Deep Boosted Regression for MR to CT Synthesis

Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification. However, attenuation correction is particularly challenging for PET-MRI as neither PET nor magnetic resonance imaging (MRI) can directly image tissue attenuation properties. MRI-based computed tomography (CT) synthesis has been proposed as an alternative to physics based and segmentation-based approaches that assign a population-based tissue density value in order to generate an attenuation map. We propose a novel deep fully convolutional neural network that generates synthetic CTs in a recursive manner by gradually reducing the residuals of the previous network, increasing the overall accuracy and generalisability, while keeping the number of trainable parameters within reasonable limits. The model is trained on a database of 20 pre-acquired MRI/CT pairs and a four-fold random bootstrapped validation with a 80:20 split is performed. Quantitative results show that the proposed framework outperforms a state-of-the-art atlas-based approach decreasing the Mean Absolute Error (MAE) from 131HU to 68HU for the synthetic CTs and reducing the PET reconstruction error from 14.3% to 7.2%.Comment: Accepted at SASHIMI201

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio

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

PURPOSE: To evaluate the dosimetric and image-guided radiation therapy (IGRT) performance of a novel generative adversarial network (GAN) generated synthetic CT (synCT) in the brain and compare its performance for clinical use including conventional brain radiotherapy, cranial stereotactic radiosurgery (SRS), planar, and volumetric IGRT. METHODS AND MATERIALS: SynCT images for 12 brain cancer patients (6 SRS, 6 conventional) were generated from T1-weighted postgadolinium magnetic resonance (MR) images by applying a GAN model with a residual network (ResNet) generator and a convolutional neural network (CNN) with 5 convolutional layers as the discriminator that classified input images as real or synthetic. Following rigid registration, clinical structures and treatment plans derived from simulation CT (simCT) images were transferred to synCTs. Dose was recalculated for 15 simCT/synCT plan pairs using fixed monitor units. Two-dimensional (2D) gamma analysis (2%/2 mm, 1%/1 mm) was performed to compare dose distributions at isocenter. Dose-volume histogram (DVH) metrics (D(95%) , D(99%) , D(0.2cc,) and D(0.035cc) ) were assessed for the targets and organ at risks (OARs). IGRT performance was evaluated via volumetric registration between cone beam CT (CBCT) to synCT/simCT and planar registration between KV images to synCT/simCT digital reconstructed radiographs (DRRs). RESULTS: Average gamma passing rates at 1%/1mm and 2%/2mm were 99.0 ± 1.5% and 99.9 ± 0.2%, respectively. Excellent agreement in DVH metrics was observed (mean difference ≤0.10 ± 0.04 Gy for targets, 0.13 ± 0.04 Gy for OARs). The population averaged mean difference in CBCT-synCT registrations were \u3c0.2 mm and 0.1 degree different from simCT-based registrations. The mean difference between kV-synCT DRR and kV-simCT DRR registrations was \u3c0.5 mm with no statistically significant differences observed (P \u3e 0.05). An outlier with a large resection cavity exhibited the worst-case scenario. CONCLUSION: Brain GAN synCTs demonstrated excellent performance for dosimetric and IGRT endpoints, offering potential use in high precision brain cancer therapy

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)

Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network

In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. We propose a loss function that favors a spatially sparse region in the image. We harness the ability of a multi-task network to produce correlated outputs as a framework to enable localisation of region of interest (RoI) via classification, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task. We demonstrate how the multi-task network with RoI focused loss offers an advantage over other configurations of the network to achieve higher accuracy of performance. This is relevant to sCT where failure to accurately estimate high Hounsfield Unit values of bone could lead to impaired accuracy in clinical applications. We compare the dose calculation maps from the proposed sCT and the real CT in a radiation therapy treatment planning setup

