Dr. Abhinav Kumar Jha Profile

Dr. Abhinav Kumar Jha

Assistant Professor at Washington Univ in St Louis

SPIE Involvement:

Author

Area of Expertise:

Image Science , SPECT , PET , Task-based image quality evaluation , Imaging Physics , Machine learning

Websites:

Company Website

Profile Summary

Professor Jha is an assistant professor with joint appointments in biomedical engineering and radiology and courtesy appointment in the Department of Electrical & Systems Engineering at Washington University. Previously, he was a faculty at Johns Hopkins University. Professor Jha obtained his PhD from the College of Optical Sciences, University of Arizona, where he was the valedictorian of his graduating batch. He is a recipient of the NSF CAREER, NIBIB Trailblazer, and NIH R01 awards. His research and teaching have also been recognized through several other awards such as the Young Investigator Symposium Award of Distinction for Translational Sciences at the ECOG-ACRIN meeting, Michael B. Merickel Best Student Paper award at SPIE Medical Imaging, Therapy Center for Excellence Young Investigator award, Imaging Innovator of the week of the Academy for Radiology and Biomedical Imaging, 30 Early Career Professionals to Watch of the Society of Nuclear Medicine and Molecular Imaging, and the Outstanding Teaching Associate Award. He is on the Board of Directors of the Physics, Instrumentation and Data Sciences Council of the Society of Nuclear Medicine and Molecular Imaging and a senior member of the IEEE.

Publications (26)

Proceedings Article | 1 April 2024 Presentation + Paper

Presentation + Paper

WIN-PDQ: a Wiener-estimator-based projection-domain quantitative SPECT method that accounts for intra-regional uptake heterogeneity

Zekun Li, Nadia Benabdallah, Daniel L. Thorek, Abhinav Jha

Proceedings Volume 12925, 1292512 (2024) https://doi.org/10.1117/12.3006569

KEYWORDS: Single photon emission computed tomography, Therapeutics, Covariance matrices, Estimation theory, Mathematical optimization, Radium

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Single photon emission computed tomography (SPECT) can enable the quantification of activity uptake in lesions and at-risk organs in α-particle-emitting radiopharmaceutical therapies (α-RPTs). However, this quantification is challenged by the extremely low detected photon counts, complicated isotope physics, and the image-degrading effects in α-RPT SPECT. Thus, strategies to optimize the SPECT system and protocol designs for the task of regional uptake quantification are much needed. Objectively performing this task-based optimization requires a reliable (accurate and precise) regional uptake quantification method. Conventional reconstruction-based quantification (RBQ) methods have been observed to be erroneous for α-RPT SPECT. Projection-domain quantification methods, which estimate regional uptake directly from SPECT projections, have demonstrated potential in providing reliable regional uptake estimates, but these methods assume constant uptake within the regions, an assumption that may not hold. To address these challenges, we propose Wiener INtegration Projection-Domain Quantification (WIN-PDQ), a Wiener-estimator-based projection-domain quantitative SPECT method. The method accounts for the heterogeneity within the regions of interest while estimating mean uptake. An early-stage evaluation of the method was conducted using 3D Monte Carlo-simulated SPECT of anthropomorphic phantoms with ²²³Ra uptake and lumpy-model-based intra-regional uptake heterogeneity. In this evaluation with phantoms of varying mean regional uptake and intra-regional uptake heterogeneity, the WIN-PDQ method yielded ensemble unbiased estimates and significantly outperformed both reconstruction-based and previously proposed projection-domain quantification methods in terms of normalized root ensemble mean squared error. In conclusion, based on these preliminary findings, the proposed WIN-PDQ method is showing potential for estimating mean regional uptake in α-RPTs and towards enabling the objective task-based optimization of SPECT system and protocol designs.

Proceedings Article | 29 March 2024 Presentation + Paper

Presentation + Paper

Can patient-specific acquisition protocol improve performance on defect detection task in myocardial perfusion SPECT?

