4.4 Review

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review

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Article Radiology, Nuclear Medicine & Medical Imaging

Performance of deep learning synthetic CTs for MR-only brain radiation therapy

Xiaoning Liu et al.

Summary: The study evaluated the dosimetric and image-guided radiation therapy (IGRT) performance of a novel generative adversarial network (GAN) generated synthetic CT (synCT) in brain cancer patients. The results demonstrated excellent agreement in dose distributions and registration accuracy between synCT and simCT, suggesting the potential clinical utility of GAN synCTs in high precision brain cancer therapy.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2021)

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IPEM topical report: guidance on the use of MRI for external beam radiotherapy treatment planning*

Richard Speight et al.

Summary: This document provides guidance for establishing and using an MRI-guided radiotherapy treatment planning service, based on the experience of institutions represented in the IPEM working group. It focuses on the use of MRI for external beam RT treatment planning within a CT-based workflow, and provides practical advice on training, patient set-up, MRI sequence optimization, and commissioning and QA for MR scanners. Not covered in this document are other uses of MRI for RT, such as treatment response assessment and MRI-only RT.

PHYSICS IN MEDICINE AND BIOLOGY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Dosimetric evaluation of synthetic CT image generated using a neural network for MR-only brain radiotherapy

Bin Tang et al.

Summary: A neural network was developed to generate sCT in the brain area quickly, achieving high accuracy in dosimetry calculations.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2021)

Review Radiology, Nuclear Medicine & Medical Imaging

MRI-guided Radiation Therapy: An Emerging Paradigm in Adaptive Radiation Oncology

Ricardo Otazo et al.

Summary: MRI-guided RT has become an important tool in cancer treatment, offering superior soft-tissue contrast and the ability to monitor tissue physiologic changes. Institutions are already using offline MRI for treatment planning, and MRI-guided linear accelerator systems allow the use of MRI during treatment, providing better adaptation to anatomic changes. Challenges in advancing MRI-guided RT include real-time volumetric anatomic imaging, image distortion due to magnetic field inhomogeneities, reproducible quantitative imaging, and biological validation of quantitative imaging.

RADIOLOGY (2021)

Article Oncology

Multicentre, deep learning, synthetic-CT generation for ano-rectal MR-only radiotherapy treatment planning

David Bird et al.

Summary: This study assesses a deep learning model for comprehensive dosimetric analysis of a large anorectal cancer cohort, demonstrating that a cGAN model using T2-SPACE MR sequences from multiple centres can produce accurate sCTs. Dose differences and gamma indices indicate high dosimetric accuracy of sCT, with factors such as modality, cancer site, sex, and centre showing clinically insignificant impact (effect ranges: -0.4% to 0.3%).

RADIOTHERAPY AND ONCOLOGY (2021)

Article Oncology

Dosimetric Validation of a GAN-Based Pseudo-CT Generation for MRI-Only Stereotactic Brain Radiotherapy

Vincent Bourbonne et al.

Summary: Stereotactic radiotherapy (SRT) is widely accepted for treating patients with a small number of brain metastases. An MRI-only workflow can reduce planning delays and misalignment risks. Generating synthetic CT scans using a GAN shows high similarity to actual CT scans for SRT planning.

CANCERS (2021)

Article Oncology

Clinical validation of a commercially available deep learning software for synthetic CT generation for brain

Minna Lerner et al.

Summary: The study validated the feasibility of using a commercially available software based on CNN algorithm to generate sCT images, showing comparable results in dosimetric and geometric evaluation with CT images, and is suitable for radiotherapy treatment planning of brain tumors.

RADIATION ONCOLOGY (2021)

Article Oncology

Synthetic computed tomography data allows for accurate absorbed dose calculations in a magnetic resonance imaging only workflow for head and neck radiotherapy

Emilia Palmer et al.

Summary: The study evaluated the accuracy of absorbed dose calculations for head and neck synthetic computed tomography data generated by a commercial convolutional neural network-based algorithm. Results showed that the absorbed doses were statistically equivalent to CT-based radiotherapy, demonstrating the potential of the algorithm for MRI-only treatment planning.