PET/MRI 및 MR-IGRT를 위한 MRI 기반 합성 CT 생성의 타당성 연구

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의과학과, 2020. 8. 이재성.Over the past decade, the application of magnetic resonance imaging (MRI) in the field of diagnosis and treatment has increased. MRI provides higher soft-tissue contrast, especially in the brain, abdominal organ, and bone marrow without the expose of ionizing radiation. Hence, simultaneous positron emission tomography/MR (PET/MR) system and MR-image guided radiation therapy (MR-IGRT) system has recently been emerged and currently available for clinical study. One major issue in PET/MR system is attenuation correction from MRI scans for PET quantification and a similar need for the assignment of electron densities to MRI scans for dose calculation can be found in MR-IGRT system. Because the MR signals are related to the proton density and relaxation properties of tissue, not to electron density. To overcome this problem, the method called synthetic CT (sCT), a pseudo CT derived from MR images, has been proposed. In this thesis, studies on generating synthetic CT and investigating the feasibility of using a MR-based synthetic CT for diagnostic and radiotherapy application were presented. Firstly, MR image-based attenuation correction (MR-AC) method using level-set segmentation for brain PET/MRI was developed. To resolve conventional inaccuracy MR-AC problem, we proposed an improved ultrashort echo time MR-AC method that was based on a multiphase level-set algorithm with main magnetic field inhomogeneity correction. We also assessed the feasibility of level-set based MR-AC method, compared with CT-AC and MR-AC provided by the manufacturer of the PET/MRI scanner. Secondly, we proposed sCT generation from the low field MR images using 2D convolution neural network model for MR-IGRT system. This sCT images were compared to the deformed CT generated using the deformable registration being used in the current system. We assessed the feasibility of using sCT for radiation treatment planning from each of the patients with pelvic, thoraic and abdominal region through geometric and dosimetric evaluation.지난 10년간 진단 및 치료분야에서 자기공명영상(Magnetic resonance imaging; MRI) 의 적용이 증가하였다. MRI는 CT와 비교해 추가적인 전리방사선의 피폭없이 뇌, 복부 기관 및 골수 등에서 더 높은 연조직 대비를 제공한다. 따라서 MRI를 적용한 양전자방출단층촬영(Positron emission tomography; PET)/MR 시스템과 MR 영상 유도 방사선 치료 시스템(MR-image guided radiation therapy; MR-IGRT)이 진단 및 치료 방사선분야에 등장하여 임상에 사용되고 있다. PET/MR 시스템의 한 가지 주요 문제는 PET 정량화를 위한 MRI 스캔으로부터의 감쇠 보정이며, MR-IGRT 시스템에서 선량 계산을 위해 MR 영상에 전자 밀도를 할당하는 것과 비슷한 필요성을 찾을 수 있다. 이는 MR 신호가 전자 밀도가 아닌 조직의 양성자 밀도 및 T1, T2 이완 특성과 관련이 있기 때문이다. 이 문제를 극복하기 위해, MR 이미지로부터 유래된 가상의 CT인 합성 CT라 불리는 방법이 제안되었다. 본 학위논문에서는 합성 CT 생성 방법 및 진단 및 방사선 치료에 적용을 위한 MR 영상 기반 합성 CT 사용의 임상적 타당성을 조사하였다. 첫째로, 뇌 PET/MR를 위한 레벨셋 분할을 이용한 MR 이미지 기반 감쇠 보정 방법을 개발하였다. MR 이미지 기반 감쇠 보정의 부정확성은 정량화 오류와 뇌 PET/MRI 연구에서 병변의 잘못된 판독으로 이어진다. 이 문제를 해결하기 위해, 자기장 불균일 보정을 포함한 다상 레벨셋 알고리즘에 기초한 개선된 초단파 에코 시간 MR-AC 방법을 제안하였다. 또한 CT-AC 및 PET/MRI 스캐너 제조업체가 제공한 MR-AC와 비교하여 레벨셋 기반 MR-AC 방법의 임상적 사용가능성을 평가하였다. 둘째로, MR-IGRT 시스템을 위한 심층 컨볼루션 신경망 모델을 사용하여 저필드 MR 이미지에서 생성된 합성 CT 방법를 제안하였다. 이 합성 CT 이미지를 변형 정합을 사용하여 생성된 변형 CT와 비교 하였다. 또한 골반, 흉부 및 복부 환자에서의 기하학적, 선량적 분석을 통해 방사선 치료계획에서의 합성 CT를 사용가능성을 평가하였다.Chapter 1. Introduction 1 1.1. Background 1 1.1.1. The Integration of MRI into Other Medical Devices 1 1.1.2. Chanllenges in the MRI Integrated System 4 1.1.3. Synthetic CT Generation 5 1.2. Purpose of Research 6 Chapter 2. MRI-based Attenuation Correction for PET/MRI 8 2.1. Background 8 2.2. Materials and Methods 10 2.2.1. Brain PET Dataset 19 2.2.2. MR-Based Attenuation Map using Level-Set Algorithm 12 2.2.3. Image Processing and Reconstruction 18 2.3. Results 20 2.4. Discussion 28 Chapter 3. MRI-based synthetic CT generation for MR-IGRT 30 3.1. Background 30 3.2. Materials and Methods 32 3.2.1. MR-dCT Paired DataSet 32 3.2.2. Synthetic CT Generation using 2D CNN 36 3.2.3. Data Analysis 38 3.3. Results 41 3.3.1. Image Comparison 41 3.3.2. Geometric Analysis 49 3.3.3. Dosimetric Analysis 49 3.4. Discussion 56 Chapter 4. Conclusions 59 Bibliography 60 Abstract in Korean (국문 초록) 64Docto

A generative adversarial network approach to synthetic-CT creation for MRI-based radiation therapy

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia), Universidade de Lisboa, Faculdade de Ciências, 2019This project presents the application of a generative adversarial network (GAN) to the creation of synthetic computed tomography (sCT) scans from volumetric T1-weighted magnetic resonance imaging (MRI), for dose calculation in MRI-based radio therapy workflows. A 3-dimensional GAN for MRI-to-CT synthesis was developed based on a 2-dimensional architecture for image-content transfer. Co-registered CT and T1 –weighted MRI scans of the head region were used for training. Tuning of the network was performed with a 7-foldcross-validation method on 42 patients. A second data set of 12 patients was used as the hold out data set for final validation. The performance of the GAN was assessed with image quality metrics, and dosimetric evaluation was performed for 33 patients by comparing dose distributions calculated on true and synthetic CT, for photon and proton therapy plans. sCT generation time was <30s per patient. The mean absolute error (MAE) between sCT and CT on the cross-validation data set was69 ± 10 HU, corresponding to a 20% decrease in error when compared to training on the original 2D GAN. Quality metric results did not differ statistically for the hold out data set (p = 0.09). Higher errors were observed for air and bone voxels, and registration errors between CT and MRI decreased performance of the algorithm. Dose deviations at the target were within 2% for the photon beams; for the proton plans, 21 patients showed dose deviations under 2%, while 12 had deviations between 2% and 8%. Pass rates (2%/ 2mm) between dose distributions were higher than 98% and 94% for photon and proton plans respectively. The results compare favorably with published algorithms and the method shows potential for MRI-guided clinical workflows. Special attention should be given when beams cross small structures and airways, and further adjustments to the algorithm should be made to increase performance for these regions