Engineering topics

Xing, L

Publications and source records attributed to Xing, L.

29 records · Page 2

DOE OSTI2016

MO-FG-CAMPUS-TeP2-05: Optimizing Stereotactic Radiosurgery Treatment of Multiple Brain Metastasis Lesions with Individualized Rotational Arc Trajectories

Purpose: Radiosurgery of multiple (n>4) brain metastasis lesions requires 3–4 noncoplanar VMAT arcs with excessively high monitor units and long delivery time. We investigated whether an improved optimization technique would decrease the needed arc numbers and increase the delivery efficiency, while improving or maintaining the plan quality. Methods: The proposed 4pi arc space optimization algorithm consists of two steps: automatic couch angle selection followed by aperture generation for each arc with optimized control points distribution. We use a greedy algorithm to select the couch angles. Starting from a single coplanar arc plan we search through the candidate noncoplanar arcs to pick a single noncoplanar arc that will bring the best plan quality when added into the existing treatment plan. Each time, only one additional noncoplanar arc is considered making the calculation time tractable. This process repeats itself until desired number of arc is reached. The technique is first evaluated in coplanar arc delivery scheme with testing cases and then applied to noncoplanar treatments of a case with 12 brain metastasis lesions. Results: Clinically acceptable plans are created within minutes. For the coplanar testing cases the algorithm yields singlearc plans with better dose distributions than that of two-arc VMAT, simultaneously with a 12–17% reduction in the delivery time and a 14–21% reduction in MUs. For the treatment of 12 brain mets while Paddick conformity indexes of the two plans were comparable the SCG-optimization with 2 arcs (1 noncoplanar and 1 coplanar) significantly improved the conventional VMAT with 3 arcs (2 noncoplanar and 1 coplanar). Specifically V16 V10 and V5 of the brain were reduced by 11%, 11% and 12% respectively. The beam delivery time was shortened by approximately 30%. Conclusion: The proposed 4pi arc space optimization technique promises to significantly reduce the brain toxicity while greatly improving the treatment efficiency.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TU-FG-201-04: Computer Vision in Autonomous Quality Assurance of Linear Accelerators

Purpose: Routine quality assurance (QA) of linear accelerators represents a critical and costly element of a radiation oncology center. Recently, a system was developed to autonomously perform routine quality assurance on linear accelerators. The purpose of this work is to extend this system and contribute computer vision techniques for obtaining quantitative measurements for a monthly multi-leaf collimator (MLC) QA test specified by TG-142, namely leaf position accuracy, and demonstrate extensibility for additional routines. Methods: Grayscale images of a picket fence delivery on a radioluminescent phosphor coated phantom are captured using a CMOS camera. Collected images are processed to correct for camera distortions, rotation and alignment, reduce noise, and enhance contrast. The location of each MLC leaf is determined through logistic fitting and a priori modeling based on knowledge of the delivered beams. Using the data collected and the criteria from TG-142, a decision is made on whether or not the leaf position accuracy of the MLC passes or fails. Results: The locations of all MLC leaf edges are found for three different picket fence images in a picket fence routine to 0.1mm/1pixel precision. The program to correct for image alignment and determination of leaf positions requires a runtime of 21– 25 seconds for a single picket, and 44 – 46 seconds for a group of three pickets on a standard workstation CPU, 2.2 GHz Intel Core i7. Conclusion: MLC leaf edges were successfully found using techniques in computer vision. With the addition of computer vision techniques to the previously described autonomous QA system, the system is able to quickly perform complete QA routines with minimal human contribution.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TU-FG-BRB-08: Challenges, Limitations and Future Outlook Towards Clinical Translation of Proton Acoustic Range Verification

Purpose: To report our investigations of proton acoustic imaging, including computer simulations and preliminary experimental studies at clinical facilities. The ultimate achievable accuracy, sensitivity and clinical translation challenges are discussed. Methods: The acoustic pulse due to pressure rise was estimated using finite element model. Since the ionoacoustic pulse is highly dependent on the proton pulse width and energy, multiple pulse widths were studied. Based on the received signal spectrum at piezoelectric ultrasound transducer with consideration of random thermal noise, maximum spatial resolution of the proton-acoustic imaging modality was calculated. The simulation studies defined the design specifications of the system to detect proton acoustic signal from Hitachi and Mevion clinical machines. A 500 KHz hydrophone with 100 dB amplification was set up in a water tank placed in front of the proton nozzle A 40 MHz data acquisition was synchronized by a trigger signal provided by the machine. Results: Given 30–800 mGy dose per pulse at the Bragg peak, the minimum number of protons detectable by the proton acoustic technique was on the order of 10×10^6 per pulse. The broader pulse widths produce signal with lower acoustic frequencies, with 10 µs pulses producing signals with frequency less than 100 kHz. As the proton beam pulse width increases, a higher dose rate is required to measure the acoustic signal. Conclusion: We have established the minimal detection limit for protonacoustic range validation for a variety of pulse parameters. Our study indicated practical proton-acoustic range verification can be feasible with a pulse shorter than 10 µs, 5×10^6 protons/pulse, 50 nA beam current and a highly sensitive ultrasonic transducer. The translational challenges into current clinical machines include proper magnetic shielding of the measurement equipment, providing a clean trigger signal from the proton machine, providing a shorter proton beam pulse and higher dose per pulse.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TU-FG-BRB-12: Real-Time Visualization of Discrete Spot Scanning Proton Therapy Beam for Quality Assurance

