Residual Back Projection With Untrained Neural Networks

Background and Objective: The success of neural networks in a number of image processing tasks has motivated their application in image reconstruction problems in computed tomography (CT). While progress has been made in this area, the lack of stability and theoretical guarantees for accuracy, together with the scarcity of high-quality training data for specific imaging domains pose challenges for many CT applications. In this paper, we present a framework for iterative reconstruction (IR) in CT that leverages the hierarchical structure of neural networks, without the need for training. Our framework incorporates this structural information as a deep image prior (DIP), and uses a novel residual back projection (RBP) connection that forms the basis for our iterations. Methods: We propose using an untrained U-net in conjunction with a novel residual back projection to minimize an objective function and achieve high-accuracy reconstruction. In each iteration, the weights of the untrained U-net are optimized, and the output of the U-net in the current iteration is used to update the input of the U-net in the next iteration through the aforementioned RBP connection. Results: Experimental results demonstrate that the RBP-DIP framework offers improvements over other state-of-the-art conventional IR methods, as well as pre-trained and untrained models with similar network structures under multiple conditions. These improvements are particularly significant in the few-view, limited-angle, and low-dose imaging configurations. Conclusions: Applying to both parallel and fan beam X-ray imaging, our framework shows significant improvement under multiple conditions. Furthermore, the proposed framework requires no training data and can be adjusted on-demand to adapt to different conditions (e.g. noise level, geometry, and imaged object)

Technological developments allowing for the widespread clinical adoption of proton radiotherapy

External beam radiation therapy using accelerated protons has undergone significant development since the first patients were treated with accelerated protons in 1954. Widespread adoption of proton therapy is now taking place and is fully justified based on early clinical and technical research and development. Two of the main advantages of proton radiotherapy are improved healthy tissue sparing and increased dose conformation. The latter has been improved dramatically through the clinical realization of Pencil Beam Scanning (PBS). Other significant advancements in the past 30 years have also helped to establish proton radiotherapy as a major clinical modality in the cancer-fighting arsenal. Proton radiotherapy technologies are constantly evolving, and several major breakthroughs have been accomplished which could allow for a major revolution in proton therapy if clinically implemented. In this thesis, I will present research and innovative developments that I personally initiated or participated in that brought proton radiotherapy to its current state as well as my ongoing involvement in leading research and technological developments which will aid in the mass adoption of proton radiotherapy. These include beam dosimetry, patient positioning technologies, and creative methods that verify the Monte Carlo dose calculations which are now used in proton treatment planning. I will also discuss major technological advances concerning beam delivery that should be implemented clinically and new paradigms towards patient positioning. Many of these developments and technologies can benefit the cancer patient population worldwide and are now ready for mass clinical implementation. These developments will improve proton radiotherapy efficiencies and further reduce the cost of proton therapy facilities. This thesis therefore reflects my historical and ongoing efforts to meet market costs and time demands so that the clinical benefit of proton radiotherapy can be realized by a more significant fraction of cancer patients worldwide

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

Evaluation of the region-specific risks of accidental radioactive releases from the European Spallation Source

The European Spallation Source (ESS) is a neutron research facility under construction in southern Sweden. The facility will produce a wide range ofradionuclides that could be released into the environment. Some radionuclides are of particular concern such as the rare earth gadolinium-148. In this article, the local environment was investigated in terms of food production and rare earth element concentration in soil. The collected data will later be used to model thetransfer of radioactive contaminations from the ESS