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At least 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

WE-AB-207A-07: A Planning CT-Guided Scatter Artifact Correction Method for CBCT Images

Purpose: Cone beam computed tomography (CBCT) imaging is on increasing demand for high-performance image-guided radiotherapy such as online tumor delineation and dose calculation. However, the current CBCT imaging has severe scatter artifacts and its current clinical application is therefore limited to patient setup based mainly on the bony structures. This study’s purpose is to develop a CBCT artifact correction method. Methods: The proposed scatter correction method utilizes the planning CT to improve CBCT image quality. First, an image registration is used to match the planning CT with the CBCT to reduce the geometry difference between the two images. Then, the planning CT-based prior information is entered into the Bayesian deconvolution framework to iteratively perform a scatter artifact correction for the CBCT mages. This technique was evaluated using Catphan phantoms with multiple inserts. Contrast-to-noise ratios (CNR) and signal-to-noise ratios (SNR), and the image spatial nonuniformity (ISN) in selected volume of interests (VOIs) were calculated to assess the proposed correction method. Results: Post scatter correction, the CNR increased by a factor of 1.96, 3.22, 3.20, 3.46, 3.44, 1.97 and 1.65, and the SNR increased by a factor 1.05, 2.09, 1.71, 3.95, 2.52, 1.54 and 1.84 for the Air, PMP, LDPE, Polystryrene, Acrylic, Delrin and Teflon inserts, respectively. The ISN decreased from 21.1% to 4.7% in the corrected images. All values of CNR, SNR and ISN in the corrected CBCT image were much closer to those in the planning CT images. The results demonstrated that the proposed method reduces the relevant artifacts and recovers CT numbers. Conclusion: We have developed a novel CBCT artifact correction method based on CT image, and demonstrated that the proposed CT-guided correction method could significantly reduce scatter artifacts and improve the image quality. This method has great potential to correct CBCT images allowing its use in adaptive radiotherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

High-throughput terahertz imaging: progress and challenges

Abstract Many exciting terahertz imaging applications, such as non-destructive evaluation, biomedical diagnosis, and security screening, have been historically limited in practical usage due to the raster-scanning requirement of imaging systems, which impose very low imaging speeds. However, recent advancements in terahertz imaging systems have greatly increased the imaging throughput and brought the promising potential of terahertz radiation from research laboratories closer to real-world applications. Here, we review the development of terahertz imaging technologies from both hardware and computational imaging perspectives. We introduce and compare different types of hardware enabling frequency-domain and time-domain imaging using various thermal, photon, and field image sensor arrays. We discuss how different imaging hardware and computational imaging algorithms provide opportunities for capturing time-of-flight, spectroscopic, phase, and intensity image data at high throughputs. Furthermore, the new prospects and challenges for the development of future high-throughput terahertz imaging systems are briefly introduced.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Scaling deep learning for material imaging with a pseudo 3D model for domain transfer

The recent introduction of deep learning methods for image processing has greatly advanced the characterization of materials using three-dimensional (3D) X-ray imaging techniques. However, deep learning models often have difficulty performing consistently across images owing to unavoidable variations in imaging conditions, which create inconsistencies even for the same material. As a result, networks must frequently be retrained for new datasets, limiting their applicability and generalization. Thus, it is critical to reduce the variations between images to enable a single model to process multiple datasets. Herein, we introduce P3T-Net, a pseudo-3D domain transfer network that transfers diverse 3D images into a uniform domain before processing using deep learning models. Remarkably, P3T-Net enables the reuse of previously trained networks for processing new images and considerably reduces the computational cost of transferring 3D images across domains. These unique capabilities were demonstrated in the following scenarios: (i) image enhancement of fast scans for geological rock and hydrogen fuel cells, (ii) enhancement of images to match the quality of multi-source imaging for lithium-ion batteries, (iii) accurate segmentation of images captured under different conditions, and (iv) tera-scale 3D transfer (10 11 voxels) on a single GPU. Overall, the proposed approach addresses cross-domain inconsistencies across various materials and conditions, thereby enabling more robust and generalizable deep learning solutions for a wide range of material imaging tasks.