Purpose: With the growing adoption of proton beam therapy there is an increasing need for effective and user-friendly tools for performing quality assurance (QA) measurements. The speed and versatility of spot-scanning proton beam (PB) therapy systems present unique challenges for traditional QA tools. To address these challenges a proof-of-concept system was developed to visualize, in real-time, the delivery of individual spots from a spot-scanning PB in order to perform QA measurements. Methods: The PB is directed toward a custom phantom with planar faces coated with a radioluminescent phosphor (Gd2O2s:Tb). As the proton beam passes through the phantom visible light is emitted from the coating and collected by a nearby CMOS camera. The images are processed to determine the locations at which the beam impinges on each face of the phantom. By so doing, the location of each beam can be determined relative to the phantom. The cameras are also used to capture images of the laser alignment system. The phantom contains x-ray fiducials so that it can be easily located with kV imagers. Using this data several quality assurance parameters can be evaluated. Results: The proof-of-concept system was able to visualize discrete PB spots with energies ranging from 70 MeV to 220 MeV. Images were obtained with integration times ranging from 20 to 0.019 milliseconds. If not limited by data transmission, this would correspond to a frame rate of 52,000 fps. Such frame rates enabled visualization of individual spots in real time. Spot locations were found to be highly correlated (R{sup 2}=0.99) with the nozzle-mounted spot position monitor indicating excellent spot positioning accuracy Conclusion: The system was shown to be capable of imaging individual spots for all clinical beam energies. Future development will focus on extending the image processing software to provide automated results for a variety of QA tests.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TU-H-CAMPUS-TeP3-03: Dose Enhancement by Gold Nanoparticles Around the Bragg Peak of Proton Beams

Purpose: To make clear the spatial distribution of dose enhancement around gold nanoparticles (GNPs) located near the proton Bragg peak, and to evaluate the potential of GNPs as a radio sensitizer. Methods: The dose enhancement by electrons emitted from GNPs under proton irradiation was estimated by Geant4 Monte Carlo simulation toolkit in two steps. In an initial macroscopic step, 100 and 195 MeV proton beams were incident on a water cube, 30 cm on a side. Energy distributions of protons were calculated at four depths, 50% and 75% proximal to the Bragg peak, 100% peak, and 75% distal to the peak (P50, P75, Peak, and D75, respectively). In a subsequent microscopic step, protons with the energy distribution calculated above were incident on a 20 nm diameter GNP in a nanometer-size water box and the spatial distribution of dose around the GNP was determined for each energy distribution. The dose enhancement factor (DEF) was also deduced. Results: The dose enhancement effect was spread to several tens of nanometers in the both depth and radial directions. The enhancement area increased in the order of P50, P75, Peak, and D75 for both cases with 100 and 195 MeV protons. In every position around the Bragg peak, the 100 MeV beam resulted in a higher dose enhancement than the 195 MeV beam. At P75, the average and maximum DEF were 3.9 and 17.0 for 100 MeV, and 3.5 and 16.2 for 195 MeV, respectively. These results indicate that lower energy protons caused higher dose enhancement in this incident proton energy range. Conclusion: The dose enhancement around GNPs spread as the position in the Bragg peak region becomes deeper and depends on proton energy. It is expected that GNPs can be used as a radio sensitizer with consideration of the location and proton beam energy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-AB-209-02: A New Inverse Planning Framework with Principle-Based Modeling of Inter-Structural Dosimetric Tradeoffs

