Some proximal methods for Poisson intensity CBCT and PET

International audienceCone-Beam Computerized Tomography (CBCT) and Positron Emission Tomography (PET) are two complementary medical imaging modalities providing respectively anatomic and metabolic information on a patient. In the context of public health, one must address the problem of dose reduction of the potentially harmful quantities related to each exam protocol : X-rays for CBCT and radiotracer for PET. Two demonstrators based on a technological breakthrough (acquisition devices work in photon-counting mode) have been developed. It turns out that in this low-dose context, i.e. for low intensity signals acquired by photon counting devices, noise should not be approximated anymore by a Gaussian distribution, but is following a Poisson distribution. We investigate in this paper the two related tomographic reconstruction problems. We formulate separately the CBCT and the PET problems in two general frameworks that encompass the physics of the acquisition devices and the specific discretization of the object to reconstruct. We propose various fast numerical schemes based on proximal methods to compute the solution of each problem. In particular, we show that primal-dual approaches are well suited in the PET case when considering non differentiable regularizations such as Total Variation. Experiments on numerical simulations and real data are in favor of the proposed algorithms when compared with well-established methods

Post-Reconstruction Deconvolution of PET Images by Total Generalized Variation Regularization

Improving the quality of positron emission tomography (PET) images, affected by low resolution and high level of noise, is a challenging task in nuclear medicine and radiotherapy. This work proposes a restoration method, achieved after tomographic reconstruction of the images and targeting clinical situations where raw data are often not accessible. Based on inverse problem methods, our contribution introduces the recently developed total generalized variation (TGV) norm to regularize PET image deconvolution. Moreover, we stabilize this procedure with additional image constraints such as positivity and photometry invariance. A criterion for updating and adjusting automatically the regularization parameter in case of Poisson noise is also presented. Experiments are conducted on both synthetic data and real patient images.Comment: First published in the Proceedings of the 23rd European Signal Processing Conference (EUSIPCO-2015) in 2015, published by EURASI

Optimization of Decision Making in Personalized Radiation Therapy using Deformable Image Registration

Cancer has become one of the dominant diseases worldwide, especially in western countries, and radiation therapy is one of the primary treatment options for 50% of all patients diagnosed. Radiation therapy involves the radiation delivery and planning based on radiobiological models derived primarily from clinical trials. Since 2015 improvements in information technologies and data storage allowed new models to be created using the large volumes of treatment data already available and correlate the actually delivered treatment with outcomes. The goals of this thesis are to 1) construct models of patient outcomes after receiving radiation therapy using available treatment and patient parameters and 2) provide a method to determine real accumulated radiation dose including the impact of registration uncertainties. In Chapter 2, a model was developed predicting overall survival for patients with hepatocellular carcinoma or liver metastasis receiving radiation therapy. These models show which patients benefit from curative radiation therapy based on liver function, and the survival benefit of increased radiation dose on survival. In Chapter 3, a method was developed to routinely evaluate deformable image registration (DIR) with computer-generated landmark pairs using the scale-invariant feature transform. The method presented in this chapter created landmark sets for comparing lung 4DCT images and provided the same evaluation of DIR as manual landmark sets. In Chapter 4, an investigation was performed on the impact of DIR error on dose accumulation using landmarked 4DCT images as the ground truth. The study demonstrated the relationship between dose gradient, DIR error and dose accumulation error, and presented a method to determine error bars on the dose accumulation process. In Chapter 5, a method was presented to determine quantitatively when to update a treatment plan during the course of a multi-fraction radiation treatment of head and neck cancer. This method investigated the ability to use only the planned dose with deformable image registration to predict dose changes caused by anatomical deformations. This thesis presents the fundamental elements of a decision support system including patient pre-treatment parameters and the actual delivered dose using DIR while considering registration uncertainties

First order algorithms in variational image processing

Variational methods in imaging are nowadays developing towards a quite universal and flexible tool, allowing for highly successful approaches on tasks like denoising, deblurring, inpainting, segmentation, super-resolution, disparity, and optical flow estimation. The overall structure of such approaches is of the form ${\cal D}(Ku) + \alpha {\cal R} (u) \rightarrow \min_u$ ; where the functional ${\cal D}$ is a data fidelity term also depending on some input data $f$ and measuring the deviation of $Ku$ from such and ${\cal R}$ is a regularization functional. Moreover $K$ is a (often linear) forward operator modeling the dependence of data on an underlying image, and $\alpha$ is a positive regularization parameter. While ${\cal D}$ is often smooth and (strictly) convex, the current practice almost exclusively uses nonsmooth regularization functionals. The majority of successful techniques is using nonsmooth and convex functionals like the total variation and generalizations thereof or $\ell_1$-norms of coefficients arising from scalar products with some frame system. The efficient solution of such variational problems in imaging demands for appropriate algorithms. Taking into account the specific structure as a sum of two very different terms to be minimized, splitting algorithms are a quite canonical choice. Consequently this field has revived the interest in techniques like operator splittings or augmented Lagrangians. Here we shall provide an overview of methods currently developed and recent results as well as some computational studies providing a comparison of different methods and also illustrating their success in applications.Comment: 60 pages, 33 figure

