scholarly journals ANALISIS PERBEDAAN CITRA MRI BRAIN PADA SEKUENT1SE DAN T1FLAIR

SINERGI ◽  
2015 ◽  
Vol 19 (3) ◽  
pp. 206
Author(s):  
Nursama Heru Apriantoro ◽  
Christianni Christianni
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Spin Echo ◽  
Spinal Fluid ◽  
Inversion Recovery ◽  
Field Of View ◽  
Slice Thickness ◽  
Weighted Image ◽  
Mri Brain ◽  
Fluid Attenuated Inversion Recovery

MRI adalah bagian dari ilmu kedokteran untuk mediagnosa kelainan organ dengan memanfaatkan medan magnet dan pergerakan proton atom hidrogen. Salah satu pemeriksaan MRI adalah pemeriksaan brain. Pemeriksaan MRI brain dapat dilakukan T1 weighted image Spin Echo (T1 SE) atau T1 Fluid Attenuated Inversion Recovery (T1 FLAIR). Kajian dilakukan untuk menentukan perbedaan T1 SE dan T1 FLAIR dari segi citra berdasarkan nilai Rasio Signal terhadap Noise (SNR) dengan MRI GE Type Signa HD xt 1.5 Tesla. Penelitian menggunakan pendekatan kuantitatif.  20 pasien  telah diambil pada pemeriksaan MRI brain pada potongan axial, dengan parameter T1 SE potongan axial dengan parameter Time Repetition (TR) 700 ms, Time Echo (TE) 20 ms, Field of View (FOV) 240 mm, Slice Thickness 5,0 mm, Spacing 1,0 mm, Number of Excitations (NEX) 1, Phase 224, dan total slice 20. T1 FLAIR  parameter TR 3000 ms, TE 13,9 ms, TI 920 ms, FOV 240 mm, slice thickness 5,0 mm, spacing 1,0 mm,   NEX 1, phase 224, dan total slice 20. SNR dihitung pada anatomi brain meliputi CSF (Cerebro Spinal Fluid), White Matter dan Gray Matter. Hasil penelitian kedua sequence tersebut menunjukkan bahwa sequence T1 SE lebih baik daripada sequence T1 FLAIR.

scholarly journals Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yi Zhong ◽  
David Utriainen ◽  
Ying Wang ◽  
Yan Kang ◽  
E. Mark Haacke
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Gray Matter ◽  
Signal Intensity ◽  
High Spatial Frequency ◽  
Inversion Recovery ◽  
White Matter Hyperintensity ◽  
Mr Images ◽  
Automated Algorithm ◽  
Fluid Attenuated Inversion Recovery

White matter hyperintensities (WMH) seen on T2WI are a hallmark of multiple sclerosis (MS) as it indicates inflammation associated with the disease. Automatic detection of the WMH can be valuable in diagnosing and monitoring of treatment effectiveness. T2 fluid attenuated inversion recovery (FLAIR) MR images provided good contrast between the lesions and other tissue; however the signal intensity of gray matter tissue was close to the lesions in FLAIR images that may cause more false positives in the segment result. We developed and evaluated a tool for automated WMH detection only using high resolution 3D T2 fluid attenuated inversion recovery (FLAIR) MR images. We use a high spatial frequency suppression method to reduce the gray matter area signal intensity. We evaluate our method in 26 MS patients and 26 age matched health controls. The data from the automated algorithm showed good agreement with that from the manual segmentation. The linear correlation between these two approaches in comparing WMH volumes was found to beY=1.04X+1.74  (R2=0.96). The automated algorithm estimates the number, volume, and category of WMH.

scholarly journals Juxtacortical susceptibility changes in progressive multifocal leukoencephalopathy at the gray–white matter junction correlates with iron-enriched macrophages

2021 ◽  
pp. 135245852199965
Author(s):  
Kedar R Mahajan ◽  
Moein Amin ◽  
Matthew Poturalski ◽  
Jonathan Lee ◽  
Danielle Herman ◽  
...  
Keyword(s):  
White Matter ◽  
Progressive Multifocal Leukoencephalopathy ◽  
Central Nervous System Lymphoma ◽  
Susceptibility Weighted Imaging ◽  
Resonance Imaging ◽  
Gradient Echo ◽  
Total N ◽  
Mri Brain ◽  
Fluid Attenuated Inversion Recovery ◽  
Magnetic Resonance Imaging Mri