Purpose: Traditional radiotherapy inverse planning relies on the weighting factors to phenomenologically balance the conflicting criteria for different structures. The resulting manual trial-and-error determination of the weights has long been recognized as the most time-consuming part of treatment planning. The purpose of this work is to develop an inverse planning framework that parameterizes the inter-structural dosimetric tradeoff among with physically more meaningful quantities to simplify the search for a clinically sensible plan. Methods: A permissible dosimetric uncertainty is introduced for each of the structures to balance their conflicting dosimetric requirements. The inverse planning is then formulated as a convex feasibility problem, which aims to generate plans with acceptable dosimetric uncertainties. A sequential procedure (SP) is derived to decompose the model into three submodels to constrain the uncertainty in the planning target volume (PTV), the critical structures, and all other structures to spare, sequentially. The proposed technique is applied to plan a liver case and a head-and-neck case and compared with a conventional approach. Results: Our results show that the strategy is able to generate clinically sensible plans with little trial-and-error. In the case of liver IMRT, the fractional volumes to liver and heart above 20Gy are found to be 22% and 10%, respectively, which are 15.1% and 33.3% lower than that of the counterpart conventional plan while maintaining the same PTV coverage. The planning of the head and neck IMRT show the same level of success, with the DVHs for all organs at risk and PTV very competitive to a counterpart plan. Conclusion: A new inverse planning framework has been established. With physically more meaningful modeling of the inter-structural tradeoff, the technique enables us to substantially reduce the need for trial-and-error adjustment of the model parameters and opens new opportunities of incorporating prior knowledge to facilitate the treatment planning process.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-AB-209-09: Optimization of Rotational Arc Station Parameter Optimized Radiation Therapy

Purpose: To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of improving VMAT in both plan quality and delivery efficiency. Methods: The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based Proximal Operator Graph Solver (POGS) within seconds. Apertures with zero or low weight were thrown out. To avoid being trapped in a local minimum, a stochastic gradient descent method was employed which also greatly increased the convergence rate of the objective function. The above procedure repeated until the plan could not be improved any further. A weighting factor associated with the total plan MU also indirectly controlled the complexities of aperture shapes. The number of apertures for VMAT and SPORT was confined to 180. The SPORT allowed the coexistence of multiple apertures in a single SP. The optimization technique was assessed by using three clinical cases (prostate, H&N and brain). Results: Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. Prostate case: the volume of the 50% prescription dose was decreased by 22% for the rectum. H&N case: SPORT improved the mean dose for the left and right parotids by 15% each. Brain case: the doses to the eyes, chiasm and inner ears were all improved. SPORT shortened the treatment time by ∼1 min for the prostate case, ∼0.5 min for brain case, and ∼0.2 min for the H&N case. Conclusion: The superior dosimetric quality and delivery efficiency presented here indicates that SPORT is an intriguing alternative treatment modality.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-AB-209-07: High Resolution X-Ray-Induced Acoustic Computed Tomography

Purpose: X-ray radiographic absorption imaging is an invaluable tool in medical diagnostics, biology and materials science. However, the use of conventional CT is limited by two factors: the detection sensitivity to weak absorption material and the radiation dose from CT scanning. The purpose of this study is to explore X-ray induced acoustic computed tomography (XACT), a new imaging modality, which combines X-ray absorption contrast and high ultrasonic resolution to address these challenges. Methods: First, theoretical models was built to analyze the XACT sensitivity to X-ray absorption and calculate the minimal radiation dose in XACT imaging. Then, an XACT system comprised of an ultrashort X-ray pulse, a low noise ultrasound detector and a signal acquisition system was built to evaluate the X-ray induced acoustic signal generation. A piece of chicken bone and a phantom with two golden fiducial markers were exposed to 270 kVp X-ray source with 60 ns exposure time, and the X-ray induced acoustic signal was received by a 2.25MHz ultrasound transducer in 200 positions. XACT images were reconstructed by a filtered back-projection algorithm. Results: The theoretical analysis shows that X-ray induced acoustic signals have 100% relative sensitivity to X-ray absorption, but not to X-ray scattering. Applying this innovative technology to breast imaging, we can reduce radiation dose by a factor of 50 compared with newly FDA approved breast CT. The reconstructed images of chicken bone and golden fiducial marker phantom reveal that the spatial resolution of the built XACT system is 350µm. Conclusion: In XACT, the imaging sensitivity to X-ray absorption is improved and the imaging dose is dramatically reduced by using ultrashort pulsed X-ray. Taking advantage of the high ultrasonic resolution, we can also perform 3D imaging with a single X-ray pulse. This new modality has the potential to revolutionize x-ray imaging applications in medicine and biology.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-CD-206-01: Expectation-Maximization Algorithm-Based Tissue Mixture Quantification for Perfusion MRI