25 ENERGY STORAGE↗
DOE OSTI2021

Background-free dual-mode optical and 13 C magnetic resonance imaging in diamond particles

Multimodal imaging—the ability to acquire images of an object through more than one imaging mode simultaneously—has opened additional perspectives in areas ranging from astronomy to medicine. In this paper, we report progress toward combining optical and magnetic resonance (MR) imaging in such a “dual” imaging mode. They are attractive in combination because they offer complementary advantages of resolution and speed, especially in the context of imaging in scattering environments. Our approach relies on a specific material platform, microdiamond particles hosting nitrogen vacancy (NV) defect centers that fluoresce brightly under optical excitation and simultaneously “hyperpolarize” lattice C 13 nuclei, making them bright under MR imaging. We highlight advantages of dual-mode optical and MR imaging in allowing background-free particle imaging and describe regimes in which either mode can enhance the other. Leveraging the fact that the two imaging modes proceed in Fourier-reciprocal domains (real and k-space), we propose a sampling protocol that accelerates image reconstruction in sparse-imaging scenarios. Our work suggests interesting possibilities for the simultaneous optical and low-field MR imaging of targeted diamond nanoparticles.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Associated Particle Imaging of Neutron Inelastic Scatter: 3-D Reconstruction, Capabilities, and Challenges

Associated particle imaging (API) offers unique advantages for 3-D imaging of neutron inelastic scatter, including single-view tomographic imaging and data acquisition when access is limited to only one side of the interrogated object. However, widespread adoption of neutron inelastic scatter imaging has been impeded by several inherent challenges, most prominently spatial resolution, self-attenuation, and statistical noise. Here, in this work, the capabilities and challenges of neutron inelastic scatter imaging are investigated. Instead of focusing on a single imaging application, we identify imaging principles that hold for various neutron inelastic scatter imaging techniques. The primary challenges for 3-D imaging are characterized. The inherent spatial resolution in the time-of-flight (TOF) dimension is derived based on the known system timing resolution and scan geometry. Three reconstruction algorithms are described and demonstrated, including the introduction of modern iterative reconstruction incorporating a physics-based system model. Simulation is leveraged to demonstrate imaging capability with varying coincidence count levels and system timing resolution. An example of measured data with both back-scatter and forward-scatter detector positioning is presented. System design characteristics and their effects on image quality are identified. The imaging framework presented in this article has the potential to facilitate growth of 3-D neutron inelastic scatter API by identifying applications that are a good match for the technique and by targeting system development resources toward the requirements of a specific imaging task.

Associated particle imaging (API)↗
DOE OSTI2018

Machine learning to classify animal species in camera trap images: Applications in ecology

Motion-activated cameras (“camera traps”) are increasingly used in ecological and management studies for remotely observing wildlife and are amongst the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analysed, typically by visually observing each image, in order to extract data that can be used in ecological analyses. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or “out-of-distribution” in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an r package (Machine Learning for Wildlife Image Classification) that allows the users to (a) use the trained model presented here and (b) train their own model using classified images of wildlife from their studies. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analysing images. Our r package makes these methods accessible to ecologists.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SU-E-I-39: Molecular Image Guided Cancer Stem Cells Therapy

