Hossein Jafarzadeh
Ph.D. Student
Biological and Biomedical Engineering
Bio
Hossein’s passion in physics and biology and curiosity in machine learning has brought him to medical physics. He is trying to show that pre-planning in high dose rate brachytherapy is possible, and ultimately reduce the time that the patients are under anesthesia.
Current Projects
Penalty Weight Optimization in High Dose Rate Brachytherapy
Treatment plan optimization problem in high dose rate brachytherapy is formulated as a constrained optimization problem. First the dose constraints and penalty weights are determined by the clinicians, then the optimization problem is solved by linear programing. The dose constraints are usually fixed for each patient depending on the treated tumor site and the treatment planning guidelines followed. However, the clinicians select different penalty weights, leading to different optimization problems and finally adopt the one that results in the most desirable dose distribution. To remove the clinicians influence on plan quality, reinforcement learning is explored.
Catheter Position Optimization in High Dose Rate Brachytherapy
In interstitial high dose rate brachytherapy, a highly radioactive source, usually 192Ir, is temporarily placed inside or in close proximity of the tumor via thin hollow implanted catheters which are connected a machine called an afterloader. The afterloader contains a single radioactive source at the end of a wire. The source is pushed into each of the catheters, one by one under computer control and guided to the tumor site. The computer controls where along the catheter the source should pause to deliver its radiation (dwell positions) and how long it dwells at each position (dwell time). After the desired dose is delivered, the source is pulled back to the afterloader and the catheters are removed.
Since the dwell times are optimized, the position of catheters has a major impact on the treatment plan quality. Efforts in optimizing the catheter positions have not been explored as extensively as the other aspects of the treatment planning workflow. This gap in knowledge motivates us to further explore this problem.
2025
Automatic catheter digitization in breast brachytherapy Journal Article
In: Medical Physics, vol. 52, iss. 9, no. e18107, 2025, ISSN: 2473-4209.
@article{nokey,
title = {Automatic catheter digitization in breast brachytherapy},
author = {Sébastien Quetin and Hossein Jafarzadeh and Jonathan Kalinowski and Hamed Bekerat and Boris Bahoric and Farhad Maleki and Shirin A. Enger},
url = {https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.18107},
doi = {https://doi.org/10.1002/mp.18107},
issn = {2473-4209},
year = {2025},
date = {2025-09-12},
urldate = {2025-09-12},
journal = {Medical Physics},
volume = {52},
number = {e18107},
issue = {9},
abstract = {Background:
High dose rate (HDR) brachytherapy requires clinicians to digitize catheters manually. This process is time-consuming, complex, and depends heavily on clinical experience-especially in breast cancer cases, where catheters may be inserted at varying angles and orientations due to an irregular anatomy.
Purpose:
This study is the first to automate catheter digitization specifically for breast HDR brachytherapy, emphasizing the unique challenges associated with this treatment site. It also introduces a pipeline that automatically digitizes catheters, generates dwell positions, and calculates the delivered dose for new breast cancer patients.
Methods:
Treatment data from 117 breast cancer patients treated with HDR brachytherapy were used. Pseudo-contours for the catheters were created from the treatment digitization points and divided into three classes: catheter body, catheter head, and catheter tip. An nnU-Net pipeline was trained to segment the pseudo-contours on treatment planning computed tomography images of 88 patients (training and validation). Then, pseudo-contours were digitized by separating the catheters into connected components. Predicted catheters with an unusual volume were flagged for manual review. A custom algorithm was designed to report and separate connected components containing colliding catheters. Finally, a spline was fitted to every separated catheter, and the tip was identified on the spline using the tip contour prediction. Dwell positions were placed from the created tip at a regular step size extracted from the DICOM plan file. Distance from each dwell position used during the clinical treatment to the fitted spline (shaft distance) was computed, as well as the distance from the treatment tip to the one identified by our pipeline. Dwell times from the clinical plan were assigned to the nearest generated dwell positions. TG-43 dose in water was computed analytically, and the absorbed dose in the medium was predicted using a published AI-based dose prediction model. Dosimetric comparison between the clinically delivered plan dose and the created automated plan dose was evaluated regarding dosimetric indices percent error.
Results:
Our pipeline was used to digitize 408 catheters on a test set of 29 patients. Shaft distance was on average 0.70 ± 3.91 mm and distance to the tip was on average 1.37 ± 5.25 mm. The dosimetric error between the manual and automated treatment plans was, on average, below 3% for planning target volume V100, V150, V200 and for the lung, heart, skin, and chest wall D2cc and D1cc, in both water and heterogeneous media. For D0.1cc values in all the organs at risk, the average error remained below 5%. The pipeline execution time, including auto-contouring, digitization, and dose to medium prediction, averages 118 s, ranging from 63 to 294 s. The pipeline successfully flagged all cases where digitization was not performed correctly.