Nu Ri Choi, Md Ashequr Rahman, Zitong Yu, Barry Siegel, Abhinav Jha

Proceedings Volume 12929, 129290O (2024) https://doi.org/10.1117/12.3006924

KEYWORDS: Anatomy, Defect detection, Medical image reconstruction, Information operations, Single photon emission computed tomography, Breast, Computed tomography, Perfusion imaging, Image quality, Design

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Myocardial perfusion imaging using single-photon emission computed tomography (SPECT), or myocardial perfusion SPECT (MPS) is a widely used clinical imaging modality for the diagnosis of coronary artery disease. Current clinical protocols for acquiring and reconstructing MPS images are similar for most patients. However, for patients with outlier anatomical characteristics, such as large breasts, images acquired using conventional protocols are often sub-optimal in quality, leading to degraded diagnostic accuracy. Solutions to improve image quality for these patients outside of increased dose or total acquisition time remain challenging. Thus, there is an important need for new methodologies that can help improve the quality of the acquired images for such patients, in terms of the ability to detect myocardial perfusion defects. One approach to improving this performance is adapting the image acquisition protocol specific to each patient. Studies have shown that in MPS, different projection angles usually contain varying amounts of information for the detection task. However, current clinical protocols spend the same time at each projection angle. In this work, we evaluated whether an acquisition protocol that is optimized for each patient could improve performance on the task of defect detection on reconstructed images for patients with outlier anatomical characteristics. For this study, we first designed and implemented a personalized patient-specific protocol-optimization strategy, which we term precision SPECT (PRESPECT). This strategy integrates the theory of ideal observers with the constraints of tomographic reconstruction to optimize the acquisition time for each projection view, such that performance on the task of detecting myocardial perfusion defects is maximized. We performed a clinically realistic simulation study on patients with outlier anatomies on the task of detecting perfusion defects on various realizations of low-dose scans by an anthropomorphic channelized Hotelling observer. Our results show that using PRESPECT led to improved performance on the defect detection task for the considered patients. These results provide evidence that personalization of MPS acquisition protocol has the potential to improve defect detection performance on reconstructed images by anthropomorphic observers for patients with outlier anatomical characteristics. Thus, our findings motivate further research to design optimal patient-specific acquisition and reconstruction protocols for MPS, as well as developing similar approaches for other medical imaging modalities.

Proceedings Article | 29 March 2024 Presentation + Paper

Presentation + Paper

How accurately can quantitative imaging methods be ranked without ground truth: an upper bound on no-gold-standard evaluation

Yan Liu, Abhinav Jha

Proceedings Volume 12929, 129290W (2024) https://doi.org/10.1117/12.3006888

KEYWORDS: Gold, Tumors, Statistical analysis, Single photon emission computed tomography, Positron emission tomography, Biomedical applications, Magnetic resonance imaging

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Proceedings Article | 7 April 2023 Presentation + Paper

Presentation + Paper

Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT

Zitong Yu, Md Ashequr Rahman, Craig Abbey, Barry Siegel, Abhinav Jha

Proceedings Volume 12463, 124631E (2023) https://doi.org/10.1117/12.2654500

KEYWORDS: Single photon emission computed tomography, Signal attenuation, Computed tomography, Image segmentation, Medical image reconstruction, Attenuation, Defect detection

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Proceedings Article | 3 April 2023 Poster + Paper

Poster + Paper

A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT

Ashequr Rahman, Zitong Yu, Barry Siegel, Abhinav Jha

Proceedings Volume 12467, 1246719 (2023) https://doi.org/10.1117/12.2655629

KEYWORDS: Denoising, Single photon emission computed tomography, Defect detection, Image processing, Image filtering, Visual system, Artificial intelligence, Deep learning

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Proceedings Article | 3 April 2023 Presentation + Paper

Presentation + Paper

Observer study-based evaluation of TGAN architecture used to generate oncological PET images

Roberto Fedrigo, Fereshteh Yousefirizi, Ziping Liu, Abhinav Jha, Robert Bergen, Jean-Francois Rajotte, Raymond Ng, Ingrid Bloise, Sara Harsini, Dan Kadrmas, Carlos Uribe, Arman Rahmim

Proceedings Volume 12467, 124670S (2023) https://doi.org/10.1117/12.2652661

KEYWORDS: Positron emission tomography, Education and training, Cancer, Image segmentation, Tumors, Video, Artificial intelligence, Nuclear medicine, Medical imaging

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Proceedings Article | 3 April 2023 Presentation + Paper

