These are only select journal articles and proceedings. For a complete list, please see the Pubmed link.

The papers posted here are for educational purposes only and adherence to copyright laws is assumed.

  • M. A. Rahman, Z. Yu, R. Laforest, C. K. Abbey, B. A. Siegel, and A. K. Jha, “DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT”, in review (arxiv link)
  • Z. Liu, J. C. Mhlanga, H. Xia, B. A.  Siegel, and A. K. Jha, “Need for objective task-based evaluation of image-segmentation algorithms for quantitative PET: A study with ACRIN 6668/RTOG 0235 multi-center clinical trial data”, J. Nuc. Med., accepted
  • Z. Li, N. Benabdallah, R. Laforest, R. L. Wahl, D. Thorek, and A. K. Jha, “Joint regional uptake quantification of Thorium-227 and Radium-223 using a multiple-energy-window projection-domain quantitative SPECT method”, in review (arxiv link)
  • Z. Li, N. Benabdallah, J. Luo, R. L. Wahl, D. Thorek, A. K. Jha, “ISIT-QA: An in silico imaging trial to evaluate a low-count quantitative SPECT method for alpha particle radiopharmaceutical therapies”, in review
  • Z. Yu, M. A. Rahman, R. Laforest, T. H. Schindler, R. Wahl, B. A. Siegel, A. K. Jha, “Need for task-based evaluation of AI-based denoising methods: A study in the context of myocardial perfusion SPECT”, Med. Phys., 2023 (link)
  • M. A. Rahman, Z. Li, Z. Yu, R. Laforest, D. Thorek, A. K. Jha, “A list-mode multi-energy window low-count SPECT reconstruction method for isotopes with multiple emission peaks”, EJNMMI Phys., 2023 (link)
  • R. Li, J. Zhang, J. E. Saffitz, P. K. Woodard*, A. K. Jha*, “Carotid atherosclerotic plaque segmentation in multi-weighted MRI using a two-stage neural network: Advantages of training with high-resolution imaging and histology”, Frontiers in Cardiovascular Medicine, April 2023
  • Z. Liu, S. Wolfe, Z. Yu, R. Laforest, J. Mhlanga, T. Fraum, M. Itani, F. Dehdashti, B. Siegel, A. K. Jha, “Observer-study-based approaches to quantitatively evaluate the realism of synthetic medical images”, Phys. Med. Biol., 2023 (link)
  • J Bradshaw, M. D. McCradden, A. K. Jha, J. Dutta, B. Saboury, E. L. Siegel, and A. Rahmim, “Artificial intelligence algorithms need to be explainable – or do they?”, J. Nuc. Med. 2023, PMID: 37116913; Invited perspective
  • Z. Liu, J. C. Mhlanga, B. A. Siegel, A. K. Jha, “Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET”, SPIE Med. Imag. 2023 (accepted) (finalist for the Robert F. Wagner Best Student Paper award) (link)
  • Z. Yu, M. A. Rahman, C. Abbey, B. A. Siegel, A. K. Jha, “Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT”, SPIE Med Imag. 2023 (accepted) (winner of the Best Student Paper award in the Physics of Medical Imaging conference and finalist for the Robert F. Wagner Best Student Paper award) (link)
  • A. Rahman, Z. Yu, B. A. Siegel, A. K. Jha, “A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT”, SPIE Med. Imag. 2023 (accepted) (Honorable mention, SPIE Medical Imaging Best Poster award) (link)
  • Z. Li, N. Benabdallah, D. Abou, B. Baumann, R. Wahl, D. Thorek, A. K. Jha, “A projection-domain low-count quantitative SPECT method for alpha-particle emitting radiopharmaceutical therapy”, IEEE Trans. Rad. Plasma Sci., 2022 (link) (arxiv) (Media coverage)
  • M. Hatt, A. Krizsan, A. Rahmim, T. Bradshaw, P. Costa, A. Forgacs, R. Seifert, A. Zwanenburg, I. El Naqa, P. Kinahan, A. K. Jha, D. Visvikis, “Joint EANM/SNMMI Guideline on Radiomics in Nuclear Medicine: : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council.”, Eur. J. Nucl. Med. Mol. Imag., 50(2), 352-375, 2023 (link)
  • B. Saboury, T. J Bradshaw, R. Boellaard, I. Buvat, J. Dutta, M. Hatt, A. K Jha, Q. Li, C. Liu, H. McMeekin, M. A. Morris, P. Scott, E. Siegel, J. Sunderland, R. Wahl, S. Zuehlsdorff, and A. Rahmim, “Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem”, J. Nuc. Med., 64(2), 188-196, 2023 (link)
  • Z. Liu, H. Moon, Z. Li, R. Laforest, J. S. Perlmutter, S. A. Norris, A. K. Jha, “A tissue-fraction estimation-based segmentation method for quantitative dopamine transporter SPECT”, Med. Phys., 2022 (link) (arxiv) (Media coverage)
  • A. K. Jha, T. J. Bradshaw, I. Buvat, M. Hatt, P. KC, C. Liu, N. F. Obuchowski, B. Saboury, P. J. Slomka, J. J. Sunderland, R. L. Wahl, Z. Yu, S. Zuehlsdorff, A. Rahmim, and R. Boellaard, “Nuclear medicine and artificial intelligence: Best practices for evaluation (the RELAINCE guidelines),”, J. Nuc. Med., 2022 (open access link) (Media coverage)
  • A. K. Jha, K. J. Myers, N. A. Obuchowski, Z. Liu, M. A. Rahman, B. Saboury, A. Rahmim, B. A. Siegel, “Objective task-based evaluation of artificial intelligence-based medical imaging methods: Framework, strategies and role of the physician”, PET Clinics (special issue on AI in PET), 2021 (link) (arxiv) (Media coverage)
  • F Yousefirizi, A. K. Jha, J Brosch-Lenz, B Saboury, A Rahmim. “Toward High-Throughput Artificial Intelligence-Based Segmentation in Oncological PET Imaging”, PET Clin. 2021 Oct;16(4):577-596 (link) (arxiv).
