For the last two years, we have been experimenting with applying compressed sensing parallel imaging for body imaging of pediatric patients. It is a joint-effort by teams from UC Berkeley, Stanford University and GE Healthcare. This paper aims to summarize our experience so far. We describe our acquisition approach: 3D spoiled-gradient-echo with poisson-disc random undersampling of the phase encodes. Our re-construction approach: ℓ1-SPIRiT, an iterative autocalibrating parallel imaging reconstruction that enforces both data consistency and joint-sparsity in the wavelet domain. Our implementation: an on-line parallelized implementation of ℓ1-SPIRiT on multi-core CPU and General Purpose Graphics Processors (GPGPU) that achieves sub-minute 3D reconstructions with 8-channels. Clinical results showing higher quality reconstruction and better diagnostic confidence than parallel imaging alone at accelerations on the order of number of coils.

Use of DVF and its PIDVF will improve the self-consistency of points, contour, and dose mappings in image guided adaptive therapy.

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Purpose: to develop a research computing framework for a large research program in IGART. The goals are to maximize reuse of code developed by cooperating research projects, to seamlessly integrate new types of research data with the database of a commercial treatment planning system (TPS), to allow for automated processing of large quantities of data, and to provide a robust software infrastructure for in‐house clinical implementation of research results. Method and Materials: The language of implementation is C++. The goal of reusability is achieved by dividing the framework into two software layers: Data Abstraction Layer (DAL), and Data Interface Layer (DIL). DAL consists of classes representing abstract data types. Research algorithms operate on DAL objects. The DIL consists of data readers implemented as classes inherited from the DAL parent. The DAL object factories retrieve data by reading data configuration files which specify the type, the format, and the physical location of data to be retrieved. An object factory instances a required DIL reader which is presented to the user as a DAL class. By this design one ensures that data processing algorithms are independent of data formats. The goal of integration with the TPS is achieved by creating a file based research database which parallels the TPS database. A data configuration file represents a database entry. The goal of automation is met by introducing a Database Interface Object (DIO) which aggregates object factories. A scripting interface allows users to retrieve data, call “user hook” function where data processing takes place, create DAL classes which are filled by the processed data, and write results into the database in user‐specified format. Results: We used object oriented design patterns to develop a robust research computing framework for IGART research. This work supported by NIH grant P01CA11602.

Purpose: To experimentally validate a new algorithm for reconstructing the 3D positions of implanted brachytherapy seeds from 2D projection images. Method and materials: The iterative forward projection matching (IFPM) algorithm consists of finding the 3D seed geometry that minimizes the sum‐of‐squared‐difference of the pixel‐by‐pixel intensities between computed projection images and measured auto‐segmented images of implanted seeds. IFPM starts with an approximation to the initial seeds configuration, e.g., the pre‐implant seed arrangement. It then iteratively refines the 3D seed coordinates until the computed projections match with the measured projections. Three pairs of computed and measured projection images, with known imaging geometry, are used. Two brachytherapy phantoms were fabricated with 12 and 72 seeds in known configurations. Three projections of each phantom were acquired using an Acuity digital simulator along with a full 660 projection Conebeam CT (CBCT). Image pre‐processing steps were performed to create the binary seed centroids images for use by IFPM algorithm. To quantify IFPM accuracy, the actual seed positions were extracted from the CBCT images by the Brachy Vision‐planning system. Results: For the 12 seed phantom data, the mean reconstruction error was found to be 0.83±0.34mm where as for 72 seed phantom it was 0.97±0.37mm. The each test trials converged in 4–10 iterations with computation time of 2.8–62 min on a 2 GHz processor. Discussion: The IFPM algorithm avoids establishing seed projection correspondence required by standard back‐projection methods. In phantom studies we have demonstrated 1 mm accuracy in reconstructing the 3D positions of brachytherapy seeds from 2D projection images. This supports the potential of this algorithm for accurate and robust seed reconstruction in patients. This project was supported by grant from Varian Medical Systems.