Online adaptive radiation therapy (ART) promises the ability to deliver an 20 optimal treatment in response to daily patient anatomic variation. A major technical barrier for the clinical implementation of online ART is the requirement of rapid image segmentation. Deformable image registration (DIR) has been used as an automated segmentation method to transfer tumor/organ contours from the planning image to daily images. However, the current 25 computational time of DIR is insufficient for online ART. In this work, this issue is addressed by using computer graphics processing units (GPUs). A greyscale based DIR algorithm called demons and five of its variants were implemented on GPUs using the Compute Unified Device Architecture (CUDA) programming environment. The spatial accuracy of these algorithms was 30 evaluated over five sets of pulmonary 4DCT images with an average size of 256×256×100 and more than 1,100 expert-determined landmark point pairs each. For all the testing scenarios presented in this paper, the GPU-based DIR computation required around 7 to 11 seconds to yield an average 3D error ranging from 1.5 to 1.8 mm. It is interesting to find out that the original passive 35 force demons algorithms outperform subsequently proposed variants based on the combination of accuracy, efficiency, and ease of implementation. X. Gu et al.2

Monte Carlo simulation is the most accurate method for absorbed dose calculations in radiotherapy. Its efficiency still requires improvement for routine clinical applications, especially for online adaptive radiotherapy. In this paper, we report our recent development on a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport. We have implemented the dose planning method (DPM) Monte Carlo dose calculation package (Sempau et al 2000 Phys. Med. Biol. 45 2263-91) on the GPU architecture under the CUDA platform. The implementation has been tested with respect to the original sequential DPM code on the CPU in phantoms with water-lung-water or water-bone-water slab geometry. A 20 MeV mono-energetic electron point source or a 6 MV photon point source is used in our validation. The results demonstrate adequate accuracy of our GPU implementation for both electron and photon beams in the radiotherapy energy range. Speed-up factors of about 5.0-6.6 times have been observed, using an NVIDIA Tesla C1060 GPU card against a 2.27 GHz Intel Xeon CPU processor.

Single-nucleotide polymorphisms (SNPs) in a gene sequence are markers for a variety of human diseases. Detection of SNPs with high specificity and sensitivity is essential for effective practical implementation of personalized medicine. Current DNA sequencing, including SNP detection, primarily uses enzyme-based methods or fluorophore-labeled assays that are time-consuming, need laboratory-scale settings, and are expensive. Previously reported electrical charge-based SNP detectors have insufficient specificity and accuracy, limiting their effectiveness. Here, we demonstrate the use of a DNA strand displacement-based probe on a graphene field effect transistor (FET) for high-specificity, single-nucleotide mismatch detection. The single mismatch was detected by measuring strand displacement-induced resistance (and hence current) change and Dirac point shift in a graphene FET. SNP detection in large double-helix DNA strands (e.g., 47 nt) minimize false-positive results. Our electrical sensor-based SNP detection technology, without labeling and without apparent cross-hybridization artifacts, would allow fast, sensitive, and portable SNP detection with single-nucleotide resolution. The technology will have a wide range of applications in digital and implantable biosensors and high-throughput DNA genotyping, with transformative implications for personalized medicine.NA sequencing has opened new windows of opportunities for diagnosis of genetic disease (1), biological informatics (2), forensics (3), and environmental monitoring (4). Discrimination of a single mismatch in a long DNA strand is of significant importance and is essential to detect single nucleotide polymorphism (SNP). SNP is a single-nucleotide mutation in a gene sequence and varies among paired chromosomes, between individuals, and across biological species. SNP mutations can have dramatic influence on the health. They are markers for variety of diseases, including various forms of cancer, genetic disorders (5-7), and are of critical importance for successful practical implementation of the concept of personalized medicine (8). Thus, the development of biosensors detecting SNP mutations with high sensitivity and specificity is essential for effective personalized medicine approaches.Current DNA sequencing, including SNP detection, is achieved primarily by enzyme-based methods, using DNA ligase (9), DNA polymerase (9), and nucleases (10). These methods generate highly accurate genotyping. However, the methods are expensive and time-consuming. One of the common enzyme-free methods to detect SNPs uses hybridization of the target DNA to a probe on a microarray and detects their binding events with fluorescence microscopy/spectroscopy. Hybridization-based methods for SNP detection have several disadvantages, including cross-hybridization between allele-specific probes (11). This limits the detection of a single mismatch in long probe-target hybridization as the longer probes have more frequent cross hybridization. For example, a single mismatch in the center o...

The widespread adoption of on-board volumetric imaging in cancer radiotherapy has stimulated research efforts to develop online adaptive radiotherapy techniques to handle the inter-fraction variation of the patient's geometry. Such efforts face major technical challenges to perform treatment planning in real time. To overcome this challenge, we are developing a supercomputing online re-planning environment (SCORE) at the University of California, San Diego (UCSD). As part of the SCORE project, this paper presents our work on the implementation of an intensity-modulated radiation therapy (IMRT) optimization algorithm on graphics processing units (GPUs). We adopt a penalty-based quadratic optimization model, which is solved by using a gradient projection method with Armijo's line search rule. Our optimization algorithm has been implemented in CUDA for parallel GPU computing as well as in C for serial CPU computing for comparison purpose. A prostate IMRT case with various beamlet and voxel sizes was used to evaluate our implementation. On an NVIDIA Tesla C1060 GPU card, we have achieved speedup factors of 20-40 without losing accuracy, compared to the results from an Intel Xeon 2.27 GHz CPU. For a specific nine-field prostate IMRT case with 5 x 5 mm(2) beamlet size and 2.5 x 2.5 x 2.5 mm(3) voxel size, our GPU implementation takes only 2.8 s to generate an optimal IMRT plan. Our work has therefore solved a major problem in developing online re-planning technologies for adaptive radiotherapy.