  • N. Benabdallah, W. Scheve, N. Dunn, D. Silvestros, P. Schelker, D. Abou, U. Jammalamadaka, R. Laforest, Z. Li, J. Liu, D. H. Ballard, N. M. Maughan, H. Gay, B. C. Baumann, R. F. Hobbs, B. Rogers, A. Iravani, A. K. Jha, F. Dehdashti, D. L. J. Thorek, “Practical considerations for quantitative clinical SPECT/CT imaging of alpha particle emitting radioisotopes”, Theranostics, 2021 (link)
  • P Lu, N Benabdallah, W Jiang, B W Simons, H Zhang, R F Hobbs, D Ulmert, B Baumann, R K Pachynski, A K Jha, D L Thorek. “Blind Image Restoration Enhances Digital Autoradiographic Imaging of Radiopharmaceutical Tissue Distribution”, J Nucl Med. 2021 (link) (Journal cover)
  • K Dutta, S Roy, T D Whitehead, J Luo, A K Jha, S Li, J D Quirk, K I Shoghi. “Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary”, Cancers (Basel). 2021 Jul 28;13(15):3795. (link)
  • Li, N. Benabdallah, D. Thorek, A. K. Jha, “A multiple-energy-window projection-domain quantitative SPECT method for joint regional uptake quantification of Th-227 and Ra-223”, Proceedings of the International Conference on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2021 (Oral Award Nominee)
  • Z. Liu, J. C. Mhlanga, R. Laforest, P. Derenoncourt, B. A. Siegel, A. K. Jha, A Bayesian approach to tissue-fraction estimation for oncological PET segmentation”, Phys. Med. Biol. 2021 (link) (arxiv) (Media coverage)
  • Liu, R. Laforest, J. Mhlanga, T. Fraum, M. Itani, F. Dehdashti, B. Siegel, A. K. Jha, “Observer study-based evaluation of a stochastic and physics-based method to generate oncological PET images”, Proceedings SPIE Medical Imaging, 2021 (link) (arxiv)
  • Yu, M. A. Rahman, R. Laforest, S. Norris, A. K. Jha, “A physics and learning-based transmission-less attenuation compensation method for SPECT”, Proceedings SPIE Medical Imaging 2021 (link) (arxiv)
  • A. Rahman, A. K. Jha, “Task-based assessment of binned and list-mode SPECT systems”, Proceedings SPIE Medical Imaging 2021 (link) (arxiv)
  • M. A. Rahman, Y. Zhu, E. Clarkson, M. Kupinski, E. Frey, A. K. Jha, Fisher information analysis of list-mode SPECT emission data for joint estimation of activity and attenuation distribution”, Inverse Problems, 2020 (link) (arxiv)
  • K. Leung, W. Marashdeh, R. Wray, S. Ashrafinia, M. Pomper. A. Rahmim, A. K. Jha, “A physics-guided modular deep-learning-based automated framework for tumor segmentation in PET”, Phys. Med. Biol., 2020, link (arxiv) (Media coverage) (Source code)
  • D S Abou, A Rittenbach, R E Tomlinson et al. “Preclinical Single Photon Emission Computed Tomography of Alpha Particle-Emitting Radium-223” Cancer Biother. Radiopharm 2020 (link)
  • A. Rahman, R. Laforest, A. K. Jha, “A list-mode OSEM-based attenuation and scatter compensation method for SPECT”, IEEE Symposium on Biomedical Imaging 2020 (link) (arxiv)
  • L. Caucci, Z. Liu, A. K. Jha, H. Han, L R Furenlid, H. H. Barrett, “Towards Continuous-to-Continuous 3D Imaging in the Real World”, Phys. Med. Biol., 18; 64(18), 2019 (link)
  • S. Rezaei, P. Ghafarian, A. K. Jha, A. Rahmim, S. Sarkar, M. R. Ay, “Joint compensation of motion and partial volume effects in oncologic PET/CT imaging”, vol. 68, 52-60, 2019 Phys. Med. (link)
  • Y. Zhu, A. K. Jha*, D. F. Wong, A. Rahmim, “Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization”, Biomed. Opt. Exp. 9(7), 3106-21, 2018 (link) (*Corresponding author) (Source code)
  • A. K. Jha, Y. Zhu, S, Arridge, D. F. Wong, and A. Rahmim, “Incorporating reflection boundary conditions in radiative transport equation-based photon propagation and image reconstruction in diffuse optical imaging”, Biomed. Opt. Exp., 9 (4), 1389-1407, 2018 (link) (Source code)
  • Ye Li, S. O’Reilly, D. Plyku, S. T. Treves, Y. Du, F. Fahey, X. Cao, A. K. Jha, G. Sgouros, W. E. Bolch, and E. Frey, “A projection image database to investigate factors affecting image quality in weight-based dosing: Application to pediatric renal SPECT”, Phys. Med. Biol., 2018 (link).
