MRI-Based Proton Treatment Planning for Base of Skull Tumors

Abstract

Purpose: To introduce a novel, deep-learning method to generate synthetic computed tomography (SCT) scans for proton treatment planning and evaluate its efficacy. Materials and Methods: 50 Patients with base of skull tumors were divided into 2 nonoverlapping training and study cohorts. Computed tomography and magnetic resonance imaging pairs for patients in the training cohort were used for training our novel 3-dimensional generative adversarial network (cycleGAN) algorithm. Upon completion of the training phase, SCT scans for patients in the study cohort were predicted based on their magnetic resonance images only. The SCT scans obtained were compared against the corresponding original planning computed tomography scans as the ground truth, and mean absolute errors (in Hounsfield units [HU]) and normalized cross-correlations were calculated. Proton plans of 45 Gy in 25 fractions with 2 beams per plan were generated for the patients based on their planning computed tomographies and recalculated on SCT scans. Dose-volume histogram endpoints were compared. A γ-index analysis along 3 cardinal planes intercepting at the isocenter was performed. Proton distal range along each beam was calculated. Results: Image quality metrics show agreement between the generated SCT scans and the ground truth with mean absolute error values ranging from 38.65 to 65.12 HU and an average of 54.55 ± 6.81 HU and a normalized cross-correlation average of 0.96 ± 0.01. The dosimetric evaluation showed no statistically significant differences (p > 0.05) within planning target volumes for dose-volume histogram endpoints and other metrics studied, with the exception of the dose covering 95% of the target volume, with a relative difference of 0.47%. The γ-index analysis showed an average passing rate of 98% with a 10% threshold and 2% and 2-mm criteria. Proton ranges of 48 of 50 beams (96%) in this study were within clinical tolerance adopted by 4 institutions. Conclusions: This study shows our method is capable of generating SCT scans with acceptable image quality, dose distribution agreement, and proton distal range compared with the ground truth. Our results set a promising approach for magnetic resonance imaging–based proton treatment planning.

A review on medical imaging synthesis using deep learning and its clinical applications

“…12,25,27,74 MR-only radiation therapy Studies have also evaluated synthetic CT in the context of proton therapy for prostate, liver, and brain cancer. 33,34,75 Unlike photon beams, which exhibit a broad dose distribution, proton beams deposit dose with very high-dose gradient (sharp Bragg peak) at the distal end of the beam. By superimposing proton beams from several angles, this dose distribution characteristic can be exploited to provide highly conformal dose to the target.…”

“…Studies have also evaluated synthetic CT in the context of proton therapy for prostate, liver, and brain cancer 33,34,75 . Unlike photon beams, which exhibit a broad dose distribution, proton beams deposit dose with very high‐dose gradient (sharp Bragg peak) at the distal end of the beam.…”

Dosimetric evaluation of synthetic CT generated with GANs for MRI‐only proton therapy treatment planning of brain tumors

“…Using the newly developed deep learning methods mentioned above could help to achieve higher accuracy while removing any manual operations. In the last year, several groups have started to investigate the application of deep learning for sCT generation, achieving very promising results 24‐27 . In addition, they analyzed the dosimetric accuracy of the generated sCT for single field uniform dose (SFUD) and conventional PTV optimization.…”

Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods

“…Inspired by the rapid expansion of artificial intelligence in both industry and academia in recent years, many research groups have attempted to integrate machine learning-based methods into medical imaging and radiation therapy [55][56][57][58][59]. The common machine learning applications include detection, segmentation, characterization, reconstruction, registration and synthesis [60][61][62][63][64][65][66]. Before the permeation of artificial intelligence into these sub-fields, conventional image processing methods have been developed for decades.…”