Purpose: To investigate the feasibility of estimating the tissue mixture perfusions and quantifying cerebral blood flow change in arterial spin labeled (ASL) perfusion MR images. Methods: The proposed perfusion MR image analysis framework consists of 5 steps: (1) Inhomogeneity correction was performed on the T1- and T2-weighted images, which are available for each studied perfusion MR dataset. (2) We used the publicly available FSL toolbox to strip off the non-brain structures from the T1- and T2-weighted MR images. (3) We applied a multi-spectral tissue-mixture segmentation algorithm on both T1- and T2-structural MR images to roughly estimate the fraction of each tissue type - white matter, grey matter and cerebral spinal fluid inside each image voxel. (4) The distributions of the three tissue types or tissue mixture across the structural image array are down-sampled and mapped onto the ASL voxel array via a co-registration operation. (5) The presented 4-dimensional expectation-maximization (4D-EM) algorithm takes the down-sampled three tissue type distributions on perfusion image data to generate the perfusion mean, variance and percentage images for each tissue type of interest. Results: Experimental results on three volunteer datasets demonstrated that the multi-spectral tissue-mixture segmentation algorithm was effective to initialize tissue mixtures from T1- and T2-weighted MR images. Compared with the conventional ASL image processing toolbox, the proposed 4D-EM algorithm not only generated comparable perfusion mean images, but also produced perfusion variance and percentage images, which the ASL toolbox cannot obtain. It is observed that the perfusion contribution percentages may not be the same as the corresponding tissue mixture volume fractions estimated in the structural images. Conclusion: A specific application to brain ASL images showed that the presented perfusion image analysis method is promising for detecting subtle changes in tissue perfusions, which is valuable for the early diagnosis of certain brain diseases, e.g. multiple sclerosis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-CD-206-05: Machine-Learning Based Segmentation of Organs at Risks for Head and Neck Radiotherapy Planning

Purpose: Accurate and efficient delineation of tumor target and organs-at-risks is essential for the success of radiotherapy. In reality, despite of decades of intense research efforts, auto-segmentation has not yet become clinical practice. In this study, we present, for the first time, a deep learning-based classification algorithm for autonomous segmentation in head and neck (HaN) treatment planning. Methods: Fifteen HN datasets of CT, MR and PET images with manual annotation of organs-at-risk (OARs) including spinal cord, brainstem, optic nerves, chiasm, eyes, mandible, tongue, parotid glands were collected and saved in a library of plans. We also have ten super-resolution MR images of the tongue area, where the genioglossus and inferior longitudinalis tongue muscles are defined as organs of interest. We applied the concepts of random forest- and deep learning-based object classification for automated image annotation with the aim of using machine learning to facilitate head and neck radiotherapy planning process. In this new paradigm of segmentation, random forests were used for landmark-assisted segmentation of super-resolution MR images. Alternatively to auto-segmentation with random forest-based landmark detection, deep convolutional neural networks were developed for voxel-wise segmentation of OARs in single and multi-modal images. The network consisted of three pairs of convolution and pooing layer, one RuLU layer and a softmax layer. Results: We present a comprehensive study on using machine learning concepts for auto-segmentation of OARs and tongue muscles for the HaN radiotherapy planning. An accuracy of 81.8% in terms of Dice coefficient was achieved for segmentation of genioglossus and inferior longitudinalis tongue muscles. Preliminary results of OARs regimentation also indicate that deep-learning afforded an unprecedented opportunities to improve the accuracy and robustness of radiotherapy planning. Conclusion: A novel machine learning framework has been developed for image annotation and structure segmentation. Our results indicate the great potential of deep learning in radiotherapy treatment planning.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-CD-207B-07: Noise Modeling of Single Photon Avalanche Diode (SPAD) for Photon Counting CT Applications

Purpose: Silicon photomultiplier (SiPM) has recently emerged as a promising photodetector for biomedical imaging applications. Due to its high multiplication gain (comparable to PMT), fast timing, low cost and compactness, it is considered a good candidate for photon counting CT. Dark noise is a limiting factor which impacts both energy resolution and detection dynamic range. Our goal is to develop a comprehensive model for noise sources for SiPM sensors. Methods: The physical parameters used in this work were based upon a test SPAD fabricated in 130nm CMOS process. The SPAD uses an n+/p-well junction, which is isolated from the p-substrate by a deep n-well junction. Inter-avalanche time measurement was used to record the time interval between two adjacent avalanche pulses. After collecting 1×106 counts, the histogram was obtained and multiple exponential fitting process was used to extract the lifetime associated with the traps within the bandgap. Results: At room temperature, the breakdown voltage of the SPAD is ∼11.4V and shows a temperature coefficient of 7.7mV/°C. The dark noise of SPAD increases with both the excess biasing voltage and temperature. The primary dark counts from the model were validated against the measurement results. A maximum relative error of 8.7% is observed at 20 °C with an excess voltage of 0.5V. The probabilities of after-pulsing are found to be dependent of both temperature and excess voltage. With 0.5V excess voltage, the after-pulsing probability is 63.5% at - 30 °C and drops to ∼6.6% at 40 °C. Conclusion: A comprehensive noise model for SPAD sensor was proposed. The model takes into account of static, dynamic and statistical behavior of SPADs. We believe that this is the first SPAD circuit simulation model that includes the band-to-band tunneling dark noise contribution and temporal dependence of the after-pulsing probability.

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