Tomi Nano, PhD
|
Title(s) | Medical Physics Resident, Radiation Oncology |
---|
School | School of Medicine |
---|
Address | 1825 4th Street San Francisco CA 94107
|
---|
Phone | -- |
---|
vCard | Download vCard |
---|
|
|
Biography University of Windsor, Windsor, ON, Canada | B.Ss. (Honors) | 05/2014 | Physics | Western University, London, ON, Canada | M.Cl.Sc. | 01/2017 | Medical Physics (CAMPEP) | Western University, London, ON, Canada | Ph.D. | 06/2019 | Medical Biophysics |
Western University | 2015
-
| 2017 | Natural Sciences and Engineering Research Council (NSERC) CGS-M | Western University | 2016
-
| 2018 | Translation Breast Cancer Research Traineeship (TBCRU) | Western University | 2018
-
| 2019 | Natural Sciences and Engineering Research Council (NSERC) PGS-D |
Overview Artificial intelligence (AI) in medicine, such as machine or deep learning, has evolved drastically in recent years. It is being applied to a broad spectrum of problems, however, there are few AI algorithms that have transitioned to clinic and disseminated into practice. Translation of AI algorithms that use medical imaging requires understanding of fundamental imaging physics. Imaging data has some degree of noise, and AI algorithms have some degree of inaccuracy. By understanding the relationship between imaging and statistical models, we can improve how to implement AI algorithms in clinics to provide more accurate predictions.
Dr. Nano's academic and research training has equipped me with a strong understanding of imaging fundamentals that include physics of detectors, image reconstruction, image processing and feature detection in a variety of imaging modalities. Outcomes from Dr. Nano's work are being used in industry and translated to clinics. Dr. Nano's research work has resulted in 7 peer reviewed manuscripts (to journals such as Medical Physics), 2 book chapters, and more than 20 conference presentation. During residency, Dr. Nano will use expertise in medical imaging and computer science to develop a mathematical framework that address challenges of translatability, interpretability and robustness of machine and deep learning predictions in radiation oncology. The overarching goal is to improve patient outcomes and reduce side effects of radiation treatment.
Dr. Nano's clinical responsibility is physics support of radiotherapy. During his residency training, he has contributed to installation, commissioning, maintenance and quality assurance of equipment and procedures used in radiation treatment planning and delivery. Highlights of Dr. Nano's clinical development work at UCSF include: 1) implementing quality assurance reporting of treatments with motion management, and 2) evaluation of inter- and intra- fraction motion during SBRT treatments.
Bibliographic
Altmetrics Details
PMC Citations indicate the number of times the publication was cited by articles in PubMed Central, and the Altmetric score represents citations in news articles and social media.
(Note that publications are often cited in additional ways that are not shown here.)
Fields are based on how the National Library of Medicine (NLM) classifies the publication's journal and might not represent the specific topic of the publication.
Translation tags are based on the publication type and the MeSH terms NLM assigns to the publication.
Some publications (especially newer ones and publications not in PubMed) might not yet be assigned Field or Translation tags.)
Click a Field or Translation tag to filter the publications.
-
Stereotactic body radiotherapy and high-dose rate brachytherapy boost in combination with intensity modulated radiotherapy for localized prostate cancer: a single-institution propensity score matched analysis. Int J Radiat Oncol Biol Phys. 2020 Dec 29.
Chen WC, Li Y, Lazar A, Altan A, Descovich M, Nano T, Ziemer B, Sudhyadhom A, Cunha A, Thomas H, Gottschalk A, Hsu IC, Roach M. PMID: 33385496.
View in: PubMed Mentions: Fields:
-
Technical Note: Performance of CyberKnife® tracking using low-dose CT and kV imaging. Med Phys. 2020 Oct 16.
Nano TF, Capaldi DPI, Yeung T, Chuang CF, Wang L, Descovich M. PMID: 33064863.
View in: PubMed Mentions: Fields:
-
Technical Note: Evaluation of audiovisual biofeedback smartphone application for respiratory monitoring in radiation oncology. Med Phys. 2020 Nov; 47(11):5496-5504.
PMID: 32969075.
View in: PubMed Mentions: Fields:
-
MTF and DQE enhancement using an apodized-aperture x-ray detector design. Med Phys. 2017 Sep; 44(9):4525-4535.
Nano TF, Escartin T, Ismailova E, Karim KS, Lindström J, Kim HK, Cunningham IA. PMID: 28636792.
View in: PubMed Mentions: 1 Fields: Translation: HumansAnimals
This graph shows the total number of publications by year. To see the data as text, click here.
This graph shows the total number of publications by year. To return to the graph, click here.
Year | Publications |
---|
2017 | 1 | 2020 | 3 |
This graph shows the number and percent of publications by field.
Fields are based on how the National Library of Medicine (NLM) classifies the publications' journals and might not represent the specific topics of the publications.
Note that an individual publication can be assigned to more than one field. As a result, the publication counts in this graph might add up to more than the number of publications the person has written.
To see the data as text, click here.
This graph shows the number and percent of publications by field.
Fields are based on how the National Library of Medicine (NLM) classifies the publications' journals and might not represent the specific topics of the publications.
Note that an individual publication can be assigned to more than one field. As a result, the publication counts in this graph might add up to more than the number of publications the person has written.
To see the data as text, click here.
newest
oldest
line numbers
double spacing
all authors
publication IDs
|
Derived automatically from this person's publications.
_
People in Profiles who have published with this person.
_
People who share similar concepts with this person.
_
Search Department
_
|