  • A. K. Jha, E. Mena, B. Caffo, S. Ashrafinia, A. Rahmim, E. C. Frey and R. Subramaniam, “Practical no-gold-standard framework to evaluate quantitative imaging methods: Application to lesion segmentation in PET”, J. Med. Imag. , 2017 (Special Section on PET imaging), 4(1), 2017 (pdf).
  • F. E. A. Elshahaby, A. K. Jha, M. Ghaly, E. C. Frey, “A Comparison of Resampling Schemes for Estimating Model Observer Performance with Small Ensembles”, Phys. Med. Biol., 22;62(18):7300-7320, 2017 (pdf)
  • X. Li, A. K. Jha, M. Ghaly, F. E. A. Elshahaby, J. M. Links, E. C. Frey, “A Sub-ensemble-based approach for detection task performance evaluation for non-multivariate-normally distributed data using multi-template linear observer strategies”, IEEE Trans. Med. Imaging, 36(4), 2017 (pdf)
  • S. Ashrafinia, H. Mohy-ud-Din, N. Karakatsanis, A. K. Jha, M. Casey, D. Kadrmas; A. Rahmim, “Generalized PSF-Modelling for Optimized Quantitation in PET Imaging”, Phys. Med. Biol., 2017 (pdf)
  • E. Mena, M. Taghipour, S. Sheikhbahaei, A. K. Jha, L. Solnes, R. M. Subramaniam, “Value of intra-tumoral metabolic heterogeneity and quantitative 18F-FDG PET/CT parameters to predict prognosis, in patients with HPV-positive primary oropharyngeal squamous cell carcinoma”, Clin. Nuc. Med., 42(5); e227-34, 2017 (pdf)
  • A. K. Jha, Y. Zhu, D. F. Wong, A. Rahmim, “A radiative transfer equation-based image-reconstruction method incorporating boundary conditions for diffuse optical imaging”, SPIE Medical Imaging, 1013705-1013705- 2017 (link)
  • A. K. Jha and E. Frey, “No-gold-standard evaluation of image-acquisition methods using patient data”, SPIE Medical Imaging, 101360L-101360L-8, 2017 (link)
  • E. Mena, S. Sheikhbahaei, M. Taghipour, A. K. Jha, J. Xiao, E. Vicente, R. M. Subramaniam, “18F-FDG PET/CT Metabolic Tumor Volume and intra-tumoral heterogeneity: Impact of Dual-Time-Point and Segmentation Methods”, Clin. Nuc. Med., 42(1), e16-e21, 2017 (link)
  • A. K. Jha, J. J. Rodriguez, A. T. Stopeck, “A maximum-likelihood method to estimate a single ADC value of lesions using diffusion MRI”, Mag. Res. Med., 76(6), 2016 (pdf) (Directions to source code)
  • A. K. Jha, B. Caffo, E. Frey, “A no-gold-standard technique for objective assessment of quantitative nuclear-medicine imaging methods”, Phys. Med. Biol., 61(7), 2780-2800, 2016 (pdf)
  • F. E. A. Elshahaby, M. Ghaly, A. K. Jha, E. C. Frey, “Factors Affecting the Normality of Anthropomorphic Channel Outputs: An Investigation using Realistic Myocardial Perfusion SPECT Images”, J. Med. Imag., 3(1), 015503-1-17, 2016 (pdf)
  • R. M. Stephen, A. K. Jha, D. J. Roe, et al., “Diffusion MRI with Semi-Automated Segmentation Can Serve as a Restricted Predictive Biomarker of the Therapeutic Response of Liver Metastasis”, Magn. Res. Imag., 33(10), 1267-33, 2015 (pdf) (Directions to source code)
  • A. K. Jha, H. H. Barrett, E. Frey, E. Clarkson, L. Caucci, M. A. Kupinski, “Singular value decomposition for photon-processing nuclear imaging systems and applications for reconstruction and computing null functions”, Phys. Med. Biol., 60, 7359-7385, 2015 (link)
  • A. K. Jha and E. Frey, “Incorporating prior information in a no-gold-standard technique to assess quantitative SPECT reconstruction methods”, 13th Intl. Meet. Fully Three-Dim. Image Recon. Radiol. Nucl. Med, 2015 (pdf)
  • A. K. Jha, E. Frey, “Estimating ROI activity concentration with photon-processing and photon-counting SPECT systems”, SPIE Med. Imag. Conf. 2015 (link)
  • V. Bora, H. H. Barrett, A. K. Jha, E. Clarkson, “Impact of Fano factor on position and energy estimation in scintillation detectors”, IEEE Trans. Nucl. Sci., 99, pp. 11-15, 2015 (link).