Conclusions:
Our pipeline is the first to automate the digitization of catheters for breast brachytherapy, as well as the first to generate dwell positions and predict corresponding AI-based absorbed dose to medium based on automatically digitized catheters. The automatically digitized catheters are in excellent agreement with the manually digitized ones while more accurately reflecting their true anatomical shape.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background:
High dose rate (HDR) brachytherapy requires clinicians to digitize catheters manually. This process is time-consuming, complex, and depends heavily on clinical experience-especially in breast cancer cases, where catheters may be inserted at varying angles and orientations due to an irregular anatomy.
Purpose:
This study is the first to automate catheter digitization specifically for breast HDR brachytherapy, emphasizing the unique challenges associated with this treatment site. It also introduces a pipeline that automatically digitizes catheters, generates dwell positions, and calculates the delivered dose for new breast cancer patients.
Methods:
Treatment data from 117 breast cancer patients treated with HDR brachytherapy were used. Pseudo-contours for the catheters were created from the treatment digitization points and divided into three classes: catheter body, catheter head, and catheter tip. An nnU-Net pipeline was trained to segment the pseudo-contours on treatment planning computed tomography images of 88 patients (training and validation). Then, pseudo-contours were digitized by separating the catheters into connected components. Predicted catheters with an unusual volume were flagged for manual review. A custom algorithm was designed to report and separate connected components containing colliding catheters. Finally, a spline was fitted to every separated catheter, and the tip was identified on the spline using the tip contour prediction. Dwell positions were placed from the created tip at a regular step size extracted from the DICOM plan file. Distance from each dwell position used during the clinical treatment to the fitted spline (shaft distance) was computed, as well as the distance from the treatment tip to the one identified by our pipeline. Dwell times from the clinical plan were assigned to the nearest generated dwell positions. TG-43 dose in water was computed analytically, and the absorbed dose in the medium was predicted using a published AI-based dose prediction model. Dosimetric comparison between the clinically delivered plan dose and the created automated plan dose was evaluated regarding dosimetric indices percent error.
Results:
Our pipeline was used to digitize 408 catheters on a test set of 29 patients. Shaft distance was on average 0.70 ± 3.91 mm and distance to the tip was on average 1.37 ± 5.25 mm. The dosimetric error between the manual and automated treatment plans was, on average, below 3% for planning target volume V100, V150, V200 and for the lung, heart, skin, and chest wall D2cc and D1cc, in both water and heterogeneous media. For D0.1cc values in all the organs at risk, the average error remained below 5%. The pipeline execution time, including auto-contouring, digitization, and dose to medium prediction, averages 118 s, ranging from 63 to 294 s. The pipeline successfully flagged all cases where digitization was not performed correctly.
Conclusions:
Our pipeline is the first to automate the digitization of catheters for breast brachytherapy, as well as the first to generate dwell positions and predict corresponding AI-based absorbed dose to medium based on automatically digitized catheters. The automatically digitized catheters are in excellent agreement with the manually digitized ones while more accurately reflecting their true anatomical shape.
High dose rate (HDR) brachytherapy requires clinicians to digitize catheters manually. This process is time-consuming, complex, and depends heavily on clinical experience-especially in breast cancer cases, where catheters may be inserted at varying angles and orientations due to an irregular anatomy.
Purpose:
This study is the first to automate catheter digitization specifically for breast HDR brachytherapy, emphasizing the unique challenges associated with this treatment site. It also introduces a pipeline that automatically digitizes catheters, generates dwell positions, and calculates the delivered dose for new breast cancer patients.
Methods:
Treatment data from 117 breast cancer patients treated with HDR brachytherapy were used. Pseudo-contours for the catheters were created from the treatment digitization points and divided into three classes: catheter body, catheter head, and catheter tip. An nnU-Net pipeline was trained to segment the pseudo-contours on treatment planning computed tomography images of 88 patients (training and validation). Then, pseudo-contours were digitized by separating the catheters into connected components. Predicted catheters with an unusual volume were flagged for manual review. A custom algorithm was designed to report and separate connected components containing colliding catheters. Finally, a spline was fitted to every separated catheter, and the tip was identified on the spline using the tip contour prediction. Dwell positions were placed from the created tip at a regular step size extracted from the DICOM plan file. Distance from each dwell position used during the clinical treatment to the fitted spline (shaft distance) was computed, as well as the distance from the treatment tip to the one identified by our pipeline. Dwell times from the clinical plan were assigned to the nearest generated dwell positions. TG-43 dose in water was computed analytically, and the absorbed dose in the medium was predicted using a published AI-based dose prediction model. Dosimetric comparison between the clinically delivered plan dose and the created automated plan dose was evaluated regarding dosimetric indices percent error.