Purpose: Cancer stem cells resistance to radiation is a problematic issue that has caused a big fail in cancer treatment. Methods: As a primary work, molecular imaging can indicate the main mechanisms of radiation resistance of cancer stem cells. By developing and commissioning new probes and nanomolecules and biomarkers, radiation scientist will able to identify the essential pathways of radiation resistance of cancer stem cells. As the second solution, molecular imaging is a best way to find biological target volume and delineate cancer stem cell tissues. In the other hand, by molecular imaging techniques one can image the treatment response in tumor and also in normal tissue. In this issue, the response of cancer stem cells to radiation during therapy course can be imaged, also the main mechanisms of radiation resistance and finding the best radiation modifiers (sensitizers) can be achieved by molecular imaging modalities. In adaptive radiotherapy the molecular imaging plays a vital role to have higher tumor control probability by delivering high radiation doses to cancer stem cells in any time of treatment. The outcome of a feasible treatment is dependent to high cancer stem cells response to radiation and removing all of which, so a good imaging modality can show this issue and preventing of tumor recurrence and metastasis. Results: Our results are dependent to use of molecular imaging as a new modality in the clinic. We propose molecular imaging as a new radiobiological technique to solve radiation therapy problems due to cancer stem cells. Conclusion: Molecular imaging guided cancer stem cell diagnosis and therapy is a new approach in the field of cancer treatment. This new radiobiological imaging technique should be developed in all clinics as a feasible tool that is more biological than physical imaging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-J-30: Benchmark Image-Based TCP Calculation for Evaluation of PTV Margins for Lung SBRT Patients

Purpose: Tumor control probability (TCP) calculated with accumulated radiation doses may help design appropriate treatment margins. Image registration errors, however, may compromise the calculated TCP. The purpose of this study is to develop benchmark CT images to quantify registration-induced errors in the accumulated doses and their corresponding TCP. Methods: 4DCT images were registered from end-inhale (EI) to end-exhale (EE) using a “demons” algorithm. The demons DVFs were corrected by an FEM model to get realistic deformation fields. The FEM DVFs were used to warp the EI images to create the FEM-simulated images. The two images combined with the FEM DVF formed a benchmark model. Maximum intensity projection (MIP) images, created from the EI and simulated images, were used to develop IMRT plans. Two plans with 3 and 5 mm margins were developed for each patient. With these plans, radiation doses were recalculated on the simulated images and warped back to the EI images using the FEM DVFs to get the accumulated doses. The Elastix software was used to register the FEM-simulated images to the EI images. TCPs calculated with the Elastix-accumulated doses were compared with those generated by the FEM to get the TCP error of the Elastix registrations. Results: For six lung patients, the mean Elastix registration error ranged from 0.93 to 1.98 mm. Their relative dose errors in PTV were between 0.28% and 6.8% for 3mm margin plans, and between 0.29% and 6.3% for 5mm-margin plans. As the PTV margin reduced from 5 to 3 mm, the mean TCP error of the Elastix-reconstructed doses increased from 2.0% to 2.9%, and the mean NTCP errors decreased from 1.2% to 1.1%. Conclusion: Patient-specific benchmark images can be used to evaluate the impact of registration errors on the computed TCPs, and may help select appropriate PTV margins for lung SBRT patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-J-183: Quantifying the Image Quality and Dose Reduction of Respiratory Triggered 4D Cone-Beam Computed Tomography with Patient- Measured Breathing

Purpose: Respiratory triggered four dimensional cone-beam computed tomography (RT 4D CBCT) is a novel technique that uses a patient's respiratory signal to drive the image acquisition with the goal of imaging dose reduction without degrading image quality. This work investigates image quality and dose using patient-measured respiratory signals for RT 4D CBCT simulations instead of synthetic sinusoidal signals used in previous work. Methods: Studies were performed that simulate a 4D CBCT image acquisition using both the novel RT 4D CBCT technique and a conventional 4D CBCT technique from a database of oversampled Rando phantom CBCT projections. A database containing 111 free breathing lung cancer patient respiratory signal files was used to create 111 RT 4D CBCT and 111 conventional 4D CBCT image datasets from realistic simulations of a 4D RT CBCT system. Each of these image datasets were compared to a ground truth dataset from which a root mean square error (RMSE) metric was calculated to quantify the degradation of image quality. The number of projections used in each simulation is counted and was assumed as a surrogate for imaging dose. Results: Based on 111 breathing traces, when comparing RT 4D CBCT with conventional 4D CBCT the average image quality was reduced by 7.6%. However, the average imaging dose reduction was 53% based on needing fewer projections (617 on average) than conventional 4D CBCT (1320 projections). Conclusion: The simulation studies using a wide range of patient breathing traces have demonstrated that the RT 4D CBCT method can potentially offer a substantial saving of imaging dose of 53% on average compared to conventional 4D CBCT in simulation studies with a minimal impact on image quality. A patent application (PCT/US2012/048693) has been filed which is related to this work.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TU-F-18A-02: Iterative Image-Domain Decomposition for Dual-Energy CT

