Deep regression neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion in acute ischemic stroke

2019 ◽  
Vol 15 (1) ◽  
pp. 151-162 ◽  
Author(s):  
Minh Nguyen Nhat To ◽  
Hyun Jeong Kim ◽  
Hong Gee Roh ◽  
Yoon-Sik Cho ◽  
Jin Tae Kwak
Keyword(s):  
Neural Networks ◽  
Ischemic Stroke ◽  
Magnetic Resonance ◽  
Acute Ischemic Stroke ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Magnetic Resonance Perfusion ◽  
Contrast Enhanced ◽  
Susceptibility Contrast

scholarly journals Deep Learning Detection of Penumbral Tissue on Arterial Spin Labeling in Stroke

Stroke ◽  
2020 ◽  
Vol 51 (2) ◽  
pp. 489-497 ◽  
Author(s):  
Kai Wang ◽  
Qinyang Shou ◽  
Samantha J. Ma ◽  
David Liebeskind ◽  
Xin J. Qiao ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Ischemic Stroke ◽  
Magnetic Resonance ◽  
Endovascular Treatment ◽  
Acute Ischemic Stroke ◽  
Dynamic Susceptibility ◽  
Resonance Imaging ◽  
Dynamic Susceptibility Contrast ◽  
Contrast Magnetic Resonance ◽  
Susceptibility Contrast

Background and Purpose— Selection of patients with acute ischemic stroke for endovascular treatment generally relies on dynamic susceptibility contrast magnetic resonance imaging or computed tomography perfusion. Dynamic susceptibility contrast magnetic resonance imaging requires injection of contrast, whereas computed tomography perfusion requires high doses of ionizing radiation. The purpose of this work was to develop and evaluate a deep learning (DL)–based algorithm for assisting the selection of suitable patients with acute ischemic stroke for endovascular treatment based on 3-dimensional pseudo-continuous arterial spin labeling (pCASL). Methods— A total of 167 image sets of 3-dimensional pCASL data from 137 patients with acute ischemic stroke scanned on 1.5T and 3.0T Siemens MR systems were included for neural network training. The concurrently acquired dynamic susceptibility contrast magnetic resonance imaging was used to produce labels of hypoperfused brain regions, analyzed using commercial software. The DL and 6 machine learning (ML) algorithms were trained with 10-fold cross-validation. The eligibility for endovascular treatment was determined retrospectively based on the criteria of perfusion/diffusion mismatch in the DEFUSE 3 trial (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke). The trained DL algorithm was further applied on twelve 3-dimensional pCASL data sets acquired on 1.5T and 3T General Electric MR systems, without fine-tuning of parameters. Results— The DL algorithm can predict the dynamic susceptibility contrast–defined hypoperfusion region in pCASL with a voxel-wise area under the curve of 0.958, while the 6 ML algorithms ranged from 0.897 to 0.933. For retrospective determination for subject-level endovascular treatment eligibility, the DL algorithm achieved an accuracy of 92%, with a sensitivity of 0.89 and specificity of 0.95. When applied to the GE pCASL data, the DL algorithm achieved a voxel-wise area under the curve of 0.94 and a subject-level accuracy of 92% for endovascular treatment eligibility. Conclusions— pCASL perfusion magnetic resonance imaging in conjunction with the DL algorithm provides a promising approach for assisting decision-making for endovascular treatment in patients with acute ischemic stroke.

scholarly journals Diagnostic Value of Semiquantitative Analysis of Dynamic Susceptibility Contrast Magnetic Resonance Imaging with GD-EOB-DTPA in Focal Liver Lesions Characterization: A Feasibility Study

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Davide Ippolito ◽  
Maddalena Colombo ◽  
Chiara Trattenero ◽  
Pietro Andrea Bonaffini ◽  
Cammillo Talei Franzesi ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Focal Liver Lesions ◽  
Dynamic Susceptibility ◽  
Liver Lesions ◽  
Resonance Imaging ◽  
Dynamic Susceptibility Contrast ◽  
Contrast Enhanced ◽  
Malignant Lesions ◽  
Susceptibility Contrast

Purpose.To assess the diagnostic accuracy of dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSCE-MRI) in differentiation between benign and malignant liver lesions by assessment of tumoral perfusion parameters.Methods Materials.Seventy-three patients with known focal liver lesions, including 45 benign (16 FNH, 27 angiomas, and 2 abscesses) and 28 malignant ones (17 metastases, 9 HCCs, and 2 cholangiocarcinoma) underwent 1.5 T MRI upper abdominal study, with standard protocol that included dynamic contrast-enhanced sequences. On dedicated workstation, time-intensity curves were determined and the following perfusion parameters were calculated: relative arterial, venous and late enhancement (RAE, RVE, RLE), maximum enhancement (ME), relative enhancement (RE), and time to peak (TTP).Results.All diagnoses were established either by histopathology or imaging follow-up. Perfusion mean values calculated in benign lesions were RAE 33.8%, RVE 66.03%, RLE 80.63%, ME 776.00%, MRE 86.27%, and TTP 146.95 sec. Corresponding perfusion values calculated in malignant lesions were RAE 22.47%, RVE 40.54%, RLE 47.52%, ME 448.78%, MRE 49.85%, and TTP 183.79 sec. Statistical difference (p<0.05) was achieved in all the perfusion parameters calculated, obtaining different cluster of perfusion kinetics between benign and malignant lesions.Conclusions.DSCE-MRI depicts kinetic differences in perfusion parameters among the different common liver lesions, related to tumour supply and microvascular characteristics.

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