scholarly journals Improvement of peripheral nerve visualization using a deep learning-based MR reconstruction algorithm

2021 ◽  
Author(s):  
Kelly C. Zochowski ◽  
Ek T. Tan ◽  
Erin C. Argentieri ◽  
Bin Lin ◽  
Alissa J. Burge ◽  
...  
Keyword(s):  
Deep Learning ◽  
Peripheral Nerve ◽  
Reconstruction Algorithm ◽  
Mr Reconstruction

scholarly journals Improvement of Peripheral Nerve Visualization Using a Deep Learning-based MR Reconstruction Algorithm

2021 ◽  
Author(s):  
Kelly C. Zochowski ◽  
Ek Tsoon Tan ◽  
Erin C. Argentieri ◽  
Bin Lin ◽  
Alissa J. Burge ◽  
...  
Keyword(s):  
Deep Learning ◽  
Peripheral Nerves ◽  
Image Interpretation ◽  
Reconstruction Algorithm ◽  
Standard Of Care ◽  
Interrater Agreement ◽  
Reconstruction Method ◽  
Reconstruction Methods ◽  
Bulk Motion ◽  
Mr Reconstruction

Abstract Objective: To assess a new deep learning-based MR reconstruction method, “DLRecon,” for clinical evaluation of peripheral nerves.Methods: Sixty peripheral nerves were prospectively evaluated in 29 patients (mean age: 49±16 years, 17 female) undergoing standard-of-care (SOC) MR neurography for clinically suspected neuropathy. SOC-MRIs and DLRecon-MRIs were obtained through conventional and DLRecon reconstruction methods, respectively. Two radiologists randomly evaluated blinded images for outer epineurium conspicuity, fascicular architecture visualization, pulsation artifact, ghosting artifact, and bulk motion. Results: DLRecon-MRIs were likely to score better than SOC-MRIs for outer epineurium conspicuity (OR=1.9, p=0.007) and visualization of fasicular architecture (OR=1.8, p<0.001) and were likely to score worse for ghosting (OR=2.8, p=0.004) and pulsation artifacts (OR=1.6, p=0.004). There was substantial to almost-perfect inter-reconstruction method agreement (AC=0.73-1.00) and fair to almost-perfect interrater agreement (AC=0.34-0.86) for all features evaluated. DLRecon-MRI had improved interrater agreement for outer epineurium conspicuity (AC=0.71, substantial agreement) compared to SOC-MRIs (AC=0.34, fair agreement). In >80% of images, the radiologist correctly identified an image as SOC- or DLRecon-MRI.Discussion: Outer epineurium and fasicular architecture conspicuity, two key morphological features critical to evaluating a nerve injury, were improved in DLRecon-MRIs compared to SOC-MRIs. Although pulsation and ghosting artifacts increased in DLRecon images, image interpretation was unaffected.

Structural light 3D reconstruction algorithm based on deep learning

2019 ◽  
Author(s):  
Haibin Niu ◽  
Limin Hu ◽  
Shi Yan ◽  
Lei Ning ◽  
Yang Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
3D Reconstruction ◽  
Reconstruction Algorithm

scholarly journals Improvement of late gadolinium enhancement image quality using a deep learning–based reconstruction algorithm and its influence on myocardial scar quantification

2020 ◽  
Author(s):  
Nikki van der Velde ◽  
H. Carlijne Hassing ◽  
Brendan J. Bakker ◽  
Piotr A. Wielopolski ◽  
R. Marc Lebel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Late Gadolinium Enhancement ◽  
Image Quality ◽  
Noise Reduction ◽  
Reconstruction Algorithm ◽  
Remote Myocardium ◽  
Full Width ◽  
Half Maximum ◽  
Gadolinium Enhancement ◽  
Scar Size

