Spatiotemporal structure-aware dictionary learning-based 4D CBCT reconstruction

Surrogate-driven respiratory motion model for projection-resolved motion estimation and motion compensated cone-beam CT reconstruction from unsorted projection data

Objective.As the most common solution to motion artefact for cone-beam CT (CBCT) in radiotherapy, 4DCBCT suffers from long acquisition time and phase sorting error. This issue could be addressed if the motion at each projection could be known, which is a severely ill-posed problem. This study aims to obtain the motion at each time point and motion-free image simultaneously from unsorted projection data of a standard 3DCBCT scan.Approach.Respiration surrogate signals were extracted by the Intensity Analysis method. A general framework was then deployed to fit a surrogate-driven motion model that characterized the relation between the motion and surrogate signals at each time point. Motion model fitting and motion compensated reconstruction were alternatively and iteratively performed. Stochastic subset gradient based method was used to significantly reduce the computation time. The performance of our method was comprehensively evaluated through digital phantom simulation and also validated on clinical scans from six patients.Results.For digital phantom experiments, motion models fitted with ground-truth or extracted surrogate signals both achieved a much lower motion estimation error and higher image quality, compared with non motion-compensated results.For the public SPARE Challenge datasets, more clear lung tissues and less blurry diaphragm could be seen in the motion compensated reconstruction, comparable to the benchmark 4DCBCT images but with a higher temporal resolution. Similar results were observed for two real clinical 3DCBCT scans.Significance.The motion compensated reconstructions and motion models produced by our method will have direct clinical benefit by providing more accurate estimates of the delivered dose and ultimately facilitating more accurate radiotherapy treatments for lung cancer patients.

"Under the hood": artificial intelligence in personalized radiotherapy

This review presents and discusses the ways in which artificial intelligence (AI) tools currently intervene, or could potentially intervene in the future, to enhance the diverse tasks involved in the radiotherapy workflow. The radiotherapy framework is presented on 2 different levels for the personalization of the treatment, distinct in tasks and methodologies. The first level is the clinically well-established anatomy-based workflow, known as adaptive radiation therapy. The second level is referred to as biology-driven workflow, explored in the research literature and recently appearing in some preliminary clinical trials for personalized radiation treatments. A 2-fold role for AI is defined according to these 2 different levels. In the anatomy-based workflow, the role of AI is to streamline and improve the tasks in terms of time and variability reductions compared to conventional methodologies. The biology-driven workflow instead fully relies on AI, which introduces decision-making tools opening uncharted frontiers that were in the past deemed challenging to explore. These methodologies are referred to as radiomics and dosiomics, handling imaging and dosimetric information, or multiomics, when complemented by clinical and biological parameters (ie, biomarkers). The review explicitly highlights the methodologies that are currently incorporated into clinical practice or still in research, with the aim of presenting the AI's growing role in personalized radiotherapy.

Combining convolutional sparse coding with total variation for sparse-view CT reconstruction

Conventional dictionary-learning-based computed tomography (CT) reconstruction methods extract patches from an original image to train, ignoring the consistency of pixels in overlapping patches. To address the problem, this paper proposes a method combining convolutional sparse coding (CSC) with total variation (TV) for sparse-view CT reconstruction. The proposed method inherits the advantages of CSC by directly processing the whole image without dividing it into overlapping patches, which preserves more details and reduces artifacts caused by patch aggregation. By introducing a TV regularization term to enhance the constraint of the image domain, the noise can be effectively further suppressed. The alternating direction method of multipliers algorithm is employed to solve the objective function. Numerous experiments are conducted to validate the performance of the proposed method in different views. Qualitative and quantitative results show the superiority of the proposed method in terms of noise suppression, artifact reduction, and image details recovery.