PHYSICS & IMAGING IN RADIATION ONCOLOGY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep learning-based MR-to-CT synthesis: The influence of varying gradient echo-based MR images as input channels

Mateusz C. Florkow et al.

MAGNETIC RESONANCE IN MEDICINE (2020)

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On the accuracy of bulk synthetic CT for MR-guided online adaptive radiotherapy

Davide Cusumano et al.

RADIOLOGIA MEDICA (2020)

Article Engineering, Biomedical

MR to CT synthesis with multicenter data in the pelvic area using a conditional generative adversarial network

Kevin N. D. Brou Boni et al.

PHYSICS IN MEDICINE AND BIOLOGY (2020)

Article Computer Science, Interdisciplinary Applications

Missing MRI Pulse Sequence Synthesis Using Multi-Modal Generative Adversarial Network

Anmol Sharma et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

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

Samaneh Kazemifar et al.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2020)

Review Oncology

Medical physics challenges in clinical MR-guided radiotherapy

Christopher Kurz et al.

RADIATION ONCOLOGY (2020)

Article Engineering, Biomedical

Abdominal synthetic CT generation from MR Dixon images using a U-net trained with 'semi-synthetic' CT data

Lianli Liu et al.

PHYSICS IN MEDICINE AND BIOLOGY (2020)

Review Computer Science, Artificial Intelligence

GANs for medical image analysis

Salome Kazeminia et al.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

A new deep convolutional neural network design with efficient learning capability: Application to CT image synthesis from MRI

Abass Bahrami et al.

MEDICAL PHYSICS (2020)

Article Engineering, Biomedical

Comparison of deep learning synthesis of synthetic CTs using clinical MRI inputs

Haley A. Massa et al.

PHYSICS IN MEDICINE AND BIOLOGY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy

Jie Fu et al.

BIOMEDICAL PHYSICS & ENGINEERING EXPRESS (2020)

Review Engineering, Biomedical

An introduction to deep learning in medical physics: advantages, potential, and challenges

Chenyang Shen et al.

PHYSICS IN MEDICINE AND BIOLOGY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

MR-based treatment planning in radiation therapy using a deep learning approach

Fang Liu et al.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Comparison of prostate delineation on multimodality imaging for MR-guided radiotherapy

Angela U. Pathmanathan et al.

BRITISH JOURNAL OF RADIOLOGY (2019)

Review Radiology, Nuclear Medicine & Medical Imaging

Applications and limitations of machine learning in radiation oncology

Daniel Jarrett et al.

BRITISH JOURNAL OF RADIOLOGY (2019)

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MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks

Yang Lei et al.

MEDICAL PHYSICS (2019)

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Deep Convolution Neural Network (DCNN) Multiplane Approach to Synthetic CT Generation From MR images-Application in Brain Proton Therapy

Maria Francesca Spadea et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR-only breast radiotherapy

Sven Olberg et al.

MEDICAL PHYSICS (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic CT generation method

Yingzi Liu et al.

BRITISH JOURNAL OF RADIOLOGY (2019)

Article Oncology

Comparison of Deep Learning-Based and Patch-Based Methods for Pseudo-CT Generation in MRI-Based Prostate Dose Planning

Axel Largent et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2019)

Review Oncology

Deep Learning: A Review for the Radiation Oncologist

Luca Boldrini et al.

FRONTIERS IN ONCOLOGY (2019)

Review Radiology, Nuclear Medicine & Medical Imaging

Deep learning in medical imaging and radiation therapy

Berkman Sahiner et al.

MEDICAL PHYSICS (2019)

Review Computer Science, Artificial Intelligence

Generative adversarial network in medical imaging: A review

Xin Yi et al.

MEDICAL IMAGE ANALYSIS (2019)

Article Oncology

MRI-Based Proton Treatment Planning for Base of Skull Tumors

Ghazal Shafai-Erfani et al.