Results:
Our pipeline was used to digitize 408 catheters on a test set of 29 patients. Shaft distance was on average 0.70 ± 3.91 mm and distance to the tip was on average 1.37 ± 5.25 mm. The dosimetric error between the manual and automated treatment plans was, on average, below 3% for planning target volume V100, V150, V200 and for the lung, heart, skin, and chest wall D2cc and D1cc, in both water and heterogeneous media. For D0.1cc values in all the organs at risk, the average error remained below 5%. The pipeline execution time, including auto-contouring, digitization, and dose to medium prediction, averages 118 s, ranging from 63 to 294 s. The pipeline successfully flagged all cases where digitization was not performed correctly.
Conclusions:
Our pipeline is the first to automate the digitization of catheters for breast brachytherapy, as well as the first to generate dwell positions and predict corresponding AI-based absorbed dose to medium based on automatically digitized catheters. The automatically digitized catheters are in excellent agreement with the manually digitized ones while more accurately reflecting their true anatomical shape.
A data-driven approach to model spatial dose characteristics for catheter placement of high dose-rate brachytherapy for prostate cancer Journal Article
In: Computers in Biology and Medicine, vol. 190, no. 110020, 2025, ISSN: 1879-0534.
@article{nokey,
title = {A data-driven approach to model spatial dose characteristics for catheter placement of high dose-rate brachytherapy for prostate cancer},
author = {Björn Morén and Hossein Jafarzadeh and Shirin A Enger},
url = {https://www.sciencedirect.com/science/article/pii/S0010482525003713?via%3Dihub},
doi = {https://doi.org/10.1016/j.compbiomed.2025.110020},
issn = {1879-0534},
year = {2025},
date = {2025-05-01},
journal = {Computers in Biology and Medicine},
volume = {190},
number = {110020},
abstract = {Background: High dose rate brachytherapy (HDR BT) is a common treatment modality for cancer. In HDR BT, a radioactive source is placed inside or close to a tumor, aiming to give a high enough dose to the tumor, while sparing nearby healthy tissue and organs at risk. Treatment planning of HDR BT for prostate cancer consists of two types of decisions, placement of catheters, modeled with binary variables, and dwell times, modeled with continuous non-negative variables. Optimal spatial placement of catheters is important for avoiding local recurrence and complications, but such characteristics have not been modeled for the combined treatment planning problem of catheter placement and dwell time optimization.
Method: We propose a data-driven approach using linear regression, mutual information, and random forests to find convex estimates of spatial dose characteristics that correlate well with contiguous volumes receiving a too-high (hot spots) or too-low dose (cold spots). These estimates were incorporated in retrospective treatment plan optimization of 28 prostate cancer patients.
Results: The proposed hot-spot terms reduced the volume receiving twice the prescribed dose by 29% at 14 catheters. Also, the results illustrate the trade-offs between the number of catheters and spatial dose characteristics.
Conclusions: Our study demonstrates that incorporating a term for hot spots in the objective function of the treatment planning model is more effective in reducing hot spots than catheter placements that are not optimized for hot spots.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: High dose rate brachytherapy (HDR BT) is a common treatment modality for cancer. In HDR BT, a radioactive source is placed inside or close to a tumor, aiming to give a high enough dose to the tumor, while sparing nearby healthy tissue and organs at risk. Treatment planning of HDR BT for prostate cancer consists of two types of decisions, placement of catheters, modeled with binary variables, and dwell times, modeled with continuous non-negative variables. Optimal spatial placement of catheters is important for avoiding local recurrence and complications, but such characteristics have not been modeled for the combined treatment planning problem of catheter placement and dwell time optimization.
Method: We propose a data-driven approach using linear regression, mutual information, and random forests to find convex estimates of spatial dose characteristics that correlate well with contiguous volumes receiving a too-high (hot spots) or too-low dose (cold spots). These estimates were incorporated in retrospective treatment plan optimization of 28 prostate cancer patients.
Results: The proposed hot-spot terms reduced the volume receiving twice the prescribed dose by 29% at 14 catheters. Also, the results illustrate the trade-offs between the number of catheters and spatial dose characteristics.
Conclusions: Our study demonstrates that incorporating a term for hot spots in the objective function of the treatment planning model is more effective in reducing hot spots than catheter placements that are not optimized for hot spots.
