1. Spinal Nerve Tolerance in Single-Fraction SBRT (Pig Model)<\/p>\n
In Collaboration with Prof. Medin et al. including Hassan\u2011Rezaeian (2019) studied spinal nerve responses to single-dose stereotactic radiation in Yucatan mini-pigs. Their finding on ED\u2085\u2080 (dose for 50% paresis) \u2248\u202f19\u202fGy for a 2.5\u202fcm segment\u2014matching tolerance of spinal cord in the same model. Indicated that peripheral spinal nerves share a similar acute dose-response curve to central spinal cord structures.<\/p>\n
Their work highlights the sensitivity of spinal nerves against delivering >19\u202fGy over short segments in SBRT.<\/p>\n
2. Brachial Plexus Tolerance in Single-Fraction SBRT (Pig Model)<\/p>\n
In Collaboration with Prof. Medin et al. including Hassan\u2011Rezaeian (2022) evaluated brachial plexus (BP) response to focal SBRT in pigs, Their finding on ED\u2085\u2080 was ~19.3\u202fGy for a 2.5\u202fcm BP segment\u2014comparable to spinal nerve ED\u2085\u2080. They found neurological deficits appeared 5\u20138 weeks post-treatment, with histopathological confirmation of radiation-induced neuropathy and no deficits noted at maximum doses \u226417.6\u202fGy. Their work highlights Brachial plexus tolerance to high-dose radiation is not notably higher than spinal nerve tolerance, and doses above ~17\u202fGy per session should be used with caution in stereotactic protocols.<\/p>\n
Their systematic review\/meta-analysis of 37 human cohorts concluded that each additional Gy above bpD\u2098\u2090\u2093 correlates with an 11% increased risk of radiation-induced plexopathy (RR\u202f=\u202f1.11 per Gy)<\/p>\n
Created deep learning models predicting VMAT dose for prostate cancer (trained on 108 cases), and used transfer learning with a minimal number of external cases to achieve Dice similarity improvements from ~0.8\u20130.9 to ~0.9\u20130.96\u2014improving DVH predictions to within 1.6%. His approach enhances generalizability of DL dose-prediction models across different clinical planning styles for prostate VMAT. The primary model using 108 prostate VMAT cases, then fine-tuned to four different planning styles (three internal, one external) using only 14\u201329 new cases each. They observed without adaptation the DSC of 0.81\u20130.94 (internal), 0.82\u20130.91 (external). However, after adaptation DSC improved to 0.88\u20130.95 (internal), 0.92\u20130.96 (external); DVH errors within 1.6%. This work shows with minimal retraining data (~20 cases), a DL model can be tailored to varying planning protocols\u2014enabling scalable automation for VMAT dose planning.<\/p>\n
Auto planning and QA for HDR Treatment<\/p>\n
He developed techniques and tools for HDR intra-op planning and QA using ESAPI to improve workflow and safety for both HDR Prostate and GYN,<\/p>\n
Acuros-based Dose Modeling,<\/p>\n
He evaluated the model-based Acuros algorithm for intraosseous high-dose-rate (HDR) brachytherapy in bone metastases, offering more accurate dosimetry in complex anatomical regions<\/p>\n
EM-Tracking<\/p>\n
Integrated and evaluated electromagnetic tracking for catheter reconstruction, during HDR prostate implant to improve intra-op HDR brachy planning.<\/p>\n
His work address intra-fraction motion during VMAT by implementing image-based tracking. They designed, developed, and evaluated systems for intra-fractional tumor motion tracking and real time dose reconstruction, particularly in high risk prostate SBRT. Their system uses fast MC engine, ~150\u202fms per MC calculation, fast enough for intra-fraction monitoring, this enables live quality assurance and error detection during delivery.<\/p>\n
Developed tools like projection marker matching (PM\u00b3) and Monte Carlo GPU\/OpenCL dose engines for clinical implementation in dose tracking for prostate SBRT treatment. PM\u00b3 (Prostate Motion Monitoring & Mitigation) leverages 2D\/3D registration to detect motion mid-treatment and reduce dose errors. This enhances precision of prostate targeting in long-delivery treatments.<\/p>\n","protected":false},"excerpt":{"rendered":"
Nima Hassan Rezaeian is a medical physicist who served as an Assistant Professor in the Department of Radiation Oncology at Assistant Professor, Geisel School of Medicine in Hanover, with an academic affiliation to Adjunct Assistant Professor, Thayer School of Engineering at Dartmouth College. He completed a Ph.D. in Physics (University of North Texas, 2015) and has a strong background in atomic and nuclear physics, evidenced by early research in precision measurements and laser spectroscopy. This foundational expertise complements his later work in clinical medical physics.<\/p>\n
His specialization in medical physics, particularly in advanced cancer treatment techniques. His research focus includes tumor motion management and tracking during radiotherapy and high-dose-rate brachytherapy for prostate cancer. He applies cutting-edge approaches like image-guided adaptive radiotherapy \u2013 for example, contributing to studies on MR-guided adaptive treatment for prostate cancer. In addition, his listed interests span from practical medical physics domains to fundamental science, covering Medical Physics, Motion Management, Atomic Physics, Precision Measurements, and Laser Spectroscopy. This indicates a blend of technical expertise: from developing clinical radiotherapy solutions to conducting high-precision experimental physics.<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-10","page","type-page","status-publish","hentry","author-2"],"_links":{"self":[{"href":"https:\/\/geiselmed.dartmouth.edu\/rezaeian\/wp-json\/wp\/v2\/pages\/10","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/geiselmed.dartmouth.edu\/rezaeian\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/geiselmed.dartmouth.edu\/rezaeian\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/geiselmed.dartmouth.edu\/rezaeian\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/geiselmed.dartmouth.edu\/rezaeian\/wp-json\/wp\/v2\/comments?post=10"}],"version-history":[{"count":1,"href":"https:\/\/geiselmed.dartmouth.edu\/rezaeian\/wp-json\/wp\/v2\/pages\/10\/revisions"}],"predecessor-version":[{"id":11,"href":"https:\/\/geiselmed.dartmouth.edu\/rezaeian\/wp-json\/wp\/v2\/pages\/10\/revisions\/11"}],"wp:attachment":[{"href":"https:\/\/geiselmed.dartmouth.edu\/rezaeian\/wp-json\/wp\/v2\/media?parent=10"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}