Objective: Describe magnetic resonance imaging (MRI) susceptibility changes in progressive multifocal leukoencephalopathy (PML) and identify neuropathological correlates. Methods: PML cases and matched controls with primary central nervous system lymphoma (PCNSL) were retrospectively identified. MRI brain at 3 T and 7 T were reviewed. MRI-pathology correlations in fixed brain autopsy tissue were conducted in three subjects with confirmed PML. Results: With PML ( n = 26 total, n = 5 multiple sclerosis natalizumab-associated), juxtacortical changes on susceptibility-weighted imaging (SWI) or gradient echo (GRE) sequences were noted in 3/3 cases on 7 T MRI and 14/22 cases (63.6%) on 1.5 T or 8/22 (36.4%) 3 T MRI. Similar findings were only noted in 3/25 (12.0%) of PCNSL patients (odds ratio (OR) 12.83, 95% confidence interval (CI), 2.9–56.7, p < 0.001) on 1.5 or 3 T MRI. On susceptibility sequences available prior to diagnosis of PML, 7 (87.5%) had changes present on average 2.7 ± 1.8 months (mean ± SD) prior to diagnosis. Postmortem 7 T MRI showed SWI changes corresponded to areas of increased iron density along the gray–white matter (GM-WM) junction predominantly in macrophages. Conclusion: Susceptibility changes in PML along the GM-WM junction can precede noticeable fluid-attenuated inversion recovery (FLAIR) changes and correlates with iron accumulation in macrophages.

MR Brain Image Segmentation to Detect White Matter, Gray Matter, and Cerebro Spinal Fluid Using TLBO Algorithm

2021 ◽  
pp. 2150034
Author(s):  
Sandhya Gudise ◽  
Giri Babu Kande ◽  
T. Satya Savithri
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Population Based ◽  
Spinal Fluid ◽  
Brain Image ◽  
Tlbo Algorithm ◽  
Wide Range ◽  
Skull Stripping ◽  
The Brain ◽  
Mr Brain

This paper proposes an advanced and precise technique for the segmentation of Magnetic Resonance Image (MRI) of the brain. Brain MRI segmentation is to be familiar with the anatomical structure, to recognize the deformities, and to distinguish different tissues which help in treatment planning and diagnosis. Nature’s inspired population-based evolutionary algorithms are extremely popular for a wide range of applications due to their best solutions. Teaching Learning Based Optimization (TLBO) is an advanced population-based evolutionary algorithm designed based on Teaching and Learning process of a classroom. TLBO uses common controlling parameters and it won’t require algorithm-specific parameters. TLBO is more appropriate to optimize the real variables which are fuzzy valued, computationally efficient, and does not require parameter tuning. In this work, the pixels of the brain image are automatically grouped into three distinct homogeneous tissues such as White Matter (WM), Gray Matter (GM), and Cerebro Spinal Fluid (CSF) using the TLBO algorithm. The methodology includes skull stripping and filtering in the pre-processing stage. The outcomes for 10 MR brain images acquired by utilizing the proposed strategy proved that the three brain tissues are segmented accurately. The segmentation outputs are compared with the ground truth images and high values are obtained for the measure’s sensitivity, specificity, and segmentation accuracy. Four different approaches, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Bacterial Foraging Algorithm (BFA), and Electromagnetic Optimization (EMO) are likewise implemented to compare with the results of the proposed methodology. From the results, it can be proved that the proposed method performed effectively than the other.

Segmenting brain white matter, gray matter and cerebro-spinal fluid using diffusion tensor-MRI derived indices

2001 ◽  
Vol 19 (9) ◽  
pp. 1167-1172 ◽  
Author(s):  
Mara Cercignani ◽  
Matilde Inglese ◽  
Malgorzata Siger-Zajdel ◽  
Massimo Filippi
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Diffusion Tensor ◽  
Spinal Fluid ◽  
Diffusion Tensor Mri ◽  
Brain White Matter

A case of Marchiafava-Bignami disease: MRI findings on spin-echo and fluid attenuated inversion recovery (FLAIR) images

2000 ◽  
Vol 34 (2) ◽  
pp. 141-143 ◽  
Author(s):  
Takashi Yamamoto ◽  
Ryuichiro Ashikaga ◽  
Yutaka Araki ◽  
Yasumasa Nishimura
Keyword(s):  
Spin Echo ◽  
Inversion Recovery ◽  
Mri Findings ◽  
Fluid Attenuated Inversion Recovery
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