Reducing MRI-guided radiotherapy planning and delivery times via efficient leaf sequencing and segment shape optimization algorithms

Jeffrey E Snyder; Joël St-Aubin; Sridhar Yaddanapudi; Spencer Marshall; Sarah Strand; Stanley Kruger; Ryan Flynn; Daniel E Hyer

doi:10.1088/1361-6560/ac5299

Back

Reducing MRI-guided radiotherapy planning and delivery times via efficient leaf sequencing and segment shape optimization algorithms

Journal article

Open access

Peer reviewed

Reducing MRI-guided radiotherapy planning and delivery times via efficient leaf sequencing and segment shape optimization algorithms

Jeffrey E Snyder, Joël St-Aubin, Sridhar Yaddanapudi, Spencer Marshall, Sarah Strand, Stanley Kruger, Ryan Flynn and Daniel E Hyer

Physics in medicine & biology, Vol.67(5), p.55005

02/24/2022

DOI: 10.1088/1361-6560/ac5299

PMID: 35130528

Files and links (1)

url

https://doi.org/10.1088/1361-6560/ac5299View

Published (Version of record) Open Access

Abstract

Extended treatment session times are an operational limitation in magnetic resonance imaging guided adaptive radiotherapy (MRIgRT). In this study a novel leaf sequencing algorithm called optimal fluence levels (OFL) and an optimization algorithm called pseudo gradient descent (PGD) are evaluated with respect to plan quality, beam complexity, and the ability to reduce treatment session times on the Elekta Unity MRIgRT system. Ten total patients were evaluated on this Institutional Review Board approved study: three with prostate cancer, three with oligometastases, two with pancreatic cancer, and two with liver cancer. Plans were generated using the clinical Monaco Hyperion optimizer and leaf sequencer and then re-optimized using OFL and PGD (OFL + PGD) while holding all IMRT constraints and planning parameters constant. All plans were normalized to ensure 95% of the PTV received the prescription dose. A paired t-test was used to evaluate statistical significance. Plan quality in terms of dosimetric OAR sparing was found to be equivalent between the OFL + PGD and conventional Monaco Hyperion optimizer plans. The OFL + PGD plans had a reduction in optimization time of 51.4% ± 5.0% ( = 0.002) and reduction in treatment delivery time of 10.6% ± 7.5% ( = 0.005). OFL + PGD generated plans had on average 13.2% ± 12.6% fewer multi-leaf collimator (MLC) segments ( = 0.009) and 0.1 ± 0.1 lower plan averaged beam modulation (PM) ( = 0.004) relative to the Monaco Hyperion plans. The OFL + PGD algorithms more quickly generate Unity treatment plans that are faster to deliver than with the conventional approach and without compromising dosimetric plan quality. This is likely due to a delivery complexity reduction enabled by OFL + PGD relative to the Monaco Hyperion plans.

Algorithms

Magnetic Resonance Imaging

Humans

Liver Neoplasms

Male

Prostatic Neoplasms

Radiotherapy, Image-Guided

Details

Title: Subtitle: Reducing MRI-guided radiotherapy planning and delivery times via efficient leaf sequencing and segment shape optimization algorithms
Creators: Jeffrey E Snyder - University of Iowa Hospitals and Clinics
Joël St-Aubin - University of Iowa
Sridhar Yaddanapudi - University of Iowa
Spencer Marshall - Elekta
Sarah Strand - University of Iowa Hospitals and Clinics
Stanley Kruger - University of Iowa
Ryan Flynn - University of Iowa Hospitals and Clinics
Daniel E Hyer - University of Iowa
Resource Type: Journal article
Publication Details: Physics in medicine & biology, Vol.67(5), p.55005
DOI: 10.1088/1361-6560/ac5299
PMID: 35130528
NLM abbreviation: Phys Med Biol
ISSN: 0031-9155
eISSN: 1361-6560
Grant note: DOI: 10.13039/501100002669, name: Elekta
Language: English
Date published: 02/24/2022
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Radiation Oncology
Record Identifier: 9984313074502771

Metrics

9 Record Views

6 Times Cited - Web of Science