Presentation + Paper

Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET

Ziping Liu, Joyce Mhlanga, Barry Siegel, Abhinav Jha

Proceedings Volume 12467, 124670R (2023) https://doi.org/10.1117/12.2647894

KEYWORDS: Image segmentation, Positron emission tomography, Artificial intelligence

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Proceedings Article | 4 April 2022 Presentation + Paper

Presentation + Paper

Deep learning framework to synthesize high-count preclinical PET images from low-count preclinical PET images

Kaushik Dutta, Ziping Liu, Richard Laforest, Abhinav Jha, Kooresh Isaac Shoghi

Proceedings Volume 12031, 120311F (2022) https://doi.org/10.1117/12.2612729

KEYWORDS: Positron emission tomography, Photons, Tumors, Data modeling, Signal to noise ratio, Liver, Denoising, Reconstruction algorithms, Image quality, Visualization, Image denoising

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Proceedings Article | 4 April 2022 Presentation + Paper

Presentation + Paper

Investigating the limited performance of a deep-learning-based SPECT denoising approach: an observer-study-based characterization

Zitong Yu, Md. Ashequr Rahman, Abhinav Jha

Proceedings Volume 12035, 120350D (2022) https://doi.org/10.1117/12.2613134

KEYWORDS: Denoising, Single photon emission computed tomography, Signal detection, Medical imaging, Defect detection, Nuclear medicine, Imaging systems, Signal processing

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Multiple objective assessment of image-quality (OAIQ)-based studies have reported that several deep-learning (DL)-based denoising methods show limited performance on signal-detection tasks. Our goal was to investigate the reasons for this limited performance. To achieve this goal, we conducted a task-based characterization of a DLbased denoising approach for individual signal properties. We conducted this study in the context of evaluating a DL-based approach for denoising single photon-emission computed tomography (SPECT) images. The training data consisted of signals of different sizes and shapes within a clustered-lumpy background, imaged with a 2D parallel-hole-collimator SPECT system. The projections were generated at normal and 20% low-dose level, both of which were reconstructed using an ordered-subset-expectation-maximization (OSEM) algorithm. A convolutional neural network (CNN)-based denoiser was trained to denoise the low-dose images. The performance of this CNN was characterized for five different signal sizes and four different signal-to-background ratio (SBRs) by designing each evaluation as a signal-known-exactly/background-known-statistically (SKE/BKS) signal-detection task. Performance on this task was evaluated using an anthropomorphic channelized Hotelling observer (CHO). We observed that the DL-based denoising approach did not improve performance on the signal-detection task for any of the signal types. None of signal types did the DL-based denoising approach improve performance. Further investigations revealed a drop in the mean of the differences between the signal-present and the signal-absent images, resulting in this limited performance on detection tasks. Additionally, it was observed that evaluation with fidelity-based figures of merit (root mean square error and structural similarity index) directly contradicted the observer study findings for all signals. Overall, these results provide new insights on the performance of the DL-based denoising approach as a function of signal size and contrast. More generally, the observer study-based characterization provides a mechanism to evaluate the sensitivity of the method to specific object properties, and may be explored as analogous to characterizations such as modulation transfer function for linear systems. Finally, this work underscores the need for objective task-based evaluation of DL-based denoising approaches.

Proceedings Article | 4 April 2022 Presentation + Paper

Presentation + Paper

No-gold-standard evaluation of quantitative imaging methods in the presence of correlated noise

Ziping Liu, Zekun Li, Joyce Mhlanga, Barry Siegel, Abhinav Jha

Proceedings Volume 12035, 120350M (2022) https://doi.org/10.1117/12.2605762

KEYWORDS: Image segmentation, Positron emission tomography, Medical imaging, Single photon emission computed tomography, Magnetic resonance imaging

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Proceedings Article | 15 February 2021 Presentation + Paper

Presentation + Paper

A physics and learning-based transmission-less attenuation compensation method for SPECT

Zitong Yu, Md Ashequr Rahman, Thomas Schindler, Richard Laforest, Abhinav Jha

Proceedings Volume 11595, 1159512 (2021) https://doi.org/10.1117/12.2582350

KEYWORDS: Signal attenuation, Single photon emission computed tomography, Physics, Computed tomography, Visualization, Brain mapping, Photons, Image segmentation, Defect detection, Data acquisition

