Abstract Development of emerging imaging systems for biological and medical imaging often involves multi-dimensional acquisition protocols and thus correspondingly the need for complex reconstruction algorithms and advanced machine learning algorithms in order to produce the desired resulting image and quantitative tumor characteristics. This proposal requests funding for a high performance computing system, entitled Protected Radiomics Analysis Commons for Deep Learning in Biomedical Discovery, to further the development and application of deep learning techniques to quantitative image analysis and image reconstruction. There are 12 specific NIH projects that will benefit from the proposed computing infrastructure system. We present the 12 projects through examples from within four Specific Research Topics areas: (a) tomographic image reconstruction, (b) quantitative image analysis, (c) functional quantification, and (d) association of radiomics (image data) with phenotypic and genomic data. The proposed system is a computing cluster, which uses ScaleMP's Versatile SMP software to aggregate the cluster nodes into a single symmetric multiprocessing computer. The major hardware components consist of 1 HP Enterpris\e ProLiant DL380 server and 8 Apollo 6500 compute nodes, with a total of 2.1 TB of main memory, 18 Intel Xeon E5-2640v4 10-core CPUs, and 32 nVidia Tesla P100 GPUs. The servers will be connected via a 100Gbps EDR Infiniband network. In addition, three important software components, which aim to reduce the complexity of the computing environment and increase researcher productivity, will be integrated into the hardware components: the aforementioned ScaleMP vSMP to create a single virtual computer from the cluster nodes, Cendio ThinLinc to provide remote desktop graphical login services, and Bitfusion Flex AI Platform which provides GPU virtualization, scheduling, and optimization, as well as curated container deployment of common deep learning frameworks. The Protected Radiomics Analysis Commons for Deep Learning in Biomedical Discovery will allow researchers to expedite, extend, and translate their current NIH funding in areas of many-dimensional image reconstruction and image analysis algorithms – all of which will benefit greatly from deep learning approaches – within a secure, protected, and HIPAA- compliant sharable environment.

Project Narrative Development of emerging imaging systems for biological and medical imaging often involves multi-dimensional acquisition protocols and thus correspondingly the need for complex reconstruction algorithms and advanced machine learning algorithms in order to produce the desired resulting image and quantitative tumor characteristics. This proposal requests funding for a high performance computing system, entitled Protected Radiomics Analysis Commons for Deep Learning in Biomedical Discovery, to further the development and application of deep learning techniques within four Specific Research Topics areas of (a) tomographic image reconstruction, (b) quantitative image analysis, (c) functional quantification, and (d) association of radiomics (image data) with phenotypic and genomic data. The Protected Radiomics Analysis Commons for Deep Learning in Biomedical Discovery will allow researchers to expedite, extend, and translate their current NIH funding in areas of many- dimensional image reconstruction and image analysis algorithms – all of which will benefit greatly from deep learning approaches – within a secure, protected, and HIPAA-compliant sharable environment.