Clinical validation of a commercially available deep learning software for synthetic CT generation for brain
Lerner, Minna LU ; Medin, Joakim LU ; Jamtheim Gustafsson, Christian LU ; Alkner, Sara LU ; Siversson, Carl LU and Olsson, Lars E. LU
(2021)
In Radiation Oncology
16(1).
- Abstract
Background: Most studies on synthetic computed tomography (sCT) generation for brain rely on in-house developed methods. They often focus on performance rather than clinical feasibility. Therefore, the aim of this work was to validate sCT images generated using a commercially available software, based on a convolutional neural network (CNN) algorithm, to enable MRI-only treatment planning for the brain in a clinical setting. Methods: This prospective study included 20 patients with brain malignancies of which 14 had areas of resected skull bone due to surgery. A Dixon magnetic resonance (MR) acquisition sequence for sCT generation was added to the clinical brain MR-protocol. The corresponding sCT images were provided by the software MRI... (More)
Background: Most studies on synthetic computed tomography (sCT) generation for brain rely on in-house developed methods. They often focus on performance rather than clinical feasibility. Therefore, the aim of this work was to validate sCT images generated using a commercially available software, based on a convolutional neural network (CNN) algorithm, to enable MRI-only treatment planning for the brain in a clinical setting. Methods: This prospective study included 20 patients with brain malignancies of which 14 had areas of resected skull bone due to surgery. A Dixon magnetic resonance (MR) acquisition sequence for sCT generation was added to the clinical brain MR-protocol. The corresponding sCT images were provided by the software MRI Planner (Spectronic Medical AB, Sweden). sCT images were rigidly registered and resampled to CT for each patient. Treatment plans were optimized on CT and recalculated on sCT images for evaluation of dosimetric and geometric endpoints. Further analysis was also performed for the post-surgical cases. Clinical robustness in patient setup verification was assessed by rigidly registering cone beam CT (CBCT) to sCT and CT images, respectively. Results: All sCT images were successfully generated. Areas of bone resection due to surgery were accurately depicted. Mean absolute error of the sCT images within the body contour for all patients was 62.2 ± 4.1 HU. Average absorbed dose differences were below 0.2% for parameters evaluated for both targets and organs at risk. Mean pass rate of global gamma (1%/1 mm) for all patients was 100.0 ± 0.0% within PTV and 99.1 ± 0.6% for the full dose distribution. No clinically relevant deviations were found in the CBCT-sCT vs CBCT-CT image registrations. In addition, mean values of voxel-wise patient specific geometric distortion in the Dixon images for sCT generation were below 0.1 mm for soft tissue, and below 0.2 mm for air and bone. Conclusions: This work successfully validated a commercially available CNN-based software for sCT generation. Results were comparable for sCT and CT images in both dosimetric and geometric evaluation, for both patients with and without anatomical anomalies. Thus, MRI Planner is feasible to use for radiotherapy treatment planning of brain tumours.
(Less)
- author
- Lerner, Minna
LU
; Medin, Joakim
LU
; Jamtheim Gustafsson, Christian
LU
; Alkner, Sara
LU
; Siversson, Carl
LU
and Olsson, Lars E.
LU
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Brain, Brain metastases, Glioma, MRI-only, Radiotherapy, Synthetic CT
- in
- Radiation Oncology
- volume
- 16
- issue
- 1
- article number
- 66
- publisher
- BioMed Central (BMC)
- external identifiers
-
- scopus:85103995135
- pmid:33827619
- ISSN
- 1748-717X
- DOI
- 10.1186/s13014-021-01794-6
- language
- English
- LU publication?
- yes
- id
- b304d0fb-cc92-4f33-8089-3a9a9230b450
- date added to LUP
- 2021-04-20 09:30:31
- date last changed
- 2024-06-15 10:04:08
@article{b304d0fb-cc92-4f33-8089-3a9a9230b450,
abstract = {{<p>Background: Most studies on synthetic computed tomography (sCT) generation for brain rely on in-house developed methods. They often focus on performance rather than clinical feasibility. Therefore, the aim of this work was to validate sCT images generated using a commercially available software, based on a convolutional neural network (CNN) algorithm, to enable MRI-only treatment planning for the brain in a clinical setting. Methods: This prospective study included 20 patients with brain malignancies of which 14 had areas of resected skull bone due to surgery. A Dixon magnetic resonance (MR) acquisition sequence for sCT generation was added to the clinical brain MR-protocol. The corresponding sCT images were provided by the software MRI Planner (Spectronic Medical AB, Sweden). sCT images were rigidly registered and resampled to CT for each patient. Treatment plans were optimized on CT and recalculated on sCT images for evaluation of dosimetric and geometric endpoints. Further analysis was also performed for the post-surgical cases. Clinical robustness in patient setup verification was assessed by rigidly registering cone beam CT (CBCT) to sCT and CT images, respectively. Results: All sCT images were successfully generated. Areas of bone resection due to surgery were accurately depicted. Mean absolute error of the sCT images within the body contour for all patients was 62.2 ± 4.1 HU. Average absorbed dose differences were below 0.2% for parameters evaluated for both targets and organs at risk. Mean pass rate of global gamma (1%/1 mm) for all patients was 100.0 ± 0.0% within PTV and 99.1 ± 0.6% for the full dose distribution. No clinically relevant deviations were found in the CBCT-sCT vs CBCT-CT image registrations. In addition, mean values of voxel-wise patient specific geometric distortion in the Dixon images for sCT generation were below 0.1 mm for soft tissue, and below 0.2 mm for air and bone. Conclusions: This work successfully validated a commercially available CNN-based software for sCT generation. Results were comparable for sCT and CT images in both dosimetric and geometric evaluation, for both patients with and without anatomical anomalies. Thus, MRI Planner is feasible to use for radiotherapy treatment planning of brain tumours.</p>}},
author = {{Lerner, Minna and Medin, Joakim and Jamtheim Gustafsson, Christian and Alkner, Sara and Siversson, Carl and Olsson, Lars E.}},
issn = {{1748-717X}},
keywords = {{Brain; Brain metastases; Glioma; MRI-only; Radiotherapy; Synthetic CT}},
language = {{eng}},
number = {{1}},
publisher = {{BioMed Central (BMC)}},
series = {{Radiation Oncology}},
title = {{Clinical validation of a commercially available deep learning software for synthetic CT generation for brain}},
url = {{http://dx.doi.org/10.1186/s13014-021-01794-6}},
doi = {{10.1186/s13014-021-01794-6}},
volume = {{16}},
year = {{2021}},
}