We are pleased to announce being awarded a CIHR project grant. Our proposal (funded for 4 years; $631,124) is entitled, “Quantitative PSMA Targeted Imaging of Prostate Cancer Patients”. We aim to improve assessment of disease for prostate cancer patients in the context of our ongoing clinical trials involving prostate-specific membrane antigen (PSMA) radioligand therapy (also known as radiopharmaceutical therapy). We will pursue advanced PSMA PET data acquisition (particularly dynamic whole-body imaging), as well as improved image reconstruction and enhancement. Our efforts will also involve automated deep-learning based segmentation of PET images, as well as predictive modeling of prostate cancer using radiomics and machine learning methods.
Author: Quantitative Tomography Lab
Eight works by our team and collaborators (2 oral and 6 posters) were recently presented at the 2018 IEEE Medical Imaging Conference in Sydney, November 14-17:
- M. R. Salmanpour, M. Shamsaee, A. Saberi Manesh, S. Setayeshi, E. Taherinezhad, I. S. Klyuzhin, J. Tang, V. Sossi, and A. Rahmim
Machine learning methods for optimal prediction of outcome in Parkinson’s disease
- K. H. Leung, M. R. Salmanpour, A. S. Manesh, I. S. Klyuzhin, V. Sossi, A. K. Jha, M. G. Pomper, Y. Du, and A. Rahmim
Using deep-learning to predict outcome of patients with Parkinson’s disease
- Y. Gao, H. Zhang, Y. Zhu, M. Bilgel, O. Rousset, S. Resnick, D. F. Wong, L. Lu, and A. Rahmim
Voxel-based partial volume correction of amyloid PET images incorporating non-local means regularization
- I. Shiri, H. Maleki, G. Hajianfar, H. Abdollahi, S. Ashrafinia, M. Ghelich Oghli, M. Oveisi, and A. Rahmim
PET/CT radiomic sequencer for prediction of EGFR and KRAS mutation status in NSCLC patients
- M. P. Adams, B. Yang, A. Rahmim, and J. Tang
Prediction of outcome in Parkinson’s disease patients from DAT SPECT images using a convolutional neural network
- J. -C. Cheng, C. W. J. Bevington, A. Rahmim, I. S. Klyuzhin, J. Matthews, R. Boellaard, V. and Sossi
Dynamic PET reconstruction utilizing a spatiotemporal 4D de-noising kernel
- H. Li, L. Lu, S. Cao, J. Gong, Q. Feng, A. Rahmim, and W. Chen
Dual-modality joint reconstruction of PET-MRI incorporating a cross-guided prior
- M. A. Lodge, J. Sunderland, and A. Rahmim
About measurement of PET spatial resolution
“It is well to remember that the entire universe, with one trifling exception, is composed of others.” (John Andrew Holmes)
Dr. Anna Celler is the recipient of the Gold medal this year by the Canadian Organization of Medical Physicists (COMP), which is being celebrated tomorrow (September 14) in Montreal. Anna taught the very first course I (Arman) took in the field of nuclear medicine. She has had 27 years of tireless efforts, having mentored many students in her Medical Imaging Research Group, and made contributions on multiple frontiers in nuclear medicine. What makes Anna especially stand out in my mind is how much she cares about the well-being of the students she works with, and how she has fought through the years to establish a legacy of research and service at our institutions. Anna retired recently, though thankfully visiting on a regular basis. I hope that we can carry forward a part of Anna’s legacy, and I’m grateful that she will continue to work with us and advise us as we set a path forward. Thank you Anna!
In his outstanding book “The War of Art”, Steven Pressfield says the following about the artist, which is applicable to anyone who seeks to produce a work of value:
“For the artist to define himself hierarchically is fatal. Let’s examine why. First, let’s look at what happens in a hierarchical orientation. An individual who defines himself by his place in a pecking order will:
1) Compete against all others in the order, seeking to elevate his station by advancing against those above him, while defending his place against those beneath.
2) Evaluate his happiness/success/achievement by his rank within the hierarchy, feeling most satisfied when he’s high and most miserable when he’s low.
3) Act toward others based upon their rank in the hierarchy, to the exclusion of all other factors.
4) Evaluate his every move solely by the effect it produces on others. He will act for others, dress for others, speak for others, think for others.
