Dr. Prahlad G. Menon Profile

Dr. Prahlad G. Menon

Assistant Professor at Univ of Pittsburg

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Author

Publications (10)

Proceedings Article | 4 April 2022 Presentation + Paper

Image transformers with regional attention for classification of aneurysm rupture risk without explicit segmentation

Anish Salvi, Ender Finol, Prahlad Menon

Proceedings Volume 12036, 1203610 (2022) https://doi.org/10.1117/12.2610872

KEYWORDS: Convolutional neural networks, Medical imaging

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Proceedings Article | 15 February 2021 Presentation

Classification of lung disease using CT image encodings computed from a novel 3D segmentation U-Net

Prahlad Menon, Palash Shah

Proceedings Volume 11601, 116010Z (2021) https://doi.org/10.1117/12.2582021

KEYWORDS: Image segmentation, Computer programming, Lung, Image classification, Computed tomography, 3D image processing, 3D modeling, Medical imaging, Neural networks, Network architectures

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Proceedings Article | 2 March 2018 Paper

Generative statistical modeling of left atrial appendage appearance to substantiate clinical paradigms for stroke risk stratification

Soroosh Sanatkhani, Prahlad Menon

Proceedings Volume 10574, 105742L (2018) https://doi.org/10.1117/12.2291568

KEYWORDS: Atrial fibrillation, Statistical analysis, Computed tomography, Principal component analysis, Volume visualization, Image segmentation, Image filtering, 3D image processing

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Left atrial appendage (LAA) is the source of 91% of the thrombi in patients with atrial arrhythmias (~2.3 million US adults), turning this region into a potential threat for stroke. LAA geometries have been clinically categorized into four appearance groups viz. Cauliflower, Cactus, Chicken-Wing and WindSock, based on visual appearance in 3D volume visualizations of contrast-enhanced computed tomography (CT) imaging, and have further been correlated with stroke risk by considering clinical mortality statistics. However, such classification from visual appearance is limited by human subjectivity and is not sophisticated enough to address all the characteristics of the geometries. Quantification of LAA geometry metrics can reveal a more repeatable and reliable estimate on the characteristics of the LAA which correspond with stasis risk, and in-turn cardioembolic risk. We present an approach to quantify the appearance of the LAA in patients in atrial fibrillation (AF) using a weighted set of baseline eigen-modes of LAA appearance variation, as a means to objectify classification of patient-specific LAAs into the four accepted clinical appearance groups. Clinical images of 16 patients (4 per LAA appearance category) with atrial fibrillation (AF) were identified and visualized as volume images. All the volume images were rigidly reoriented in order to be spatially co-registered, normalized in terms of intensity, resampled and finally reshaped appropriately to carry out principal component analysis (PCA), in order to parametrize the LAA region’s appearance based on principal components (PCs/eigen mode) of greyscale appearance, generating 16 eigen-modes of appearance variation. Our pilot studies show that the most dominant LAA appearance (i.e. reconstructable using the fewest eigen-modes) resembles the Chicken-Wing class, which is known to have the lowest stroke risk per clinical mortality statistics. Our findings indicate the possibility that LAA geometries with high risk of stroke are higher-order statistical variants of underlying lower risk shapes.

Proceedings Article | 2 March 2018 Paper

Relating regional characteristics of left atrial shape to presence of scar in patients with atrial fibrillation

Soroosh Sanatkhani, Michael Oladosu, Karandeep Chera, Sotirios Nedios, Prahlad Menon

Proceedings Volume 10574, 105742N (2018) https://doi.org/10.1117/12.2293947

KEYWORDS: Atrial fibrillation, Image segmentation, Computed tomography, 3D image processing

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Proceedings Article | 24 February 2017 Paper

Personalized design and virtual evaluation of physician-modified stent grafts for juxta-renal abdominal aortic aneurysms

Soroosh Sanatkhani, Sanjeev Shroff, Prahlad Menon

Proceedings Volume 10133, 101332C (2017) https://doi.org/10.1117/12.2254404

KEYWORDS: CFD analysis, 3D modeling, Hemodynamics, 3D image processing, Surgery, Computed tomography, Virtual reality, Fluid dynamics, Arteries, Computational fluid dynamics, Blood circulation, 3D image reconstruction

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Proceedings Article | 29 March 2016 Paper

Investigating relationships between left atrial volume, symmetry, and sphericity

Prahlad Menon, Sotiris Nedios, Gerhard Hindricks, Andreas Bollmann

Proceedings Volume 9788, 97882J (2016) https://doi.org/10.1117/12.2216491

KEYWORDS: Shape analysis, Atrial fibrillation, Image analysis, Visualization, Biological research, Cardiac imaging, Image segmentation, 3D metrology, Computed tomography, Lanthanum

