B. Gino Fallone
Contact
Professor, Faculty of Medicine & Dentistry - Oncology Dept
- bfallone@ualberta.ca
- Phone
- (780) 432-8750
- Address
-
Cross Cancer Institute
11560 University AvenueEdmonton ABT6G 1Z2
Overview
Area of Study / Keywords
medical physics image-guided RT linac-MR develop
About
see Google Scholar for complete list of publications
Fellow: Canadian College of Physicists in Medicine
Diplomat: American Board of Medical Physics
Diplomat: American Board of Radiology
Professional Physicist: Canadian Association of Physicists
Fellow: American Association of Physicists in Medicine
Fellow: Canadian Organization of Medical Physicists
Knight, Order of Merit, Italy (Cavaliere, Ordine al Merito della Repubblica Italiana)
Research
SEE FOR pUBLICATION LIST
Integration of a Linac with an MRI System for Real-Time Image-Guided Radiotherapy.
Please see the Linac-MR.ca website.
Image-Guided Adaptive Radiotherapy (IGAR)
The main goal of the research program entitled "Image-guided adaptive radiotherapy (IGAR)" in CBIAR, is to achieve the maximum curing potential by precisely delivering a radiation dose distribution that is "sculpted" to the anatomical and biological extent of the tumor while delivering minimal dose to healthy tissue to reduce side effects.
The IGAR program incorporates one of the world's first human Helical TomoTherapy System, a ultra-high human whole body (3 T) MR Imaging and Spectroscopy System, an animal 9.4T MR Imaging and Spectroscopy System, an image-fusion laboratory, and the computer laboratories for the Canadian Computational Cancer Center (C4). The MR systems provide the anatomical, functional, metabolic and biochemical information to identify diseases at an earlier stage, and monitor treatment response immediately after and at intervals after treatment. The resultant information is integrated with PET images within our image fusion laboratories to design advanced adaptive radiation therapy delivered by the experimental TomoTherapy system. The C4 facilities will evaluate the clinical outcome with respect to treatment by imaging follow-up and by correlating with gene profiling information provided of PolyomX. This evaluation will allow the quantification of fundamental cancer processes and its varying treatment, in an effort to develop novel treatment strategies.
We deliver the dose by a Helical TomoTherapy system, a revolutionary treatment device that was funded by the CFI, ASRA and the ACF (Total of about $5M. Conventional modern CT and MR (1.5 T) do not show the full extent of the tumor. To obtain the full biological extent of the tumor and the healthy tissues (functional, biochemical information in addition to the anatomical information), we have received funds from the CFI and ASRIP (about $10 M) to install a ultra-high field MR system. The system comprises a 3 T MRI and MRS system ($4 M) from Philips which is far superior in imaging capabilities to conventional 1.5 T systems. In addition, we were able to acquire a 9.4 T (super high field) animal MRI and MRS system to perform studies in MR molecular imaging. The latter system brings us to the limit of what futuristic MR imaging and spectroscopy can do for cancer detection, and cancer therapy from radiation and from other cancer treatment agents. The acquisition of the latter system has been made possible with the partial financial $660K commitment from the ACF.
The IGAR facilities are unique in the world, and allow the delivery of advanced adaptive radiation treatments, offer functional and metabolic information of cancer, and quantify cancer processes, and make the Cross Cancer Institute the most advanced center in the world for cancer molecular imaging and adaptive radiation therapy.
Digital Electroradiography
The investigation of a high resolution non-contact voltage sensor to obtain latent images from xeroradiographic plates composed of amorphous Selenium (a-Se), resulting in the development of a digital radiographic system for on-line portal imaging. We are presently investigating the performance of a novel portal imaging device composed of metal/a-Se coupled to amorphous Silicon active flat-panels (thin film transistors).
Digital Portal Imaging and Automatic Image Registration and Verification
Our studies in the digital contrast enhancement of portal localization radiographs have resulted in improved visualization of the treatment area and adjacent regions, thus facilitating the task of accurate localization. Automatic registration of portal to reference images have been performed with morphological processing and correlation techniques. We have also implemented techniques to create megavoltage digital reconstructed radiographs (DRRs) of 3-D CT data to produce CT simulation of patient treatment and subsequent automatic registration to portal images. We are presently investigating the automatic registration of portal to DRRs in three dimensions for on-line verification of radiation treatment.
