Ran Klein profile picture

Contact Information

Ran Klein, PhD
613-761-4072
rklein@toh.on.ca

https://orcid.org/0000-0002-4357-3779

Research Activities

Current projects:

Myocardial blood flow quantification with 82Rb PET

At the University of Ottawa Heart Institute, National Cardiac PET Centre we have been developing PET for routine quantification of myocardial blood flow (MBF) and myocardial flow reserve (MFR). These efforts have resulted in the development and commercialization of the following technologies:

Our current efforts are focused on improving precision of MBF and MFR by tackling problems such as respiratory and patient motion correction (Collaborator: R. deKemp, T. Xu, A Alessio| Student: S Manwell, C Hunter)

 

Lesion synthesis in FDG PET and CT

We have developed state-of-the-art methods for synthesizing realistic lesions in both PET and CT data. We are currently applying these methods to generate large libraries of images with well characterized fake lesions for the following goals:

  • Characterize the limits of detection (LOD), including lesion contrast, lesion size, image noise and anatomical region, of human-observers under fixed imaging conditions such as PET image reconstruction method using synthetic lesions.
  • Optimization of PET image reconstruction and visualization for lesion detection on the merits of LOD.
  • Developing machine-learning based lesion detection artificial intelligence (AI) using very large libraries of synthetic lesions.
  • Objectively comparing the performance of clinicians and AI for the task of lesion detection with regards to both success rates and LOD.

(Collaborators: C. Collin, E. Krupinski, General Electric Health Care PET R&D team, Student: H Gabrani-Juma, MASc Candidate, Biomedical Engineering, Carleton University)

 

FDG PET-CT Image segmentaion and lesion detection

Using analytical methods and machine-learning we are developing artifical intelligence (AI) for computer aided diagnosis (CAD) of FDG PET-CT studies. In collaboration with Hermes Medical Solutions we are enabling collaboraion between human and machine observers using Hybrid3DTM to improve diagnostic accuracy, automatically generate templated clinical reports, and to provide active learning feedback to our AI.

(Student: O Salman, PhD Candidate Systems and Computer Engineering, Carleton University)

 

Recent Projects

Two-compartment exchange phantom

With Catherine Coolens and Shelley Medical Solutions we have developed and validated a two-compartment exchange phantom for multimodiality imaging. The DCE perfusion from phantom phantom can be used for validation of quantitative dynamic imaging including PET, SPECT, CT and MRI for modality development, qualification testing in clinical trials, and quality control.

 

(Collaborators: C. Coolens | Student: H Gabrani-Juma | Industry partner: Shelley Medical Imaging Technologies)

 

Quantitative SUV SPECT

Quantitative SPECT that can generate SUV scaled images has become routinely avaialable, but its clinical utility is not yet clear. Since 2015 we have been increasingly applying SUV SPECT imaging in routine clinical practice to enable research of its clinical utility. To date we have produced numerous publication and abstracts using SUV SPECT and parathyroid adenomas, thyroid imaging and tumour imaging.

(Collaborators: L. Zuckier, W. Zeng | Industry partner: Hermes Medical Solutions)