Purpose: Dual energy CT (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Direct decomposition via matrix inversion suffers from significant degradation of image signal-to-noise ratios, which reduces clinical value. Existing de-noising algorithms achieve suboptimal performance since they suppress image noise either before or after the decomposition and do not fully explore the noise statistical properties of the decomposition process. We propose an iterative image-domain decomposition method for noise suppression in DECT, using the full variance-covariance matrix of the decomposed images. Methods: The proposed algorithm is formulated in the form of least-square estimation with smoothness regularization. It includes the inverse of the estimated variance-covariance matrix of the decomposed images as the penalty weight in the least-square term. Performance is evaluated using an evaluation phantom (Catphan 600) and an anthropomorphic head phantom. Results are compared to those generated using direct matrix inversion with no noise suppression, a de-noising method applied on the decomposed images, and an existing algorithm with similar formulation but with an edge-preserving regularization term. Results: On the Catphan phantom, our method retains the same spatial resolution as the CT images before decomposition while reducing the noise standard deviation of decomposed images by over 98%. The other methods either degrade spatial resolution or achieve less low-contrast detectability. Also, our method yields lower electron density measurement error than direct matrix inversion and reduces error variation by over 97%. On the head phantom, it reduces the noise standard deviation of decomposed images by over 97% without blurring the sinus structures. Conclusion: We propose an iterative image-domain decomposition method for DECT. The method combines noise suppression and material decomposition into an iterative process and achieves both goals simultaneously. The proposed algorithm shows superior performance on noise suppression with high image spatial resolution and low-contrast detectability. This work is supported by a Varian MRA grant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

TH-A-18C-10: Dynamic Intensity Weighted Region of Interest Imaging

Purpose: For image guidance tasks full image quality is not required throughout the entire image. With dynamic filtration of the kV imaging beam the noise properties of the CT image can be locally controlled, providing a high quality image around the target volume with a lower quality surrounding region while providing substantial dose sparing to the patient as well as reduced scatter fluence on the detector. Methods: A dynamic collimation device with 3mm copper blades has been designed to mount in place of the bowtie filter on the On-Board Imager (Varian Medical Systems). The beam intensity is reduced by 95% behind the copper filters and the aperture is controlled dynamically to conformally illuminate a given ROI during a standard cone-beam CT scan. A data correction framework to account for the physical effects of the collimator prior to reconstruction was developed. Furthermore, to determine the dose savings and scatter reduction a monte carlo model was built in BEAMnrc with specifics from the Varian Monte Carlo Data Package. The MC model was validated with Gafchromic film. Results: The reconstructed image shows image quality comparable to a standard scan in the specified ROI, with higher noise and streaks in the outer region but still sufficient information for alignment to high contrast structures. The monte carlo modeling showed that the scatter-to-primary ratio was reduced from 1.26 for an unfiltered scan to 0.45 for an intensity weighted scan, suggesting that image quality may be improved in the inner ROI. Dose in the inner region was reduced 10–15% due to reduced scatter and by as much as 75% in the outer region. Conclusion: Dynamic intensity-weighted ROI imaging allows reduction of imaging dose to sensitive organs away from the target region while providing images that retain their utility for patient setup and procedure guidance. Funding was provided in part by Varian Medical Systems and NIH Grants 1RO1CA120540, T32EB002103, S10 RR021039 and P30 CA14599. The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of any of the supporting organizations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