Optimal Computational Trade-Off of Inexact Proximal Methods (short version)

International audienceIn this paper, we investigate the trade-off between convergence rate and computational cost when minimizing a composite functional with proximal-gradient methods, which are popular optimisation tools in machine learning. We consider the case when the proximity operator is approximated via an iterative procedure, which yields algorithms with two nested loops. We show that the strategy minimizing the computational cost to reach a desired accuracy in finite time is to keep the number of inner iterations constant, which differs from the strategy indicated by a convergence rate analysis

Importance of heterogeneity correction for prostate therapy planning as it relates to prostate motion

2012 Fall.Includes bibliographical references.Prostate adenocarcinoma is the most common cancer among men and second leading cause of mortality of men in the United States. External beam radiotherapy (RT) is often used for local prostate tumor control as part of multimodality therapy. Dosimetric treatment planning for RT is based on complex calculations made by computerized planning software, which are designed to achieve a target prescribed dose to the prostate while not exceeding normal tissue constraints. Those RT planning calculations are made from an initial pre-treatment computed tomographic (CT) scan, which provides the location, volume and density of the prostate and critical normal tissues. The calculation step applies Heterogeneity Correction (HC) during RT planning, which adjusts the delivered radiation fields according to regional tissue densities such as the presence of bone in the anatomic region of interest. Inter-fraction and intra-fraction prostate movement are both known to occur during the course of radiotherapy. Current standards of practice utilize ways to track and account for prostatic movement in order to maintain accurate delivery to that organ. However, those methods do not adjust for the HC that was already applied during the original treatment plan calculations. The use of HC for prostate cancer RT is therefore of particular importance because prostate movement relative to the pelvic skeleton might result in dosimetric inaccuracies, since the HC used in initial RT planning is based on the original prostate position. This project was part of a larger research study in which intact normal male dogs received hypofractionated stereotactic radiation to the prostate, as a translational animal model for human prostate cancer. In this study, inter-fraction prostate motion was evaluated and then those data were used to examine the impact of this movement on the use of heterogeneity correction (HC) on stereotactic body radiation therapy (SBRT) of the prostate, by evaluating the dose received by the planned target volume (PTV) and surrounding tissue during prostate RT planning. In Aim 1, cone beam CT (CBCT) images from ten dogs were evaluated retrospectively to estimate typical inter-fraction prostate movement. Organs of interest were contoured on each daily treatment CBCT data set, and those images were registered (fused) to the original planning CT. Prostate motion was quantified by determining the displacement of each isocenter relative to the original radiotherapy planning CT. For Aim 2, CT scans acquired during the course of SBRT were used to prospectively calculate new treatment plans that incorporated prostate displacement from four dogs, with and without HC. Organs of interest were contoured on each CT data set, and images were registered (fused) to the original planning CT. As above, prostate motion was quantified by measuring the isocenter movement in three axes relative to original RT planning CT. An optimal original planning CT was run twice for each CT, with and without HC, while adjusting the prostatic isocenter. Dosimetric data for organs of interest were evaluated using dose volume histograms (DVH) and comparing doses to previously defined constraint values. Results indicated a wide range of inter-fraction prostate displacement in both Aims 1 and 2, slightly greater in magnitude than similar human prostate movement data. The greatest prostate displacement was in the y axis (anteroposterior). No statistically significant differences were seen in target or normal tissue doses, with or without HC, suggesting that even in the presence of marked prostate motion, potential inaccuracies caused by HC may not have a great impact on the prostate RT planning. As expected, without HC there was a trend for the dose to the most organs of interest to increase slightly. In terms of how displacement affected tissue doses, maximum displacement of prostate was associated with adjacent tissues exceeding the known normal tissue tolerance. In particular, caudal and left displacement led to large doses exceeding the constraint limits for the posterior rectal wall. Those data indicate the importance of continued tracking or other methods to counteract prostate motion. The results provide a more informed approach for using HC relative to prostate motion during treatment of prostate cancer, as well as providing data relevant to tumor control, acute and late toxicities associated with inter-fraction movement of prostate RT

Stereotactic ablative radiotherapy for medically inoperable early stage lung cancer: early outcomes

