scholarly journals MRI lesion volume measurement in multiple sclerosis and its correlation with disability: a comparison of fast fluid attenuated inversion recovery (fFLAIR) and spin echo sequences

1998 ◽  
Vol 64 (2) ◽  
pp. 197-203 ◽  
Author(s):  
M L Gawne-Cain ◽  
J I O'Riordan ◽  
A Coles ◽  
B Newell ◽  
A J Thompson ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Spin Echo ◽  
Volume Measurement ◽  
Inversion Recovery ◽  
Lesion Volume ◽  
Fluid Attenuated Inversion Recovery ◽  
Spin Echo Sequences

MRI in multiple sclerosis of the spinal cord: evaluation of fast short-tan inversion-recovery and spin-echo sequences

2000 ◽  
Vol 42 (11) ◽  
pp. 810-813 ◽  
Author(s):  
J. L. Dietemann ◽  
A. Thibaut-Menard ◽  
J. M. Warter ◽  
C. Neugroschl ◽  
C. Tranchant ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Spinal Cord ◽  
Spin Echo ◽  
Inversion Recovery ◽  
Spin Echo Sequences

Multi-Contrast, Isotropic, Single-Slab 3D MR Imaging in Multiple Sclerosis

2009 ◽  
Vol 22 (1_suppl) ◽  
pp. 33-42
Author(s):  
Bastiaan Moraal ◽  
Stefan D. Roosendaal ◽  
Petra J. W. Pouwels ◽  
Hugo Vrenken ◽  
Ronald A. van Schijndel ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Spin Echo ◽  
Brain Lesions ◽  
Lesion Detection ◽  
Inversion Recovery ◽  
Rapid Acquisition ◽  
Fluid Attenuated Inversion Recovery ◽  
Student’S T ◽  
3D Sequences ◽  
3D Flair

To describe signal and contrast properties of an isotropic, single-slab 3D dataset [double inversion-recovery (DIR), fluid-attenuated inversion recovery (FLAIR), T2, and T1-weighted magnetization prepared rapid acquisition gradient-echo (MPRAGE)] and to evaluate its performance in detecting multiple sclerosis (MS) brain lesions compared to 2D T2-weighted spin-echo (T2SE). All single-slab 3D sequences and 2D-T2SE were acquired in 16 MS patients and 9 age-matched healthy controls. Lesions were scored independently by two raters and characterized anatomically. Two-tailed Bonferroni-corrected Student's t-tests were used to detect differences in lesion detection between the various sequences per anatomical area after log-transformation. In general, signal and contrast properties of the 3D sequences enabled improved detection of MS brain lesions compared to 2D-T2SE. Specifically, 3D-DIR showed the highest detection of intracortical and mixed WM-GM lesions, whereas 3D-FLAIR showed the highest total number of WM lesions. Both 3D-DIR and 3D-FLAIR showed the highest number of infratentorial lesions. 3D-T2 and 3D-MPRAGE did not improve lesion detection compared to 2D-T2SE. Multi-contrast, isotropic, single-slab 3D MRI allowed an improved detection of both GM and WM lesions compared to 2D-T2SE. A selection of single-slab 3D contrasts, for example, 3D-FLAIR and 3D-DIR, could replace 2D sequences in the radiological practice.

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 Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis

2020 ◽  
pp. 135245852092136 ◽  
Author(s):  
Ivan Coronado ◽  
Refaat E Gabr ◽  
Ponnada A Narayana
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Network Performance ◽  
Three Dimensional ◽  
Proton Density ◽  
Dice Similarity Coefficient ◽  
Lesion Volume ◽  
Imaging Data ◽  
Post Contrast ◽  
Fluid Attenuated Inversion Recovery

Objective: The aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients. Methods: A three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing–remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and lesion-wise true-positive (TPR) and false-positive (FPR) rates. Performance was also evaluated as a function of enhancing lesion volume. Results: The DSC/TPR/FPR values averaged over all the enhancing lesion sizes were 0.77/0.90/0.23 using the U5 model. These values for the largest enhancement volumes (>500 mm3) were 0.81/0.97/0.04. For U2, the average DSC/TPR/FPR values were 0.72/0.86/0.31. Comparable performance was observed with U1. For all types of input, the network performance degraded with decreased enhancement size. Conclusion: Excellent segmentation of enhancing lesions was observed for enhancement volume ⩾70 mm3. The best performance was achieved when the input included all five multispectral image sets.

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