scholarly journals Robust partial Fourier reconstruction for diffusion‐weighted imaging using a recurrent convolutional neural network

2021 ◽  
Author(s):  
Fasil Gadjimuradov ◽  
Thomas Benkert ◽  
Marcel Dominik Nickel ◽  
Andreas Maier
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Diffusion Weighted Imaging ◽  
Diffusion Weighted ◽  
Fourier Reconstruction

scholarly journals Automatic Assessment of ASPECTS Using Diffusion-Weighted Imaging in Acute Ischemic Stroke Using Recurrent Residual Convolutional Neural Network

Diagnostics ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 803
Author(s):  
Luu-Ngoc Do ◽  
Byung Hyun Baek ◽  
Seul Kee Kim ◽  
Hyung-Jeong Yang ◽  
Ilwoo Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Diffusion Weighted Imaging ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Automatic Assessment ◽  
Anterior Circulation ◽  
Deep Learning Algorithm ◽  
Diffusion Weighted

The early detection and rapid quantification of acute ischemic lesions play pivotal roles in stroke management. We developed a deep learning algorithm for the automatic binary classification of the Alberta Stroke Program Early Computed Tomographic Score (ASPECTS) using diffusion-weighted imaging (DWI) in acute stroke patients. Three hundred and ninety DWI datasets with acute anterior circulation stroke were included. A classifier algorithm utilizing a recurrent residual convolutional neural network (RRCNN) was developed for classification between low (1–6) and high (7–10) DWI-ASPECTS groups. The model performance was compared with a pre-trained VGG16, Inception V3, and a 3D convolutional neural network (3DCNN). The proposed RRCNN model demonstrated higher performance than the pre-trained models and 3DCNN with an accuracy of 87.3%, AUC of 0.941, and F1-score of 0.888 for classification between the low and high DWI-ASPECTS groups. These results suggest that the deep learning algorithm developed in this study can provide a rapid assessment of DWI-ASPECTS and may serve as an ancillary tool that can assist physicians in making urgent clinical decisions.

scholarly journals Diffusion-Weighted Imaging-Magnetic Resonance Imaging Information under Class-Structured Deep Convolutional Neural Network Algorithm in the Prognostic Chemotherapy of Osteosarcoma

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yong Hu ◽  
Jie Tang ◽  
Shenghao Zhao ◽  
Ye Li
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Neoadjuvant Chemotherapy ◽  
Convolutional Neural Network ◽  
Diffusion Weighted Imaging ◽  
Resonance Imaging ◽  
Imaging Diagnosis ◽  
The Difference ◽  
Mri Image

In order to improve the efficiency of early imaging diagnosis of patients with osteosarcoma and the effect of neoadjuvant chemotherapy based on the results of imaging examinations, 48 patients with suspected osteosarcoma were selected as the research objects and their diffusion-weighted imaging (DWI)-magnetic resonance imaging (MRI) images were regularized in this study. Then, a DWI-MRI image discrimination model was established based on the class-structured deep convolutional neural network (CSDCNN) algorithm. The peak signal-to-noise ratio (PSNR), mean square error (MSE), and edge preserve index (EPI) were applied to evaluate the image quality after processing by the CSDCNN algorithm; the accuracy, recall rate, precise rate, and F1 score were employed to evaluate the diagnostic efficiency of CSDCNN algorithm; the apparent diffusion coefficient (ADC) was adopted to evaluate the therapeutic effect of neoadjuvant chemotherapy based on the CSDCNN algorithm, and SegNet, LeNet, and AlexNet algorithms were introduced for comparison. The results showed that the PSNR, MSE, and EPI values of DWI-MRI images of patients with osteosarcoma were 29.1941, 0.0016, and 0.9688, respectively, after using the CSDCNN algorithm to process the DWI-MRI images. The three indicators were significantly better than other algorithms, and the difference was statistically significant ( P < 0.05 ). According to the results of imaging diagnosis of patients with osteosarcoma, there was no significant difference between the assisted diagnosis effect of the CSDCNN algorithm and the pathological examination results ( P > 0.05 ). The results of adjuvant chemotherapy based on the CSDCNN algorithm found that the ADCmean value of the patients after chemotherapy was 1.66 ± 0.17 and the ADCmin value was 1.33 ± 0.15; the two indicators were significantly higher than other algorithms, and the difference was statistically significant ( P < 0.05 ). In conclusion, the CSDCNN algorithm had a good effect on DWI-MRI image processing of patients with osteosarcoma, which could improve the diagnostic accuracy of patients with osteosarcoma. Moreover, the diagnosis results based on this algorithm could achieve better neoadjuvant chemotherapy effects and assist clinicians in imaging diagnosis and clinical treatment of patients with osteosarcoma.

OC-0344: Automatic contouring of diffusion-weighted MRI from an MR-Linac using a convolutional neural network

2020 ◽  
Vol 152 ◽  
pp. S183
Author(s):  
O. Gurney-Champion ◽  
J. Kieselmann ◽  
W. Kee ◽  
B. Ng-Cheng-Hin ◽  
K. Newbold ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted ◽  
Automatic Contouring

scholarly journals Accelerating Prostate Diffusion-weighted MRI Using a Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study

2020 ◽  
Vol 2 (5) ◽  
pp. e200007
Author(s):  
Elena A. Kaye ◽  
Emily A. Aherne ◽  
Cihan Duzgol ◽  
Ida Häggström ◽  
Erich Kobler ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feasibility Study ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted

MRT des Prostatakarzinoms: Diffusion Weighted Imaging im Vergleich mit der Histologie

2009 ◽  
Vol 181 (S 01) ◽  
Author(s):  
J Yamamura ◽  
G Salomon ◽  
J Graessner ◽  
A Hohenstein ◽  
M Graefen ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Diffusion Weighted

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network
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