We describe a cloud-based medical image reconstruction service designed to meet a real-time and daily demand to reconstruct thousands of images from proton cancer treatment facilities worldwide. Rapid reconstruction of a three-dimensional Proton Computed Tomography (pCT) image can require the transfer of 100 GB of data and use of approximately 120 GPU-enabled compute nodes. The nature of proton therapy means that demand for such a service is sporadic and comes from potentially hundreds of clients worldwide. We thus explore the use of a commercial cloud as a scalable and cost-efficient platform for pCT reconstruction. To address the high performance requirements of this application we leverage Amazon Web Services' GPU-enabled cluster resources that are provisioned with high performance networks between nodes. To support episodic demand, we develop an on-demand multi-user provisioning service that can dynamically provision and resize clusters based on image reconstruction requirements, priorities, and wait times. We compare the performance of our pCT reconstruction service running on commercial cloud resources with that of the same application on dedicated local high performance computing resources. We show that we can achieve scalable and on-demand reconstruction of large scale pCT images for simultaneous multi-client requests, processing images in less than 10 minutes for less than $10 per image.

Accurate estimation of effective camera focal length is crucial to the success of panoramic image stitching. Fast techniques for estimating the focal length exist, but are dependent upon a close initial approximation or the existence of a full circle panoramic image sequence. Numerical solutions of the focal length demonstrate strong coupling between the focal length and the angles used to position each component image about the common spherical center. This paper demonstrates that parameterizing panoramic image positions using spherical arc length instead of angles effectively decouples the focal length from the image position. This new parameterization does not require an initial focal length estimate for quick convergence, nor does it require a full circle panorama in order to refine the focal length. Experiments with synthetic and real image sets demonstrate the robustness of the method and a speedup of 5 to 20 times over angle based positioning.

We present a new method for creating n-dimensional rotation matrices from manipulating the projections of n-dimensional data coordinate axes onto a viewing plane. A user interface for n-dimensional rotation is implemented. The interface is shown to have no rotational hysteresis.>