Objective To evaluate the clinical outcome and safety of stereotactic ablative radiotherapy for medically inoperable stage I non- small-cell lung carcinoma. Design Retrospective case series. Setting Pamela Youde Nethersole Eastern Hospital, Hong Kong. Patients All patients with medically inoperable stage I non-small-cell lung carcinoma receiving stereotactic ablative radiotherapy since its establishment in 2008. Main outcome measures Disease control rate, overall survival, and treatment toxicities. Results Sixteen stage I non-small-cell lung carcinoma patients underwent the procedure from June 2008 to November 2011. The median patient age was 82 years and the majority (81%) had moderate-tosevere co-morbidity based on the Adult Comorbidity Evaluation 27 index. With a median follow-up of 22 months, the 2-year primary tumour control rate, disease-free survival and overall survival rates were 91%, 71% and 87%, respectively. No grade 3 (National Cancer Institute Common Terminology Criteria for Adverse Events) or higher treatment-related complications were reported. Conclusion Stereotactic ablative radiotherapy can achieve a high degree of local control safely in medically inoperable patients with early lung cancer.published_or_final_versio

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy

Single-arm prospective interventional study assessing feasibility of using gallium-68 ventilation and perfusion PET/CT to avoid functional lung in patients with stage III non-small cell lung cancer

BACKGROUND: In the curative-intent treatment of locally advanced lung cancer, significant morbidity and mortality can result from thoracic radiation therapy. Symptomatic radiation pneumonitis occurs in one in three patients and can lead to radiation-induced fibrosis. Local failure occurs in one in three patients due to the lungs being a dose-limiting organ, conventionally restricting tumour doses to around 60 Gy. Functional lung imaging using positron emission tomography (PET)/CT provides a geographic map of regional lung function and preclinical studies suggest this enables personalised lung radiotherapy. This map of lung function can be integrated into Volumetric Modulated Arc Therapy (VMAT) radiotherapy planning systems, enabling conformal avoidance of highly functioning regions of lung, thereby facilitating increased doses to tumour while reducing normal tissue doses. METHODS AND ANALYSIS: This prospective interventional study will investigate the use of ventilation and perfusion PET/CT to identify highly functioning lung volumes and avoidance of these using VMAT planning. This single-arm trial will be conducted across two large public teaching hospitals in Australia. Twenty patients with stage III non-small cell lung cancer will be recruited. All patients enrolled will receive dose-escalated (69 Gy) functional avoidance radiation therapy. The primary endpoint is feasibility with this achieved if ≥15 out of 20 patients meet pre-defined feasibility criteria. Patients will be followed for 12 months post-treatment with serial imaging, biomarkers, toxicity assessment and quality of life assessment. DISCUSSION: Using advanced techniques such as VMAT functionally adapted radiation therapy may enable safe moderate dose escalation with an aim of improving local control and concurrently decreasing treatment related toxicity. If this technique is proven feasible, it will inform the design of a prospective randomised trial to assess the clinical benefits of functional lung avoidance radiation therapy. ETHICS AND DISSEMINATION: This study was approved by the Peter MacCallum Human Research Ethics Committee. All participants will provide written informed consent. Results will be disseminated via publications. TRIALS REGISTRATION NUMBER: NCT03569072; Pre-results

Towards on-line plan adaptation of unified intensity-modulated arc therapy using a fast-direct aperture optimization algorithm

External beam radiotherapy (EBRT) plays a vital role in the treatment of cancer, with close to half of all cancer patients receiving EBRT at some point over their course of treatment. Although EBRT is a well-established form of treatment, there are a number of ways in which EBRT could still be improved in terms of quality and efficiency for treatment planning and radiation dose delivery. This thesis reports a series of improvements made to EBRT. First, we developed and evaluated a new treatment planning technique called unified intensity-modulated arc therapy (UIMAT) which combines the optimization and delivery of rotational volumetric modulated arc therapy (VMAT) and fixed-gantry intensity-modulated radiation therapy (IMRT). When retrospectively compared to clinical treatment plans using VMAT or IMRT alone, UIMAT plans reduced the dose to nearby critical structures by as much as 23% without compromising tumour volume coverage. The UIMAT plans were also more efficient to deliver. The reduction in normal tissue dose could help lower the probability of treatment-related toxicities, or alternatively could be used to improve tumour control probability, via dose escalation, while maintaining current dose limits for organs at risk. Second, we developed a new fast inverse direct aperture optimization (FIDAO) algorithm for IMRT, VMAT, and UIMAT treatment planning. FIDAO introduces modifications to the direct aperture optimization (DAO) process that help improve its computational efficiency. As demonstrated in several test cases, these modifications do not significantly impact the plan quality but reduced the DAO time by as much as 200-fold. If implemented with graphical processing units (GPUs), this project may allow for applications such as on-line treatment adaptation. Third, we investigated a method of acquiring tissue density information from cone-beam computed tomography (CBCT) datasets for on-line dose calculations, plan assessment, and potentially plan adaptation using FIDAO. This calibration technique accounts for patient-specific scattering conditions, demonstrated high dosimetric accuracy, and can be easily automated for on-line plan assessment. Collectively, these three projects will help reduce the normal tissue doses from EBRT, improve the planning and delivery efficiency, and pave the way for application like on-line plan assessment and adaptive radiotherapy in response to anatomical changes