INTERNATIONAL JOURNAL OF PARTICLE THERAPY (2019)

Proceedings Paper Engineering, Biomedical

Pseudo-CT image generation from mDixon MRI images using fully convolutional neural networks

J. Stadelmann et al.

MEDICAL IMAGING 2019: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING (2019)

Proceedings Paper Engineering, Biomedical

The impact of MRI-CT registration errors on deep learning-based synthetic CT generation

Mateusz C. Florkow et al.

MEDICAL IMAGING 2019: IMAGE PROCESSING (2019)

Review Oncology

The rationale for MR-only treatment planning for external radiotherapy

Joakim Jonsson et al.

CLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY (2019)

Article Biology

Survey on deep learning for radiotherapy

Philippe Meyer et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2018)

Review Oncology

Systematic Review of Synthetic Computed Tomography Generation Methodologies for Use in Magnetic Resonance Imaging-Only Radiation Therapy

Emily Johnstone et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2018)

Article Oncology

MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network

Anna M. Dinkla et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2018)

Article Computer Science, Artificial Intelligence

Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image

Lei Xiang et al.

MEDICAL IMAGE ANALYSIS (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

MR and CT data with multiobserver delineations of organs in the pelvic areaPart of the Gold Atlas project

Tufve Nyholm et al.

MEDICAL PHYSICS (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

Generating synthetic CTs from magnetic resonance images using generative adversarial networks

Hajar Emami et al.

MEDICAL PHYSICS (2018)

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Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Reid F. Thompson et al.

RADIOTHERAPY AND ONCOLOGY (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

Experimental evaluation of the impact of low tesla transverse magnetic field on dose distribution in presence of tissue interfaces

Davide Cusumano et al.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2018)

Proceedings Paper Computer Science, Theory & Methods

Unpaired Brain MR-to-CT Synthesis Using a Structure-Constrained CycleGAN

Heran Yang et al.

DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT, DLMIA 2018 (2018)

Review Engineering, Biomedical

MRI-only treatment planning: benefits and challenges

Amir M. Owrangi et al.

PHYSICS IN MEDICINE AND BIOLOGY (2018)

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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Vijay Badrinarayanan et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)

Article Computer Science, Artificial Intelligence

A survey on deep learning in medical image analysis

Geert Litjens et al.

MEDICAL IMAGE ANALYSIS (2017)

Article Radiology, Nuclear Medicine & Medical Imaging

MR-based synthetic CT generation using a deep convolutional neural network method

Xiao Han

MEDICAL PHYSICS (2017)

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Challenges in calculation of the gamma index in radiotherapy - Towards good practice

M. Hussein et al.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2017)

Review Oncology

A review of substitute CT generation for MRI-only radiation therapy

Jens M. Edmund et al.

RADIATION ONCOLOGY (2017)

Proceedings Paper Computer Science, Interdisciplinary Applications

Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease

Jelmer M. Wolterink et al.

RECONSTRUCTION, SEGMENTATION, AND ANALYSIS OF MEDICAL IMAGES (2017)

Proceedings Paper Optics

Pseudo CT Estimation from MRI Using Patch-based Random Forest

Xiaofeng Yang et al.

MEDICAL IMAGING 2017: IMAGE PROCESSING (2017)

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Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model

Tri Huynh et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)

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MR image-based synthetic CT for IMRT prostate treatment planning and CBCT image-guided localization

Shupeng Chen et al.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2016)

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Deep learning

Yann LeCun et al.

NATURE (2015)

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MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration

Mattias P. Heinrich et al.

MEDICAL IMAGE ANALYSIS (2012)

Article Computer Science, Interdisciplinary Applications

N4ITK: Improved N3 Bias Correction

Nicholas J. Tustison et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2010)

Article Engineering, Biomedical

Experimental verification of magnetic field dose effects for the MRI-accelerator

A. J. E. Raaijmakers et al.

PHYSICS IN MEDICINE AND BIOLOGY (2007)

Article Computer Science, Artificial Intelligence

Image quality assessment: From error visibility to structural similarity

Z Wang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2004)

Article Computer Science, Interdisciplinary Applications

New variants of a method of MRI scale standardization

LG Nyúl et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2000)