In the hierarchy, the artist faces outward. Meeting someone new he asks himself, What can this person do for me? How can this person advance my standing?
In the hierarchy, the artist looks up and looks down. The one place he can’t look is that place he must: within.”
Thirteen accepted works by our group and collaborators (8 oral and 5 posters) are being presented at the 2018 Annual Meeting of the Society of Nuclear Medicine & Molecular Imaging (SNMMI) in Philadelphia June 23-26:
- A. Rahmim, K. P. Bak-Fredslund, S. Ashrafinia, C. R. Schmidtlein, R. M. Subramaniam, A. Morsing, S. Keiding, J. Horsager, and O. L. Munk
Quantification of colorectal liver metastases using FDG PET volumetric and heterogeneity features for improved prediction of clinical outcome
- A. Rahmim, S. Ashrafinia, S. Rowe, C. R. Schmidtlein, M. H. Vendelbo, T. El-Galaly, L. C. Gormsen, and O. L. Munk
Quantification of lymphoma using FDG PET heterogeneity features for improved prediction of clinical outcome
- S. Ashrafinia, P. Dalaie, R. Yan, P. Ghazi, C. Marcus, M. Taghipour, P. Huang, M. G. Pomper, T. Schindler, and A. Rahmim
Radiomics analysis of clinical myocardial perfusion SPECT to predict coronary artery calcification
- S. Ashrafinia, P. Dalaie, R. Yan, P. Huang, Martin G. Pomper, T. Schindler, and A. Rahmim
Application of texture and radiomics analysis to clinical myocardial perfusion SPECT imaging
- H. Leung, W. Marashdeh, S. Ashrafinia, A. Rahmim, M. G. Pomper, and A. K. Jha
A deep-learning-based fully automated segmentation approach to delineate tumors in FDG PET images of lung cancer patients
- S. Klyuzhin, N. Shenkov, A. Rahmim, and V. Sossi
Use of deep convolutional neural networks to predict Parkinson’s disease progression from DaTscan SPECT images
- D. Du, W. Lv, Q. Yuan, Q. Wang, Q. Feng, W. Chen, A. Rahmim, and L. Lu
Machine learning methods for optimal differentiation of recurrence versus inflammation from post-therapy nasopharyngeal 18F-FDG PET/CT images
- X. Hong, W. Lv, Q. Yuan, Q. Wang, Q. Feng, W. Chen, A. Rahmim, and L. Lu
Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal 18F-FDG PET/CT images
- Y. Gao, M. Bilgel, S. Ashrafinia, Lijun Lu, Olivier Rousset, Susan Resnick, Dean F. Wong, Arman Rahmim
Evaluation of non-local methods with and without anatomy information for improved quantitative amyloid PET imaging
- A. Rahmim, M. A. Lodge, N. A. Karakatsanis, V. Y. Panin, Y. Zhou, A. McMillan, S. Cho, H. Zaidi, M. E. Casey, R. L. Wahl
Dynamic whole-body PET imaging: principles, potentials and applications
- W. Lv, Q. Yuan, Q. Wang, J. Ma, Q. Feng, W. Chen, A. Rahmim, and L. Lu
Prognostic potentials of radiomics analysis on the PET and CT components of PET/CT complementary to clinical parameters in patients with nasopharyngeal carcinoma
- L. Lu, P. Wang, J. Ma, Q. Feng, A. Rahmim, and W. Chen
Generalized factor analysis incorporating alpha-divergence and kinetics-based clustering: application to dynamic myocardial perfusion PET imaging
- Y. Li, A. Rahmim, and L. Lu
Direct Bayesian parametric image reconstruction from dynamic myocardial perfusion PET data
After 13 years at Johns Hopkins University, our lab is moving to Vancouver in July! Our lab will be jointly affiliated with the Departments of Radiology and Physics & Astronomy at the University of British Columbia (UBC), as well as the BC Cancer Agency (BCCA). We look forward to playing an active role in concerted and integrated efforts to improve research, education and clinical practice at UBC and BCCA. Hopkins, as an amazing institute and community, has been extremely gracious to us, to which we’re indebted, and we surely hope and plan to pursue significant collaborations and joint efforts.