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Proceedings Article | 15 February 2021 Presentation + Paper

Presentation + Paper

Observer study-based evaluation of a stochastic and physics-based method to generate oncological PET images

Ziping Liu, Richard Laforest, Joyce Mhlanga, Tyler Fraum, Malak Itani, Farrokh Dehdashti, Barry Siegel, Abhinav Jha

Proceedings Volume 11599, 1159905 (2021) https://doi.org/10.1117/12.2582765

KEYWORDS: Positron emission tomography, Tumors, Stochastic processes, Image segmentation, Radiotherapy

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Proceedings Article | 15 February 2021 Presentation + Paper

Presentation + Paper

Task-based assessment of binned and list-mode SPECT systems

Md Ashequr Rahman, Abhinav Jha

Proceedings Volume 11599, 1159904 (2021) https://doi.org/10.1117/12.2582266

KEYWORDS: Single photon emission computed tomography, Imaging systems, Data acquisition, Yield improvement, Signal detection, Reconstruction algorithms, Expectation maximization algorithms, Error analysis, Binary data

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Proceedings Article | 15 March 2019 Paper

Paper

Incorporating CT prior information in the robust fuzzy C-means algorithm for QSPECT image segmentation

Junyu Chen, Abhinav Jha, Eric Frey

Proceedings Volume 10949, 109491W (2019) https://doi.org/10.1117/12.2506805

KEYWORDS: Image segmentation, Bone, Computed tomography, Single photon emission computed tomography, Image processing algorithms and systems, Fuzzy logic, 3D image processing

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SPIE Journal Paper | 10 April 2017 Open Access

Open Access

Listening to membrane potential: photoacoustic voltage-sensitive dye recording

Haichong Zhang, Ping Yan, Jeeun Kang, Diane Abou, Hanh N. Le, Abhinav Jha, Daniel L. Thorek, Jin Kang, Arman Rahmim, Dean Wong, Emad Boctor, Leslie Loew

JBO, Vol. 22, Issue 04, 045006, (April 2017) https://doi.org/10.1117/12.10.1117/1.JBO.22.4.045006

KEYWORDS: Luminescence, Absorbance, Photoacoustic spectroscopy, Voltage sensitive dyes, Brain, Ultrasonography, Neuroimaging, Quantum efficiency, Chromophores, Polarization

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Proceedings Article | 13 March 2017 Presentation + Paper

Presentation + Paper

A radiative transfer equation-based image-reconstruction method incorporating boundary conditions for diffuse optical imaging

Abhinav Jha, Yansong Zhu, Dean Wong, Arman Rahmim

Proceedings Volume 10137, 1013705 (2017) https://doi.org/10.1117/12.2255802

KEYWORDS: Brain, Refractive index, Neuroimaging, Reconstruction algorithms, Diffuse optical imaging, 3D modeling, Scattering, Optical imaging, Tissue optics, Laser scattering, Sensors, Absorption, Reflection

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Proceedings Article | 10 March 2017 Presentation + Paper

Presentation + Paper

No-gold-standard evaluation of image-acquisition methods using patient data

Abhinav Jha, Eric Frey

Proceedings Volume 10136, 101360L (2017) https://doi.org/10.1117/12.2255902

KEYWORDS: Image acquisition, Scanners, Single photon emission computed tomography, Image quality, Quantitative analysis, Tumors, Positron emission tomography, Collimators, Data modeling, Medical imaging, Cancer

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SPIE Journal Paper | 3 March 2017

Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

Abhinav Jha, Esther Mena, Brian Caffo, Saeed Ashrafinia, Arman Rahmim, Eric Frey, Rathan Subramaniam

JMI, Vol. 4, Issue 01, 011011, (March 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.1.011011

KEYWORDS: Video, Video compression, Echocardiography, Motion measurement, Video processing, Image segmentation, Image quality, Diagnostics, Image processing, Motion estimation

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Proceedings Article | 17 February 2017 Paper

Paper

A three-step reconstruction method for fluorescence molecular tomography based on compressive sensing

Yansong Zhu, Abhinav Jha, Jakob Dreyer, Hanh N. Le, Jin Kang, Per Roland, Dean Wong, Arman Rahmim

