scholarly journals Domain Adaptation for Deviating Acquisition Protocols in CNN-Based Lesion Classification on Diffusion-Weighted MR Images

2018 ◽  
pp. 73-80
Author(s):  
Jennifer Kamphenkel ◽  
Paul F. Jäger ◽  
Sebastian Bickelhaupt ◽  
Frederik Bernd Laun ◽  
Wolfgang Lederer ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Mr Images ◽  
Diffusion Weighted ◽  
Lesion Classification

The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas

2021 ◽  
pp. 1-8
Author(s):  
Haimei Cao ◽  
Xiang Xiao ◽  
Jun Hua ◽  
Guanglong Huang ◽  
Wenle He ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Roc Curve ◽  
Diffusion Weighted Imaging ◽  
Low Grade ◽  
High Grade ◽  
Mr Images ◽  
Vascular Space ◽  
Diffusion Weighted ◽  
High Grade Gliomas ◽  
And Diffusion

Objectives: The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas. Methods: Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI. Relative arteriolar cerebral blood volume (rCBVa) and minimum apparent diffusion coefficient (mADC) were compared between low-grade and high-grade gliomas. Receiver operating characteristic (ROC) curve analysis was performed to compare the tumor grading efficiency of rCBVa, mADC, and the combination of the two parameters. Results: Two observers diagnosed accurate tumor grade in 40 of 51 (78.4%) patients in the first review and in 46 of 51 (90.2%) in the second review. Both rCBVa and mADC showed significant differences between low-grade and high-grade gliomas. ROC analysis gave a threshold value of 1.52 for rCBVa and 0.85 × 10−3 mm2/s for mADC to provide a sensitivity and specificity of 88.0 and 81.2% and 100.0 and 68.7%, respectively. The area under the ROC curve (AUC) was 0.87 and 0.85 for rCBVa and mADC, respectively. The combination of rCBVa and mADC values increased the AUC to 0.92. Conclusion: The combined application of iVASO and DWI may improve the diagnostic accuracy of glioma grading.

scholarly journals Mapping gradient nonlinearity and miscalibration using diffusion‐weighted MR images of a uniform isotropic phantom

2021 ◽  
Author(s):  
Alan Seth Barnett ◽  
M. Okan Irfanoglu ◽  
Bennett Landman ◽  
Baxter Rogers ◽  
Carlo Pierpaoli
Keyword(s):  
Mr Images ◽  
Diffusion Weighted ◽  
Gradient Nonlinearity

scholarly journals Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 898
Author(s):  
Marta Saiz-Vivó ◽  
Adrián Colomer ◽  
Carles Fonfría ◽  
Luis Martí-Bonmatí ◽  
Valery Naranjo
Keyword(s):  
High Performance ◽  
Computational Models ◽  
Domain Adaptation ◽  
Semantic Segmentation ◽  
Patient Specific ◽  
Mr Images ◽  
Training Samples ◽  
Volumetric Images ◽  
Acquisition Costs ◽  
Left And Right

Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.

scholarly journals Differentiating benign and malignant inflammatory breast lesions: Value of T2 weighted and diffusion weighted MR images

2018 ◽  
Vol 50 ◽  
pp. 38-44 ◽  
Author(s):  
Shotaro Kanao ◽  
Masako Kataoka ◽  
Mami Iima ◽  
Debra Masako Ikeda ◽  
Masakazu Toi ◽  
...  
Keyword(s):  
Breast Lesions ◽  
Mr Images ◽  
Diffusion Weighted ◽  
And Diffusion

Correction of motional artifacts in diffusion-weighted MR images using navigator echoes

1994 ◽  
Vol 12 (3) ◽  
pp. 455-460 ◽  
Author(s):  
R.J. Ordidge ◽  
J.A. Helpern ◽  
Z.X. Qing ◽  
R.A. Knight ◽  
V. Nagesh
Keyword(s):  
Mr Images ◽  
Diffusion Weighted

Imaging Tutorial: Differential Diagnosis of Bright Lesions on Diffusion-weighted MR Images

Radiographics ◽  
10.1148/rg.e7 ◽  
2003 ◽  
Vol 23 (1) ◽  
pp. e7-e7 ◽  
Author(s):  
Tadeusz W. Stadnik ◽  
Philippe Demaerel ◽  
Robert R Luypaert ◽  
Christo Chaskis ◽  
Katrijn L. Van Rompaey ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Mr Images ◽  
Diffusion Weighted ◽  
Bright Lesions

Intensity profile based measurement of prostate gold markers influence on 1.5 and 3T diffusion-weighted MR images

2011 ◽  
Vol 50 (6) ◽  
pp. 866-872 ◽  
Author(s):  
Susanne Rylander ◽  
Sara Thörnqvist ◽  
Søren Haack ◽  
Erik Morre Pedersen ◽  
Ludvig Paul Muren
Keyword(s):  
Intensity Profile ◽  
Mr Images ◽  
Diffusion Weighted
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