Abstract Objectives The aim of this study was to assess the effect of a deep learning (DL)–based reconstruction algorithm on late gadolinium enhancement (LGE) image quality and to evaluate its influence on scar quantification. Methods Sixty patients (46 ± 17 years, 50% male) with suspected or known cardiomyopathy underwent CMR. Short-axis LGE images were reconstructed using the conventional reconstruction and a DL network (DLRecon) with tunable noise reduction (NR) levels from 0 to 100%. Image quality of standard LGE images and DLRecon images with 75% NR was scored using a 5-point scale (poor to excellent). In 30 patients with LGE, scar size was quantified using thresholding techniques with different standard deviations (SD) above remote myocardium, and using full width at half maximum (FWHM) technique in images with varying NR levels. Results DLRecon images were of higher quality than standard LGE images (subjective quality score 3.3 ± 0.5 vs. 3.6 ± 0.7, p < 0.001). Scar size increased with increasing NR levels using the SD methods. With 100% NR level, scar size increased 36%, 87%, and 138% using 2SD, 4SD, and 6SD quantification method, respectively, compared to standard LGE images (all p values < 0.001). However, with the FWHM method, no differences in scar size were found (p = 0.06). Conclusions LGE image quality improved significantly using a DL-based reconstruction algorithm. However, this algorithm has an important impact on scar quantification depending on which quantification technique is used. The FWHM method is preferred because of its independency of NR. Clinicians should be aware of this impact on scar quantification, as DL-based reconstruction algorithms are being used. Key Points • The image quality based on (subjective) visual assessment and image sharpness of late gadolinium enhancement images improved significantly using a deep learning–based reconstruction algorithm that aims to reconstruct high signal-to-noise images using a denoising technique. • Special care should be taken when scar size is quantified using thresholding techniques with different standard deviations above remote myocardium because of the large impact of these advanced image enhancement algorithms. • The full width at half maximum method is recommended to quantify scar size when deep learning algorithms based on noise reduction are used, as this method is the least sensitive to the level of noise and showed the best agreement with visual late gadolinium enhancement assessment.

scholarly journals PERFORMANCE OF A NOVEL DEEP LEARNING-BASED MRI RECONSTRUCTION ALGORITHM TO IMPROVE MYOCARDIAL PERFUSION IMAGES

2021 ◽  
Vol 77 (18) ◽  
pp. 1305
Author(s):  
Hena Patel ◽  
Shuo Wang ◽  
Haonan Wang ◽  
Martin Janich ◽  
Donovan Gorre ◽  
...  
Keyword(s):  
Deep Learning ◽  
Myocardial Perfusion ◽  
Reconstruction Algorithm ◽  
Mri Reconstruction

scholarly journals Use of Deep Learning to Improve the Computational Complexity of Reconstruction Algorithms in High Energy Physics

2021 ◽  
Vol 11 (23) ◽  
pp. 11467
Author(s):  
Núria Valls Canudas ◽  
Míriam Calvo Gómez ◽  
Elisabet Golobardes Ribé ◽  
Xavier Vilasis-Cardona
Keyword(s):  
Deep Learning ◽  
Particle Physics ◽  
High Energy Physics ◽  
Reconstruction Algorithm ◽  
High Energy ◽  
Electromagnetic Calorimeter ◽  
Training Dataset ◽  
Reconstruction Algorithms ◽  
Reconstruction Process ◽  
Small Parts

The optimization of reconstruction algorithms has become a key aspect in the field of experimental particle physics. Since technology has allowed gradually increasing the complexity of the measurements, the amount of data taken that needs to be interpreted has grown as well. This is the case with the LHCb experiment at CERN, where a major upgrade currently undergoing will considerably increase the data processing rate. This has presented the need to search for specific reconstruction techniques that aim to accelerate one of the most time consuming reconstruction algorithms in LHCb, the electromagnetic calorimeter clustering. Together with the use of deep learning techniques and the understanding of the current reconstruction algorithm, we propose a method that decomposes the reconstruction process into small parts that can be formulated as a cellular automaton. This approach is shown to benefit the generalized learning of small convolutional neural network architectures and also simplify the training dataset. Final results applied to a complete LHCb simulation reconstruction are compatible in terms of efficiency, and execute in nearly constant time with independence on the complexity of the data.

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