Author: a.jha

We have been awarded our first R01! The grant is on the topic of no-gold-standard evaluation of quantitative imaging methods. More details here.

We are excited to be presenting three talks at the upcoming SPIE Medical Imaging meeting Z. Liu, R. Laforest, J. Mhlanga, T. Fraum, M. Itani, F. Dehdashti, B. A. Siegel, A. K. Jha, “Observer study-based evaluation of a stochastic and physics-based method to generate oncological PET images” (link to proceedings and presentation) (arxiv) Z. Yu, […]

Delighted to share that the four abstracts we had submitted to the SNMMI 2020 Annual Meeting were all accepted for talks and then also highlighted in the summary session of the PIDS council. The references are as below: 1. H.S. Moon, Z. Liu, M. Ponisio, R. Laforest, and A. K. Jha, A physics-guided and learning-based […]

Delighted to share that our article on analyzing the information content in SPECT emission data on the task of jointly estimating activity and attenuation distribution was accepted to Inverse Problems. Link to the article is here. Arxiv link here. This work shows that scatter-window photons in SPECT, that are typically discarded, when acquired in list-mode […]

Excited to share that our article on developing a modular physics-guided deep-learning-based method for segmenting oncological PET images was recently accepted to the journal Physics in Medicine and Biology. Here is the link to the article, and here is the arxiv link. The article was also highlighted in the McKelvey Engineering newsletter. Source code for […]

Congratulations to Hae Sol on defending his thesis on developing new image-analysis methods for DaTscan SPECT. His committee consisted of Drs. Joel Perlmutter, Richard Laforest, Dennis Barbour, and Abhinav Jha. Hae Sol is the first Masters student to defend from the lab. Exciting times!

Zekun Li has been awarded the SNMMI Student Research Grant: Discovering Molecular Imaging. In this grant, Zekun will develop novel methods for SPECT reconstruction. Congratulations, Zekun! News article here: https://engineering.wustl.edu/news/Pages/doctoral-student-wins-snmmi-grant-to-study-novel-imaging-methods.aspx

The CMI lab had a great presence at the IEEE International Symposium on Biomedical Imaging 2020. Lab members gave three talks on SPECT reconstruction and image analysis during a very well-attended virtual session on Computational Imaging for Nuclear Medicine. Asheq also received financial support to cover his registration costs at the meeting. Thanks ISBI organizing […]

Dr. Jha from the CMI Lab gave a brief invited presentation at the  FDA public workshop on the Emerging Role of Artificial Intelligence in Medical Imaging. His presentation was titled “AI in Nuclear Medicine: Opportunities and Risks”. The talk was later highlighted in the day’s summary. The webcast for the presentation is available here (at […]

Excited to report that our collaborative study on compensating motion and partial volume effects in PET imaging was recently published. Link to article here.

The CMI lab had an excellent presence at the WMIC 2019 meeting. Ziping and Hae Sol presented their work on combining physics and machine learning for segmenting nuclear-medicine images. Dr. Jha gave an invited talk at the meeting. Overall, a great conference!

At the WMIC 2019 meeting, Dr. Jha gave an invited talk on methods for quantitative medical imaging at the Imaging Data Science Forum. The focus of the talk was on new and improved methods for quantitative nuclear-medicine imaging, especially given the emergence of machine learning-based tools.

Delighted to have collaborated with the Center for Gamma Ray Imaging on developing a methodology for continuous-to-continuous 3D imaging in the real world. More details here.

Qiye Tan and Jinxin Liu, summer interns at our lab, presented the outstanding work they have conducted during their stay here at a seminar yesterday. Best wishes to these two students as they go ahead in their careers.

The CMI lab had a great time celebrating Holi, the festival of colors!

Dr. Jha has been selected as Ones to Watch: 30 early career researchers making a difference in 2019 by the Society of Nuclear Medicine and Molecular Imaging (SNMMI).

Delighted to share that Ziping Liu, PhD student from the Department of Biomedical Engineering at Washington University, joined the lab. Ziping obtained his Bachelors from the Department of Biomedical Engineering at University of Rochester. His research interests are in Task-specific medical imaging, PET System Modeling, and Image Analysis. Welcome Ziping! 

Excited to share that Md Ashequr Rahman, PhD student from the Image Science PhD program, has joined the Computational Medical Imaging Lab. Asheq obtained his Bachelors in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology, Bangladesh. Welcome Asheq!

Dr. Abhinav Jha presented the keynote at the Siteman Cancer Center Oncologic Imaging Research Program Student Poster Session and Special lecture. His talk was titled  “Computational Medical Imaging for Precision Medicine” and discussed various computational-imaging, that included machine-learning and model-based methods, to improve precision medicine. Further, excited to share that Priyanshu Jain, a student member […]

Our lab has been awarded the NIBIB Trailblazer award for a project on developing new methods for transmission-less attenuation compensation in SPECT imaging! In this project, we propose to design and validate novel methods for SPECT reconstruction that will not require an additional transmission scan (such as the CT scan) for attenuation compensation. For more […]

Our lab has been awarded the NVIDIA GPU grant! NVIDIA will provide us state-of-the-art Quadro P6000 card. Armed with 3,840 CUDA parallel processing cores, a peak single-precision performance of 12 TFLOPS, and a 24 GB GPU memory, this card will provide an excellent high-performance computing platform for several computational medical imaging solutions that we are […]

Our paper on developing a new reconstruction method for fluorescence molecular tomography in the context of transcranial imaging has been published in Biomedical Optics Express. The paper demonstrates that accurately modeling noise in FMT while accounting for sparsity leads to improved reconstruction performance!

Our abstract titled “A deep-learning-based fully automated segmentation approach to delineate tumors in FDG-PET images of patients with lung cancer” was accepted for an oral presentation at the 2018 SNMMI Annual Meeting. In this work, we developed and demonstrated the efficacy of a deep learning-based method for tumor segmentation in PET images. Since these images are […]

Dr. Abhinav Jha will be joining Washington University in St. Louis as an Assistant Professor of Biomedical Engineering in the School of Engineering and Applied Sciences and of the Mallinckrodt Institute of Radiology at the School of Medicine starting April 2018. His recruitment is part of the Imaging Sciences Initiative at WashU.

Optimizing myocardial perfusion SPECT systems and methods for perfusion defect detection is highly significant. While mathematical observers exist for this purpose, they do not account for the variability in cardiac defect characteristics. In a series of articles, we investigate the causes for this issue and propose a novel mathematical observer that addresses this issue. X. […]

Metrics derived from quantitative PET, in particular intra-tumor heterogeneity, have sparked considerable interest as biomarkers for prognosis and predicting therapy response. In collaboration with radiologists at the Division of Nuclear Medicine, we have been investigating the role of intra-tumor heterogeneity in patients with different cancers. We recently published a couple of articles based on our research, […]

We have had three talks accepted for presentation at the Society of Nuclear Medicine and Molecular Imaging (SNMMI) Annual Meeting 2017. A. K. Jha, C. Marcus, R. Wray. R. Subramaniam, and E. Frey, “A no-gold-standard framework to evaluate FDG-PET tumor-segmentation methods on the task of measuring prognostic biomarkers for lung-cancer treatment”, Therapy Center Young Investigator Award, […]

Our paper on developing a statistical technique to estimate a single ADC value from a lesion using diffusion MRI was recently published in the journal Magnetic Resonance in Medicine. Especially exciting that the proposed method was rigorous, computationally fast, easy-to-implement, convenient-to-use, and more accurate than state-of-the-art methods in clinical settings! This work was in collaboration with […]