wise segmentation methods if these lesions are included in the segmentation process. Several authors have presented different techniques to improve brain tissue volume estimations by filling WM lesions before segmentation with intensities similar to those of WM. Here, we propose a new method to refill WM lesions, where contrary to similar approaches, lesion voxel intensities are replaced by random values of a normal distribution generated from the mean WM signal intensity of each two-dimensional slice. We test the performance of our method by estimating the deviation in tissue volume between a set of 30 T1-w 1.5 T and 30 T1-w 3 T images of healthy subjects and the same images where: WM lesions have been previously registered and afterwards replaced their voxel intensities to those between gray matter (GM) and WM tissue. Tissue volume is computed independently using FAST and SPM8. When compared with the state-of-the-art methods, on 1.5 T data our method yields the lowest deviation in WM between original and filled images, independently of the segmentation method used. It also performs the lowest differences in GM when FAST is used and equals to the best method when SPM8 is employed. On 3 T data, our method also outperforms the state-of-the-art methods when FAST is used while performs similar to the best method when SPM8 is used. The proposed technique is currently available to researchers as a stand-alone program and as an SPM extension.
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PMID:A white matter lesion-filling approach to improve brain tissue volume measurements. 2537 19

Automatic segmentation of multiple sclerosis (MS) lesions in brain magnetic resonance imaging (MRI) has been widely investigated in the recent years with the goal of helping MS diagnosis and patient follow-up. In this research work, Gaussian mixture model (GMM) has been used to segment the MS lesions in MRIs, including T1-weighted (T1-w), T2-w, and T2-fluid attenuation inversion recovery. Usually, GMM is optimized by using expectation-maximization (EM) algorithm. The drawbacks of this optimization method are, it does not converge to optimal maximum or minimum and furthermore, there are some voxels, which do not fit the GMM model and have to be rejected. So, GMM is time-consuming and not too much efficient. To overcome these limitations, in this research study, at the first step, GMM was applied to segment only T1-w images by using 100 various starting points when the maximum number of iterations was considered to be 50. Then segmentation results were used to calculate the parameters of the other two images. Furthermore, FAST-trimmed likelihood estimator algorithm was applied to determine which voxels should be rejected. The output result of the segmentation was classified in three classes; White and Gray matters, cerebrospinal fluid, and some rejected voxels which prone to be MS. In the next phase, MS lesions were detected by using some heuristic rules. This new method was applied on the brain MRIs of 25 patients from two hospitals. The automatic segmentation outputs were scored by two specialists and the results show that our method has the capability to segment the MS lesions with dice similarity coefficient score of 0.82. The results showed a better performance for the proposed approach, in comparison to those of previous works with less time-consuming.
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PMID:A New Method to Segment the Multiple Sclerosis Lesions on Brain Magnetic Resonance Images. 2695 67

Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community.
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PMID:Automated tissue segmentation of MR brain images in the presence of white matter lesions. 2759 4