Mr. Roshan Reddy Upendra Profile

Roshan Reddy Upendra

at Rochester Institute of Technology

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Publications (3)

SPIE Journal Paper | 24 May 2023

Deep learning architecture for 3D image super-resolution of late gadolinium enhanced cardiac MRI

Roshan Reddy Upendra, Richard Simon, Cristian A. Linte

JMI, Vol. 10, Issue 05, 051808, (May 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.5.051808

KEYWORDS: Image segmentation, Super resolution, 3D modeling, Education and training, 3D image processing, Data modeling, 3D image enhancement, Interpolation, Image resolution, Deep learning

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PurposeHigh-resolution late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI) volumes are difficult to acquire due to the limitations of the maximal breath-hold time achievable by the patient. This results in anisotropic 3D volumes of the heart with high in-plane resolution, but low-through-plane resolution. Thus, we propose a 3D convolutional neural network (CNN) approach to improve the through-plane resolution of the cardiac LGE-MRI volumes.ApproachWe present a 3D CNN-based framework with two branches: a super-resolution branch to learn the mapping between low-resolution and high-resolution LGE-MRI volumes, and a gradient branch that learns the mapping between the gradient map of low-resolution LGE-MRI volumes and the gradient map of high-resolution LGE-MRI volumes. The gradient branch provides structural guidance to the CNN-based super-resolution framework. To assess the performance of the proposed CNN-based framework, we train two CNN models with and without gradient guidance, namely, dense deep back-projection network (DBPN) and enhanced deep super-resolution network. We train and evaluate our method on the 2018 atrial segmentation challenge dataset. Additionally, we also evaluate these trained models on the left atrial and scar quantification and segmentation challenge 2022 dataset to assess their generalization ability. Finally, we investigate the effect of the proposed CNN-based super-resolution framework on the 3D segmentation of the left atrium (LA) from these cardiac LGE-MRI image volumes.ResultsExperimental results demonstrate that our proposed CNN method with gradient guidance consistently outperforms bicubic interpolation and the CNN models without gradient guidance. Furthermore, the segmentation results, evaluated using Dice score, obtained using the super-resolved images generated by our proposed method are superior to the segmentation results obtained using the images generated by bicubic interpolation (p < 0.01) and the CNN models without gradient guidance (p < 0.05).ConclusionThe presented CNN-based super-resolution method with gradient guidance improves the through-plane resolution of the LGE-MRI volumes and the structure guidance provided by the gradient branch can be useful to aid the 3D segmentation of cardiac chambers, such as LA, from the 3D LGE-MRI images.

Proceedings Article | 15 February 2021 Presentation + Paper

Joint deep learning framework for image registration and segmentation of late gadolinium enhanced MRI and cine cardiac MRI

Roshan Reddy Upendra, Richard Simon, Cristian Linte

Proceedings Volume 11598, 115980F (2021) https://doi.org/10.1117/12.2581386

KEYWORDS: Image segmentation, Cardiovascular magnetic resonance imaging, Image registration, Magnetic resonance imaging, Gadolinium, Image enhancement, Data modeling, Tissues, Performance modeling, Network architectures

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Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) imaging, the current benchmark for assessment of myocardium viability, enables the identification and quantification of the compromised myocardial tissue regions, as they appear hyper-enhanced compared to the surrounding, healthy myocardium. However, in LGE CMR images, the reduced contrast between the left ventricle (LV) myocardium and LV blood-pool hampers the accurate delineation of the LV myocardium. On the other hand, the balanced-Steady State Free Precession (bSSFP) cine CMR imaging provides high resolution images ideal for accurate segmentation of the cardiac chambers. In the interest of generating patient-specific hybrid 3D and 4D anatomical models of the heart, to identify and quantify the compromised myocardial tissue regions for revascularization therapy planning, in our previous work, we presented a spatial transformer network (STN) based convolutional neural network (CNN) architecture for registration of LGE and bSSFP cine CMR image datasets made available through the 2019 Multi-Sequence Cardiac Magnetic Resonance Segmentation Challenge (MS-CMRSeg). We performed a supervised registration by leveraging the region of interest (RoI) information using the manual annotations of the LV blood-pool, LV myocardium and right ventricle (RV) blood-pool provided for both the LGE and the bSSFP cine CMR images. In order to reduce the reliance on the number of manual annotations for training such network, we propose a joint deep learning framework consisting of three branches: a STN based RoI guided CNN for registration of LGE and bSSFP cine CMR images, an U-Net model for segmentation of bSSFP cine CMR images, and an U-Net model for segmentation of LGE CMR images. This results in learning of a joint multi-scale feature encoder by optimizing all three branches of the network architecture simultaneously. Our experiments show that the registration results obtained by training 25 of the available 45 image datasets in a joint deep learning framework is comparable to the registration results obtained by stand-alone STN based CNN model by training 35 of the available 45 image datasets and also shows significant improvement in registration performance when compared to the results achieved by the stand-alone STN based CNN model by training 25 of the available 45 image datasets.

Proceedings Article | 16 March 2020 Paper

Automated segmentation of cardiac chambers from cine cardiac MRI using an adversarial network architecture

Roshan Reddy Upendra, Shusil Dangi, Cristian Linte

Proceedings Volume 11315, 113152Y (2020) https://doi.org/10.1117/12.2550656

KEYWORDS: Image segmentation, Network architectures, Cardiovascular magnetic resonance imaging, Magnetic resonance imaging, Medical imaging, Convolutional neural networks

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