  • A. K. Jha, E. Clarkson, M. A. Kupinski, “An ideal-observer framework to investigate signal detectability in diffuse optical imaging”, Biomed. Opt. Express, 4(10), 2107-23, 2013 (link) (Source code)
  • A. K. Jha, H. T. van Dam, M. A. Kupinski, E. Clarkson, “Simulating Silicon photomultiplier response to scintillation light”, IEEE Trans. Nucl. Sci., 60(1), 336-51, 2013 (link) (Source code)
  • A. K. Jha, M. A. Kupinski, H. H. Barrett, E. Clarkson, J. H. Hartman, “A three-dimensional Neumann-series approach to model light transport in non-uniform media”, J. Opt. Soc. Amer. A, 29(8), 1885-99, 2012 (link) (Source code)
  • A. K. Jha, M. A. Kupinski, T. Masumura, E. Clarkson, A. A. Maslov, H. H. Barrett, “Simulating photon transport in uniform media using the radiative transport equation: A study using the Neumann-series approach”, J. Opt. Soc. Amer. A, 29(8), 1741-1757, 2012. (Top 10 downloaded articles in Aug. 2012 edition) (link) (Source code)
  • A. K. Jha, M. A. Kupinski, J. J. Rodriguez, R. Stephen, A. Stopeck, “Task-based evaluation of segmentation algorithms for diffusion-weighted MRI without using a gold standard”, Phys. Med. Biol., 57(13) 4425-46, 2012 (link)
  • Y. Zhu, A. K. Jha*, J. K. Dreyer, H. Le, J. U. Kang, P. E. Roland, D. F. Wong, and A. Rahmim, “A three-step reconstruction method for fluorescence molecular tomography based on compressive sensing”, Proc. SPIE 10059, Optical Tomography and Spectroscopy of Tissue, 1005911, 2017 (*corresponding author) (link)
  • L. Caucci, A. K. Jha, L. R. Furenlid, E. W. Clarkson, M. A. Kupinski, H. H. Barrett, “Image science with photon-processing detectors”, IEEE Nucl. Sci. Symp. Med. Imag. Conf., 2013 (link)
  • A. K. Jha, H. H. Barrett, E. Clarkson, L. Caucci, M. A. Kupinski, “Analytic list-mode reconstruction techniques”, 12th Intl. Meet. Fully Three-Dim. Image Recon. Radiol. Nucl. Med, CA, June 2013 (pdf)
  • A. K. Jha, E. Clarkson, M. A. Kupinski, H. H. Barrett. “Joint reconstruction of activity and attenuation map using LM SPECT emission data”, Proc. SPIE 8668 Med. Imag., 86681W, 2013 (link)
  • Park, B. W. Miller, A. K. Jha, L. R. Furenlid, W. C. Hunter, H. H. Barrett, “A prototype detector for a novel high-resolution PET system: Bazooka PET”, IEEE Med Imag. Conf., 2012 (link)
  • A. K. Jha, J. J. Rodriguez, R. M. Stephen, A. T. Stopeck, “A clustering algorithm for liver lesion segmentation of diffusion-weighted MR images”, Proc. IEEE Southwest Symp. Image Anal. Interpret., pp. 93-6, 2010 (link)
  • A. K. Jha, M. A. Kupinski, J. J. Rodriguez, R. M. Stephen, A. T. Stopeck, “Evaluating segmentation algorithms for diffusion-weighted MR images: a task-based approach”, Proc. SPIE 7627,76270L1-8, 2010 (Best Student Paper Award) (link)