In the computational medical imaging lab, we are focused on developing physics, signal processing, and machine-learning based algorithms for image reconstruction and image analysis such that these algorithms yield optimal performance on the underlying clinical tasks. For example, in an image of a patient with cancer symptoms, the task could be detecting the tumor or estimating some tumor property such as volume. Thus, the design of imaging algorithms, should yield the best performance in that task. This task performance can be objectively quantified using signal processing and computational imaging methods. This idea of objective assessment of image quality (OAIQ) for designing medical imaging solutions is the overarching theme of research in our lab. Our research is focused on the imaging modalities of SPECT and PET, even as we collaborate with several groups within and outside Washington University on other imaging modalities.
Following are some current research directions:
- No-gold-standard evaluation: A key challenge in the clinical translation of quantitative imaging methods is the evaluation of these methods with patient data. Such evaluation typically requires the availability of a gold standard, but these are usually unavailable. To address this issue, we are developing no-gold-standard evaluation techniques, that, without the availability of a gold standard or the true quantitative value, are able to rank the different QI methods. This project is supported by an NIH R01 award.
- Photon-processing imaging systems: We are developing methods that use list-mode SPECT data to enable transmission-less attenuation compensation in cardiac SPECT, with the goal of helping reduce dose and costs, improving diagnosis, and increasing patient comfort. This project was supported by the NIBIB Trailblazer award. In another project, we are developing approaches to improve quantitative SPECT using list-mode data.
- Quantitative PET for early prediction of therapy response in cancer: We are developing novel methods to quantify volumetric and radiomic features from PET images and evaluating these features as biomarkers for predicting therapy response in patients with cancer. The work is supported by an NIH R56 award.
- Quantitative SPECT for alpha-particle therapies: We are developing new methods to quantify regional uptake for alpha-particle radiopharmaceutical therapies. A key challenge in this quantification task is the ultra low number of detected counts (20 times lower than conventional SPECT). We are developing approaches that directly estimate uptake from the projection data and skip the reconstruction step. These methods are observed to yield accurate performance on quantification tasks.
- Dopamine transporter (DaT) SPECT to measure severity of Parkinson disease: We are developing new image reconstruction and analysis methods with the goal of developing SPECT-based markers to measure severity of Parkinson disease.