Image quality and dose evaluation of MVCT TomoTherapy acquisitions: A phantom study

Marco, Paolo De; Osman, Iman; Castellini, Fabio; Ricotti, R.; Leonardi, M.C.; Miglietta, E.; Cambria, Raffaella; Origgi, Daniela; Jereczek-Fossa, B.A.; Garibaldi, Cristina; Cattani, Federica

doi:10.1016/j.ejmp.2019.01.009

Cited by 12 publications

(11 citation statements)

References 23 publications

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“…Los tipos de imágenes de IGRT que podemos utilizar para adaptar un tratamiento de radioterapia son las de carácter volumétrico, tanto con energías de kilovoltaje, 9,22 como de megavoltage. [23][24][25] Las soluciones comerciales que permiten este tipo de adquisición en los diferentes equipos de radioterapia son:…”

Section: Modalidadesunclassified

Implementación y uso clínico de la radioterapia adaptativa. Informe del grupo de trabajo de radioterapia adaptativa de la Sociedad Española de Física Médica (SEFM)

García-Mollá¹,

Rubio

Alandí

et al. 2021

Rev Fis Med

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La radioterapia adaptativa (Adaptive Radiation Therapy, ART) se puede definir como la modificación del plan de tratamiento administrado a un paciente durante el curso de la radioterapia para tener en cuenta los cambios en la anatomía. La aplicación de ART supone un cambio en el proceso radioterápico que implica el uso de numerosos recursos adicionales, no estando implementada de forma general. Así, aun tratándose de una idea introducida hace más de 20 años, todavía a fecha de hoy son objeto de estudio muchos de los aspectos que definen la ART: la decisión de proceder a la nueva planificación del tratamiento, el momento óptimo para adaptar el plan, el umbral dosimétrico o anatómico que define la necesidad de adaptar y la identificación de los pacientes que realmente se beneficiarían de la adaptación del tratamiento. Este informe del Grupo de Trabajo sobre ART de la Sociedad Española de Física Médica (SEFM) tiene como objetivo describir los principios de esta técnica, así como los elementos necesarios para su implementación. Se exponen las dos estrategias principales atendiendo al momento de su aplicación: offline, para actuar ante los cambios progresivos que se observan durante el tratamiento, y online, para mitigar los cambios aleatorios. Se revisa el estado actual de ART aplicado a la práctica clínica para diferentes localizaciones anatómicas. Se describen algunos de los instrumentos clave para su implementación: los algoritmos de registro deformable (Deformable Image Registration, DIR), exponiendo las principales métricas de similitud, los métodos de optimización de la similitud, y los modelos de deformación utilizados, analizando las características de los algoritmos de los softwares más utilizados. Además, dada la importancia que tiene conocer la incertidumbre adicional que introduce el uso de los algoritmos DIR (sobre todo cuando se utilizan para deformar la matriz de dosis absorbida) se dedica un apartado a la validación de estos algoritmos, incluyendo los resultados para los principales softwares comerciales disponibles actualmente. Por último, se presentan unas recomendaciones a la hora de aplicar ART en la práctica clínica.

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Section: Modalidadesunclassified

Implementación y uso clínico de la radioterapia adaptativa. Informe del grupo de trabajo de radioterapia adaptativa de la Sociedad Española de Física Médica (SEFM)

García-Mollá¹,

Rubio

Alandí

et al. 2021

Rev Fis Med

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“…16,17 Megavoltage (MV) images mitigate many of the disadvantages exhibited by kV images and have been used to for both image guidance 18 and for generating electron density maps for dose calculation [19][20][21][22] and adaptive radiotherapy planning. [23][24][25][26] By utilizing the same high-energy x-ray beam for both imaging and treatment, the MV imaging isocenter coincides perfectly with the treatment isocenter while also evading problems related to streaking artifacts and beam hardening due to its higher photon energy spectrum. Several commercially available options for on-board volumetric MV imaging include the megavoltage computed tomography (MVCT) platform integrated into the TomoTherapy system (Accuray, Sunnyvale, CA) 27,28 and the MV-CBCT imaging platform integrated into the Halcyon system (Varian Medical Systems, Palo Alto, CA).…”

Section: Introductionmentioning

confidence: 98%

“…Additionally, kV imaging can be susceptible to artifacts caused by metallic material such as dental fillings or prosthetics 16,17 . Megavoltage (MV) images mitigate many of the disadvantages exhibited by kV images and have been used to for both image guidance 18 and for generating electron density maps for dose calculation 19–22 and adaptive radiotherapy planning 23–26 . By utilizing the same high‐energy x‐ray beam for both imaging and treatment, the MV imaging isocenter coincides perfectly with the treatment isocenter while also evading problems related to streaking artifacts and beam hardening due to its higher photon energy spectrum.…”