Method: We propose a data-driven approach using linear regression, mutual information, and random forests to find convex estimates of spatial dose characteristics that correlate well with contiguous volumes receiving a too-high (hot spots) or too-low dose (cold spots). These estimates were incorporated in retrospective treatment plan optimization of 28 prostate cancer patients.
Results: The proposed hot-spot terms reduced the volume receiving twice the prescribed dose by 29% at 14 catheters. Also, the results illustrate the trade-offs between the number of catheters and spatial dose characteristics.
Conclusions: Our study demonstrates that incorporating a term for hot spots in the objective function of the treatment planning model is more effective in reducing hot spots than catheter placements that are not optimized for hot spots.
2024
Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization Journal Article
In: Physics in Medicine & Biology, vol. 69, 2024.
@article{nokey,
title = {Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization},
author = {Hossein Jafarzadeh and Majd Antaki and Ximeng Mao and Marie Duclos and Farhard Maleki and Shirin A Enger },
doi = {10.1088/1361-6560/ad4448},
year = {2024},
date = {2024-05-21},
urldate = {2024-05-21},
journal = {Physics in Medicine & Biology},
volume = {69},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2023
Doctoral Internship Award Miscellaneous
2023, (Graduate and Post Doctoral Studies, McGill University ).
@misc{nokey,
title = {Doctoral Internship Award},
author = {Hossein Jafarzadeh},
year = {2023},
date = {2023-05-20},
urldate = {2023-05-20},
note = {Graduate and Post Doctoral Studies, McGill University
},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
2022
Biological & Biomedical Engineering PhD Recruitment Award award
2022.
@award{nokey,
title = {Biological & Biomedical Engineering PhD Recruitment Award },
author = {Hossein Jafarzadeh },
url = {https://www.mcgill.ca/bbme/programs/funding#BME-Recruitment-Award},
year = {2022},
date = {2022-05-10},
keywords = {},
pubstate = {published},
tppubtype = {award}
}
Bayesian Optimization in Treatment Planning of High Dose Rate Brachytherapy Proceedings Article
In: MEDICAL PHYSICS, pp. E200–E200, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
@inproceedings{jafarzadeh2022bayesian,
title = {Bayesian Optimization in Treatment Planning of High Dose Rate Brachytherapy},
author = { Hossein Jafarzadeh and Ximeng Mao and Shirin A. Enger},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {MEDICAL PHYSICS},
volume = {49},
number = {6},
pages = {E200--E200},
organization = {WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2021
Mitacs Globalink Research Award award
2021.
@award{Thibodeau-Antonacci2021c,
title = {Mitacs Globalink Research Award},
author = {Alana Thibodeau-Antonacci and Hossein Jafarzadeh and Liam Carroll and Luca L. Weishaupt},
url = {https://www.mitacs.ca/en/programs/globalink/globalink-research-award},
year = {2021},
date = {2021-07-01},
urldate = {2021-07-01},
organization = {MITACS},
abstract = {The Mitacs Globalink Research Award (GRA) supports research collaborations between Canada and select partner organizations and eligible countries and regions. It was awarded to Alana Thibodeau-Antonacci, Hossein Jafarzadeh, Liam Carroll and Luca L. Weishaupt.
Under the joint supervision of a home and host professor, successful senior undergraduate students, graduate students, as well as postdoctoral fellows will receive a $6,000 research award to conduct a 12- to 24-week research project in the other country. Awards are offered in partnership with Mitacs’s Canadian academic partners (and, in some cases, with Mitacs’s international partners) and are subject to available funding. },
howpublished = {Mitacs},
keywords = {},
pubstate = {published},
tppubtype = {award}
}
The Mitacs Globalink Research Award (GRA) supports research collaborations between Canada and select partner organizations and eligible countries and regions. It was awarded to Alana Thibodeau-Antonacci, Hossein Jafarzadeh, Liam Carroll and Luca L. Weishaupt.
Under the joint supervision of a home and host professor, successful senior undergraduate students, graduate students, as well as postdoctoral fellows will receive a $6,000 research award to conduct a 12- to 24-week research project in the other country. Awards are offered in partnership with Mitacs’s Canadian academic partners (and, in some cases, with Mitacs’s international partners) and are subject to available funding.
Under the joint supervision of a home and host professor, successful senior undergraduate students, graduate students, as well as postdoctoral fellows will receive a $6,000 research award to conduct a 12- to 24-week research project in the other country. Awards are offered in partnership with Mitacs’s Canadian academic partners (and, in some cases, with Mitacs’s international partners) and are subject to available funding.