Medical imaging systems acquire data for particular 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 systems and methods should help yield the best performance in the clinical task. This task performance can be objectively quantified using statistical 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 the IRIS Lab. Fig. 1 illustrates how this idea can be used to evaluate two different imaging systems on a particular task.
Fig. 1: A task-specific approach to medical imaging system design
Research in the lab 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 main 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 SPECT 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 is supported by the NIBIB Trailblazer award. In another project, we are developing approaches to improve quantification in 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 projection-domain quantification 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.