Section: Introductionmentioning

confidence: 99%

Generation of synthetic megavoltage CT for MRI‐only radiotherapy treatment planning using a 3D deep convolutional neural network

et al. 2022

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Background: Megavoltage computed tomography (MVCT) has been implemented on many radiotherapy treatment machines for on-board anatomical visualization, localization, and adaptive dose calculation. Implementing an MRonly workflow by synthesizing MVCT from magnetic resonance imaging (MRI) would offer numerous advantages for treatment planning and online adaptation. Purpose: In this work, we sought to synthesize MVCT (sMVCT) datasets from MRI using deep learning to demonstrate the feasibility of MRI-MVCT only treatment planning. Methods: MVCTs and T1-weighted MRIs for 120 patients treated for head-andneck cancer were retrospectively acquired and co-registered. A deep neural network based on a fully-convolutional 3D U-Net architecture was implemented to map MRI intensity to MVCT HU. Input to the model were volumetric patches generated from paired MRI and MVCT datasets. The U-Net was initialized with random parameters and trained on a mean absolute error (MAE) objective function. Model accuracy was evaluated on 18 withheld test exams. sMVCTs were compared to respective MVCTs. Intensity-modulated volumetric radiotherapy (IMRT) plans were generated on MVCTs of four different disease sites and compared to plans calculated onto corresponding sMVCTs using the gamma metric and dose-volume-histograms (DVHs). Results: MAE values between sMVCT and MVCT datasets were 93.3 ± 27.5, 78.2 ± 27.5, and 138.0 ± 43.4 HU for whole body, soft tissue, and bone volumes, respectively. Overall, there was good agreement between sMVCT and MVCT, with bone and air posing the greatest challenges. The retrospective dataset introduced additional deviations due to sinus filling or tumor growth/shrinkage between scans, differences in external contours due to variability in patient positioning,or when immobilization devices were absent from diagnostic MRIs.Dose distributions of IMRT plans evaluated for four test cases showed close agreement between sMVCT and MVCT images when evaluated using DVHs and gamma dose metrics, which averaged to 98.9 ± 1.0% and 96.8 ± 2.6% analyzed at 3%/3 mm and 2%/2 mm, respectively. Conclusions: MVCT datasets can be generated from T1-weighted MRI using a 3D deep convolutional neural network with dose calculation on a sample sMVCT in close agreement with the MVCT. These results demonstrate the feasibility of using MRI-derived sMVCT in an MR-only treatment planning workflow.

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“…MVCT images have a precise linear relationship between CT values and electron density (CT‐ED) 4,9,17 . Therefore, it is highly desirable to perform dose calculations based on the CT‐ED relationship 18,19 . However, the MVCT image quality is poor, making it challenging to delineate tumor targets and organs at risk (OARs).…”

Section: Introductionmentioning

confidence: 99%

“…4,9,17 Therefore, it is highly desirable to perform dose calculations based on the CT-ED relationship. 18,19 However, the MVCT image quality is poor, making it challenging to delineate tumor targets and organs at risk (OARs). The indirect method employs deformable image regis-tration (DIR) to align the planning kVCT (pCT) to MVCT and generate a deformable pCT, which can be used for ART.…”

Section: Introductionmentioning

confidence: 99%

A qualitative study of improving megavoltage computed tomography image quality and maintaining dose accuracy using cycleGAN‐based image synthesis

Xie

Zhang

et al. 2023

Medical Physics

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BackgroundDue to inconsistent positioning, tumor shrinking, and weight loss during fractionated treatment, the initial plan was no longer appropriate after a few fractional treatments, and the patient will require adaptive helical tomotherapy (HT) to overcome the issue. Patients are scanned with megavoltage computed tomography (MVCT) before each fractional treatment, which is utilized for patient setup and provides information for dose reconstruction. However, the low contrast and high noise of MVCT make it challenging to delineate treatment targets and organs at risk (OAR).PurposeThis study developed a deep‐learning‐based approach to generate high‐quality synthetic kilovoltage computed tomography (skVCT) from MVCT and meet clinical dose requirements.MethodsData from 41 head and neck cancer patients were collected; 25 (2995 slices) were used for training, and 16 (1898 slices) for testing. A cycle generative adversarial network (cycleGAN) based on attention gate and residual blocks was used to generate MVCT‐based skVCT. For the 16 patients, kVCT‐based plans were transferred to skVCT images and electron density profile‐corrected MVCT images to recalculate the dose. The quantitative indices and clinically relevant dosimetric metrics, including the mean absolute error (MAE), structural similarity index measure (SSIM), peak signal‐to‐noise ratio (PSNR), gamma passing rates, and dose‐volume‐histogram (DVH) parameters (Dmax, Dmean, Dmin), were used to assess the skVCT images.ResultsThe MAE, PSNR, and SSIM of MVCT were 109.6 ± 12.3 HU, 27.5 ± 1.1 dB, and 91.9% ± 1.7%, respectively, while those of skVCT were 60.6 ± 9.0 HU, 34.0 ± 1.9 dB, and 96.5% ± 1.1%. The image quality and contrast were enhanced, and the noise was reduced. The gamma passing rates improved from 98.31% ± 1.11% to 99.71% ± 0.20% (2 mm/2%) and 99.77% ± 0.18% to 99.98% ± 0.02% (3 mm/3%). No significant differences (p > 0.05) were observed in DVH parameters between kVCT and skVCT.ConclusionWith training on a small data set (2995 slices), the model successfully generated skVCT with improved image quality, and the dose calculation accuracy was similar to that of MVCT. MVCT‐based skVCT can increase treatment accuracy and offer the possibility of implementing adaptive radiotherapy.

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Image quality and dose evaluation of MVCT TomoTherapy acquisitions: A phantom study

Cited by 12 publications

References 23 publications

Implementación y uso clínico de la radioterapia adaptativa. Informe del grupo de trabajo de radioterapia adaptativa de la Sociedad Española de Física Médica (SEFM)

Implementación y uso clínico de la radioterapia adaptativa. Informe del grupo de trabajo de radioterapia adaptativa de la Sociedad Española de Física Médica (SEFM)

Generation of synthetic megavoltage CT for MRI‐only radiotherapy treatment planning using a 3D deep convolutional neural network

A qualitative study of improving megavoltage computed tomography image quality and maintaining dose accuracy using cycleGAN‐based image synthesis

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