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Proceedings Article | 21 March 2016 Paper

In-vivo cell tracking to quantify endothelial cell migration during zebrafish angiogenesis

Prahlad Menon, Elizabeth Rochon, Beth Roman

Proceedings Volume 9784, 97844A (2016) https://doi.org/10.1117/12.2217614

KEYWORDS: Blood circulation, Heart, In vivo imaging, Two photon confocal microscopy, Genetics, Data acquisition, Image processing, Green fluorescent protein, Image enhancement, Image analysis, Image filtering, 3D image processing, Digital filtering, Confocal microscopy, 3D acquisition

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Proceedings Article | 21 March 2016 Paper

A neural network approach to lung nodule segmentation

Yaoxiu Hu, Prahlad Menon

Proceedings Volume 9784, 97842O (2016) https://doi.org/10.1117/12.2217291

KEYWORDS: Lung, Image segmentation, Neural networks, Computed tomography, Lung cancer, Image analysis, Biological research, Machine learning, Image processing, 3D image processing, Spherical lenses, Image classification, Binary data

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Proceedings Article | 20 March 2015 Paper

Evaluating MRI based vascular wall motion as a biomarker of Fontan hemodynamic performance

Prahlad Menon, Haifa Hong

Proceedings Volume 9413, 94132Q (2015) https://doi.org/10.1117/12.2080825

KEYWORDS: Hemodynamics, Scalable video coding, Image segmentation, Motion analysis, Magnetic resonance imaging, Visualization, Computer simulations, Heart, Cardiovascular magnetic resonance imaging, Blood

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Proceedings Article | 21 March 2014 Paper

Novel MRI-derived quantitative biomarker for cardiac function applied to classifying ischemic cardiomyopathy within a Bayesian rule learning framework

Prahlad Menon, Lailonny Morris, Mara Staines, Joao Lima, Daniel Lee, Vanathi Gopalakrishnan

Proceedings Volume 9034, 90341L (2014) https://doi.org/10.1117/12.2042118

KEYWORDS: Cardiovascular magnetic resonance imaging, Heart, Magnetic resonance imaging, Motion analysis, 3D imaging standards, 3D image processing, Video, Feature extraction, Image segmentation, Image analysis

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Characterization of regional left ventricular (LV) function may have application in prognosticating timely response and informing choice therapy in patients with ischemic cardiomyopathy. The purpose of this study is to characterize LV function through a systematic analysis of 4D (3D + time) endocardial motion over the cardiac cycle in an effort to define objective, clinically useful metrics of pathological remodeling and declining cardiac performance, using standard cardiac MRI data for two distinct patient cohorts accessed from CardiacAtlas.org: a) MESA – a cohort of asymptomatic patients; and b) DETERMINE – a cohort of symptomatic patients with a history of ischemic heart disease (IHD) or myocardial infarction. The LV endocardium was segmented and a signed phase-to-phase Hausdorff distance (HD) was computed at 3D uniformly spaced points tracked on segmented endocardial surface contours, over the cardiac cycle. An LV-averaged index of phase-to-phase endocardial displacement (P2PD) time-histories was computed at each tracked point, using the HD computed between consecutive cardiac phases. Average and standard deviation in P2PD over the cardiac cycle was used to prepare characteristic curves for the asymptomatic and IHD cohort. A novel biomarker of RMS error between mean patient-specific characteristic P2PD over the cardiac cycle for each individual patient and the cumulative P2PD characteristic of a cohort of asymptomatic patients was established as the RMS-P2PD marker. The novel RMS-P2PD marker was tested as a cardiac function based feature for automatic patient classification using a Bayesian Rule Learning (BRL) framework. The RMS-P2PD biomarker indices were significantly different for the symptomatic patient and asymptomatic control cohorts (p<0.001). BRL accurately classified 83.8% of patients correctly from the patient and control populations, with leave-one-out cross validation, using standard indices of LV ejection fraction (LV-EF) and LV end-systolic volume index (LV-ESVI). This improved to 91.9% with inclusion of the RMS-P2PD biomarker and was congruent with improvements in both sensitivity for classifying patients and specificity for identifying asymptomatic controls from 82.6% up to 95.7%. RMS-P2PD, when contrasted against a collective normal reference, is a promising biomarker to investigate further in its utility for identifying quantitative signs of pathological endocardial function which may boost standard image makers as precursors of declining cardiac performance.

Showing 5 of 10 publications

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