Inverse Treatment Planning
State-of-the art conformal therapy is a technique that achieves increased survival rate by using intensity modulated beams to deliver dose distributions that tailor a high-dose region to the tumor volume while the dose to the healthy tissue as low as possible. Optimizing techniques have developed to design the modulated beams through a process of inverse treatment planning (ITP). We have develop a novel ITP technique and now plan to incorporate clinically relevant objectives and constraints, and to incorporate emerging radiobiological information to design plans that maximizes tumor control with minimum complications.
Conformal Therapy
We are pursuing Image-Guided Adaptive Radiotherapy (IGAR) techniques that use advanced imaging modalities, conventional and ultra-high field MR, PET, CT, megavoltage CT, to deliver highly conformal therapy beams using a unique Helical TomoTherapy system within the Center for Biological Imaging and Adaptive Radiotherapy (CBIAR). Research in this avenue involves megavoltage CT, detector design and analysis, inverse planning, Monte Carlo simulation and dosimetry, radiobiological modeling, etc. Within the newly-created Canadian Computational Cancer Center, we provide through a computational and theoretical approach, the systematic simplification, quantification and visualization of cancer processes and conventional treatments into practical models that can be developed to significantly increase cure rates.
Courses
ONCOL 562 - Theory of Medical Imaging
A system theory approach to the production, analysis, processing and reconstruction of medical images. An extensive use of Fourier techniques is used to describe the processes involved with conventional radiographic detectors, digital and computed radiography. Review and application of image processing techniques used in diagnostic and therapeutic medicine. Consent of Department required.
Featured Publications
Time Domain Principal Component Analysis for Rapid, Real-Time MRI Reconstruction from Undersampled Data
Proceed. International Society for Magnetic Resonance in Medicine. 2022 May;
Yang F, Ghosh S, Yee D, Patel S, Pervez N, Parliament M, Usmani N, Danielson B, Amanie J, Pearcey R, Field C, Fallone G, Murtha A
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS. 2022 January; 114 (1):99-107 10.1016/j.ijrobp.2022.04.045
K. Purich. H. Cai, Z. Xu, A,G, Tessier, A. Black, R. Hung, E. Brown, B. Xu, P. Wu, B. Zhang, D. Xin, B.G. Fallone, R. V. Rajotte, Y. Wu, G.R. Rayat
XENOTRANSPLANTATION. 2021 November; 29 (1):e12720 10.1111/xen.12720
Mark Wright, Bryson Dietz, Eugene Yip, Jihyun Yun, Zsolt Gabos, B. Gino Fallone, Keith Wachowicz
MEDICAL PHYSICS. 2021 November; 48 (11):4523-4532 10.1002/mp.15238
M H. Wang, L. J. Vos, D. Yee, Samir Patel, Nadeem Pervez, Matthew Parliament, Nawaid Usmani, Brita Danielson, John Amanie, Robert Pearcey, Sunita Ghosh, Colin Field, Gino Fallone and Albert D. Murtha
Practical Radiation Oncology. 2021 September; 11 (5):384-393 10.1016/j.prro.2021.02.011
Deep learning-based auto-contouring algorithm for tumour tracking using a Linac-MR,"
medical physics. 2021 August;
Kiaran P. McGee, Neelam Tyagi, John E. Bayouth, Minsong Cao, B. Gino Fallone, Carri K. Glide-Hurst, Frank L. Goerner, Olga L. Green, Taeho Kim, Eric S. Paulson, Nathan E. Yanasak, Edward F. Jackson, James H. Goodwin, Sonja Dieterich, David W. Jordan, Geoffrey D. Hugo, Matt A. Bernstein, James M. Balter, Kalpana M. Kanal, John D. Hazle, Norbert J. Pel
MEDICAL PHYSICS. 2021 August; 48 (8):4523-4531 10.1002/mp.14996
Optimizing the Accuracy and Training Speed of An Artificial Neural Network for Three-Dimensional Tumor Trajectory Prediction in Linac-MR Image-Guided Radiation Therap
Medical Physics. 2021 August;
Optimizingthe accuracy and training speedof an artificial neural networks for 3-Dtumour trajectorypredictions in linac-MR image-gi radiation therapy
medical physics. 2021 July;
Barta R., Volotovskyy V., Wachowicz K., Fallone B.G., De Zanche N.
Magnetic Resonance in Medicine. 2021 April; 85 (4):2327-2333 10.1002/mrm.28540