Proceedings Volume 10059, 1005911 (2017) https://doi.org/10.1117/12.2252664

KEYWORDS: Tomography, Reconstruction algorithms, Fluorescence tomography, Expectation maximization algorithms, Compressed sensing, Brain, Scattering, In vivo imaging, Data acquisition, Sensors

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SPIE Journal Paper | 28 January 2016 Open Access

Open Access

Factors affecting the normality of channel outputs of channelized model observers: an investigation using realistic myocardial perfusion SPECT images

Fatma Elzahraa Elshahaby, Michael Ghaly, Abhinav Jha, Eric Frey

JMI, Vol. 3, Issue 01, 015503, (January 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.1.015503

KEYWORDS: Statistical analysis, Information operations, Medical imaging, Single photon emission computed tomography, Data modeling, Binary data, Performance modeling, Imaging systems, Image filtering, Heart

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Proceedings Article | 13 April 2015 Paper

Paper

Objective evaluation of reconstruction methods for quantitative SPECT imaging in the absence of ground truth

Abhinav Jha, Na Song, Brian Caffo, Eric Frey

Proceedings Volume 9416, 94161K (2015) https://doi.org/10.1117/12.2081286

KEYWORDS: Single photon emission computed tomography, Imaging systems, Algorithm development, Gold, Medical imaging, Reconstruction algorithms, Image segmentation, Data modeling, Advanced distributed simulations, Computed tomography

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Proceedings Article | 13 April 2015 Paper

Paper

Estimating ROI activity concentration with photon-processing and photon-counting SPECT imaging systems

Abhinav Jha, Eric Frey

Proceedings Volume 9412, 94120R (2015) https://doi.org/10.1117/12.2082278

KEYWORDS: Imaging systems, Single photon emission computed tomography, Sensors, Photodetectors, Statistical analysis, Reconstruction algorithms, Data acquisition, Fourier transforms, Medical imaging, Computed tomography

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Proceedings Article | 17 March 2015 Paper

Paper

The effect of signal variability on the histograms of anthropomorphic channel outputs: factors resulting in non-normally distributed data

Fatma E. Elshahaby, Michael Ghaly, Abhinav Jha, Eric Frey

Proceedings Volume 9416, 94160P (2015) https://doi.org/10.1117/12.2081629

KEYWORDS: Medical imaging, Statistical analysis, Image processing, Nuclear medicine, Image filtering, Image restoration, Visual process modeling, Matrices, Monte Carlo methods, Visual system

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Proceedings Article | 26 September 2013 Paper

Paper

Impact of fano factor on position and energy estimation in scintillation detectors

Vaibhav Bora, Harrison Barrett, Abhinav Jha, Eric Clarkson

Proceedings Volume 8854, 885405 (2013) https://doi.org/10.1117/12.2033642

KEYWORDS: Sensors, Photons, Scintillation, Crystals, Scintillators, Gamma radiation, Geometrical optics, Detector arrays, Spatial resolution, Image sensors

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Proceedings Article | 19 March 2013 Paper

Paper

Joint reconstruction of activity and attenuation map using LM SPECT emission data

Abhinav Jha, Eric Clarkson, Matthew Kupinski, Harrison Barrett

Proceedings Volume 8668, 86681W (2013) https://doi.org/10.1117/12.2008111

KEYWORDS: Signal attenuation, Single photon emission computed tomography, Expectation maximization algorithms, Reconstruction algorithms, Computed tomography, Data modeling, Gamma radiation, Compton scattering, Algorithm development, Data processing

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Proceedings Article | 27 February 2010 Paper

Paper

Evaluating segmentation algorithms for diffusion-weighted MR images: a task-based approach

Abhinav Jha, Matthew Kupinski, Jeffrey Rodríguez, Renu Stephen, Alison Stopeck

Proceedings Volume 7627, 76270L (2010) https://doi.org/10.1117/12.845515

KEYWORDS: Image segmentation, Expectation maximization algorithms, Error analysis, Diffusion, Magnetic resonance imaging, Image processing algorithms and systems, Signal to noise ratio, Image analysis, Medical imaging, Fuzzy logic

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Showing 5 of 26 publications

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