Technical Note: 4D cone-beam CT reconstruction from sparse-view CBCT data for daily motion assessment in pencil beam scanned proton therapy (PBS-PT)

Purpose: The number of pencil beam scanned proton therapy (PBS-PT) facilities equipped with cone-beam computed tomography (CBCT) imaging treating thoracic indications is constantly rising. To enable daily internal motion monitoring during PBS-PT treatments of thoracic tumors, we assess the performance of Motion-Aware RecOnstructiOn method using Spatial and Temporal Regularization (MA-ROOSTER) four-dimensional CBCT (4DCBCT) reconstruction for sparse-view CBCT data and a realistic data set of patients treated with proton therapy. Methods: Daily CBCT projection data for nine non-small cell lung cancer (NSCLC) patients and one SCLC patient were acquired at a proton gantry system (IBA Proteus® One). Four-dimensional CBCT images were reconstructed applying the MA-ROOSTER and the conventional phase-correlated Feldkamp-Davis-Kress (PC-FDK) method. Image quality was assessed by visual inspection, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and the structural similarity index measure (SSIM). Furthermore, gross tumor volume (GTV) centroid motion amplitudes were evaluated. Results: Image quality for the 4DCBCT reconstructions using MA-ROOSTER was superior to the PC-FDK reconstructions and close to FDK images (median CNR: 1.23 [PC-FDK], 1.98 [MA-ROOSTER], and 1.98 [FDK]; median SNR: 2.56 [PC-FDK], 4.76 [MA-ROOSTER], and 5.02 [FDK]; median SSIM: 0.18 [PC-FDK vs FDK], 0.31 [MA-ROOSTER vs FDK]). The improved image quality of MA-ROOSTER facilitated GTV contour warping and realistic motion monitoring for most of the reconstructions. Conclusion: MA-ROOSTER based 4DCBCTs performed well in terms of image quality and appear to be promising for daily internal motion monitoring in PBS-PT treatments of (N)SCLC patients

Dynamic Cone-beam CT Reconstruction using Spatial and Temporal Implicit Neural Representation Learning (STINR)

Objective: Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable applications including tumor motion tracking/prediction and intra-delivery dose calculation/accumulation. However, the dynamic CBCT reconstruction is a substantially challenging spatiotemporal inverse problem, due to the extremely limited projection sample available for each CBCT reconstruction (one projection for one CBCT volume). Approach: We developed a simultaneous spatial and temporal implicit neural representation (STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image and the evolution of its motion into spatial and temporal multi-layer perceptrons (MLPs), and iteratively optimized the neuron weighting of the MLPs via acquired projections to represent the dynamic CBCT series. In addition to the MLPs, we also introduced prior knowledge, in form of principal component analysis (PCA)-based patient-specific motion models, to reduce the complexity of the temporal INRs to address the ill-conditioned dynamic CBCT reconstruction problem. We used the extended cardiac torso (XCAT) phantom to simulate different lung motion/anatomy scenarios to evaluate STINR. The scenarios contain motion variations including motion baseline shifts, motion amplitude/frequency variations, and motion non-periodicity. The scenarios also contain inter-scan anatomical variations including tumor shrinkage and tumor position change. Main results: STINR shows consistently higher image reconstruction and motion tracking accuracy than a traditional PCA-based method and a polynomial-fitting based neural representation method. STINR tracks the lung tumor to an averaged center-of-mass error of <2 mm, with corresponding relative errors of reconstructed dynamic CBCTs <10%

A Review of 4DCT Imaging and Reconstruction Methods

In this paper, the main literature related to 4DCT imaging and reconstruction techniques over the past 20 years is reviewed, and the contents are summarized. This paper provides a systematic and comprehensive introduction to 4DCT research from five perspectives: the concept of 4DCT, scanning mode and imaging method, reconstruction algorithm, application, research status, and future development expectations. In this study, five types of reconstruction algorithms are summarized, and the advantages, disadvantages, and research difficulties of each algorithm are briefly evaluated. Finally, we conduct a brief statistical analysis on the cited works from the perspective of reconstruction methods, revealing the research progress and future research trends of 4DCT reconstruction algorithms

Surrogate driven respiratory motion model derived from CBCT projection data

Cone Beam Computed Tomography (CBCT) is the most common imaging method for Image Guided Radiation Therapy (IGRT). However due to the slow rotating gantry, the image quality of CBCT can be adversely affected by respiratory motion, as it blurs the tumour and nearby organs at risk (OARs), which makes visualization of organ boundaries difficult, in particular for organs in the thoracic region. Currently one approach to tackle the problem of respiratory motion is the use of respiratory motion model to compensate for the motion during CBCT image reconstruction. The overall goal of this work is to estimate the 3D motion, including the breath-to-breath variability, on the day of treatment directly from the CBCT projection data, without requiring any external devices. The work presented here consist of two main parts: firstly, we introduce a novel data driven method based on Principal Component Analysis PCA, with the goal to extract a surrogate signal related to the internal anatomy from the CBCT projections. Secondly, using the extracted signals, we use surrogate-driven respiratory motion models to estimate the patient’s 3D respiratory motion. We utilized a recently developed generalized framework that unifies image registration and correspondence model fitting into a single optimization. This enables the model to be fitted directly to unsorted/unreconstructed data (CBCT projection data), thereby allowing an estimate of the patient’s respiratory motion on the day of treatment. To evaluate our methods, we have used an anthropomorphic software phantom combined with CBCT projection simulations. We have also tested the proposed method on clinical data with promising results obtained

ROBUST DEEP LEARNING METHODS FOR SOLVING INVERSE PROBLEMS IN MEDICAL IMAGING

The medical imaging field has a long history of incorporating machine learning algorithms to address inverse problems in image acquisition and analysis. With the impressive successes of deep neural networks on natural images, we seek to answer the obvious question: do these successes also transfer to the medical image domain? The answer may seem straightforward on the surface. Tasks like image-to-image transformation, segmentation, detection, etc., have direct applications for medical images. For example, metal artifact reduction for Computed Tomography (CT) and reconstruction from undersampled k-space signal for Magnetic Resonance (MR) imaging can be formulated as an image-to-image transformation; lesion/tumor detection and segmentation are obvious applications for higher level vision tasks. While these tasks may be similar in formulation, many practical constraints and requirements exist in solving these tasks for medical images. Patient data is highly sensitive and usually only accessible from individual institutions. This creates constraints on the available groundtruth, dataset size, and computational resources in these institutions to train performant models. Due to the mission-critical nature in healthcare applications, requirements such as performance robustness and speed are also stringent. As such, the big-data, dense-computation, supervised learning paradigm in mainstream deep learning is often insufficient to address these situations. In this dissertation, we investigate ways to benefit from the powerful representational capacity of deep neural networks while still satisfying the above-mentioned constraints and requirements. The first part of this dissertation focuses on adapting supervised learning to account for variations such as different medical image modality, image quality, architecture designs, tasks, etc. The second part of this dissertation focuses on improving model robustness on unseen data through domain adaptation, which ameliorates performance degradation due to distribution shifts. The last part of this dissertation focuses on self-supervised learning and learning from synthetic data with a focus in tomographic imaging; this is essential in many situations where the desired groundtruth may not be accessible

Moving Metal Artifact Reduction and Intrinsic Gating for Cone-Beam CT Scans of the Thorax Region

In this work, novel algorithms in the field of retrospective intrinsic respiratory and cardiac gating and moving metal artifact reduction (MMAR) for cone-beam CT (CBCT) scans that are used in image-guided radiation therapy (IGRT) were developed. The added difficulty for CBCT scans is the relatively long acquisition time of up to 60 s compared to 0.25 s for clinical CT scans. The occuring respiratory and cardiac motion in combination with metal inserts cannot be handled with classical metal artifact reduction (MAR) methods. The proposed MMAR algorithms utilize an approach that combines the fields of motion compensation (MoCo) with classical MAR methods. In order for the MMAR methods to work, the motion state has to be known for every projection angle. A novel intrinsic gating approach, that automatically generates a list of potential motion surrogate candidates and identifies the best, was developed and used as a basis for the MMAR algorithms, so they can be applied for patients where no externally recorded motion signal is available. Whereas classical approaches for intrinsic gating are not designed for a laterally shifted detector, that is commonly used in IGRT with a CBCT, the novel algorithm works on scans with or without a shifted detector. It can also be used as a basis for 4D (3D + respiratory) or 5D (3D + respiratory + cardiac) MoCo algorithms

On the investigation of a novel x-ray imaging techniques in radiation oncology

Radiation therapy is indicated for nearly 50% of cancer patients in Australia. Radiation therapy requires accurate delivery of ionising radiation to the neoplastic tissue and pre-treatment in situ x-ray imaging plays an important role in meeting treatment accuracy requirements. Four dimensional cone-beam computed tomography (4D CBCT) is one such pre-treatment imaging technique that can help to visualise tumour target motion due to breathing at the time of radiation treatment delivery. Measuring and characterising the target motion can help to ensure highly accurate therapeutic x-ray beam delivery. In this thesis, a novel pre-treatment x-ray imaging technique, called Respiratory Triggered 4D cone-beam Computed Tomography (RT 4D CBCT), is conceived and investigated. Specifically, the aim of this work is to progress the 4D CBCT imaging technology by investigating the use of a patient’s breathing signal to improve and optimise the use of imaging radiation in 4D CBCT to facilitate the accurate delivery of radiation therapy. These investigations are presented in three main studies: 1. Introduction to the concept of respiratory triggered four dimensional conebeam computed tomography. 2. A simulation study exploring the behaviour of RT 4D CBCT using patientmeasured respiratory data. 3. The experimental realisation of RT 4D CBCT working in a real-time acquisitions setting. The major finding from this work is that RT 4D CBCT can provide target motion information with a 50% reduction in the x-ray imaging dose applied to the patient

A Heterogeneous Patient-Specific Biomechanical Model of the Lung for Tumor Motion Compensation and Effective Lung Radiation Therapy Planning

Radiation therapy is a main component of treatment for many lung cancer patients. However, the respiratory motion can cause inaccuracies in radiation delivery that can lead to treatment complications. In addition, the radiation-induced damage to healthy tissue limits the effectiveness of radiation treatment. Motion management methods have been developed to increase the accuracy of radiation delivery, and functional avoidance treatment planning has emerged to help reduce the chances of radiation-induced toxicity. In this work, we have developed biomechanical model-based techniques for tumor motion estimation, as well as lung functional imaging. The proposed biomechanical model accurately estimates lung and tumor motion/deformation by mimicking the physiology of respiration, while accounting for heterogeneous changes in the lung mechanics caused by COPD, a common lung cancer comorbidity. A biomechanics-based image registration algorithm is developed and is combined with an air segmentation algorithm to develop a 4DCT-based ventilation imaging technique, with potential applications in functional avoidance therapies