3D high spectral and spatial resolution imaging of ex vivo mouse brain

Purpose: Widely used MRI methods show brain morphology both in vivo and ex vivo at very high resolution. Many of these methods (e.g., T{sub 2}{sup *}-weighted imaging, phase-sensitive imaging, or susceptibility-weighted imaging) are sensitive to local magnetic susceptibility gradients produced by subtle variations in tissue composition. However, the spectral resolution of commonly used methods is limited to maintain reasonable run-time combined with very high spatial resolution. Here, the authors report on data acquisition at increased spectral resolution, with 3-dimensional high spectral and spatial resolution MRI, in order to analyze subtle variations in water proton resonance frequency and lineshape that reflect local anatomy. The resulting information compliments previous studies based on T{sub 2}{sup *} and resonance frequency. Methods: The proton free induction decay was sampled at high resolution and Fourier transformed to produce a high-resolution water spectrum for each image voxel in a 3D volume. Data were acquired using a multigradient echo pulse sequence (i.e., echo-planar spectroscopic imaging) with a spatial resolution of 50 × 50 × 70 μm{sup 3} and spectral resolution of 3.5 Hz. Data were analyzed in the spectral domain, and images were produced from the various Fourier components of the water resonance. This allowed precise measurement of local variations in water resonance frequency and lineshape, at the expense of significantly increased run time (16–24 h). Results: High contrast T{sub 2}{sup *}-weighted images were produced from the peak of the water resonance (peak height image), revealing a high degree of anatomical detail, specifically in the hippocampus and cerebellum. In images produced from Fourier components of the water resonance at −7.0 Hz from the peak, the contrast between deep white matter tracts and the surrounding tissue is the reverse of the contrast in water peak height images. This indicates the presence of a shoulder in the water resonance that is not present at +7.0 Hz and may be specific to white matter anatomy. Moreover, a frequency shift of 6.76 ± 0.55 Hz was measured between the molecular and granular layers of the cerebellum. This shift is demonstrated in corresponding spectra; water peaks from voxels in the molecular and granular layers are consistently 2 bins apart (7.0 Hz, as dictated by the spectral resolution) from one another. Conclusions: High spectral and spatial resolution MR imaging has the potential to accurately measure the changes in the water resonance in small voxels. This information can guide optimization and interpretation of more commonly used, more rapid imaging methods that depend on image contrast produced by local susceptibility gradients. In addition, with improved sampling methods, high spectral and spatial resolution data could be acquired in reasonable run times, and used for in vivo scans to increase sensitivity to variations in local susceptibility.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Cardiac motion correction based on partial angle reconstructed images in x-ray CT

Purpose: Cardiac x-ray CT imaging is still challenging due to heart motion, which cannot be ignored even with the current rotation speed of the equipment. In response, many algorithms have been developed to compensate remaining motion artifacts by estimating the motion using projection data or reconstructed images. In these algorithms, accurate motion estimation is critical to the compensated image quality. In addition, since the scan range is directly related to the radiation dose, it is preferable to minimize the scan range in motion estimation. In this paper, the authors propose a novel motion estimation and compensation algorithm using a sinogram with a rotation angle of less than 360°. The algorithm estimates the motion of the whole heart area using two opposite 3D partial angle reconstructed (PAR) images and compensates the motion in the reconstruction process. Methods: A CT system scans the thoracic area including the heart over an angular range of 180° + α + β, where α and β denote the detector fan angle and an additional partial angle, respectively. The obtained cone-beam projection data are converted into cone-parallel geometry via row-wise fan-to-parallel rebinning. Two conjugate 3D PAR images, whose center projection angles are separated by 180°, are then reconstructed with an angular range of β, which is considerably smaller than a short scan range of 180° + α. Although these images include limited view angle artifacts that disturb accurate motion estimation, they have considerably better temporal resolution than a short scan image. Hence, after preprocessing these artifacts, the authors estimate a motion model during a half rotation for a whole field of view via nonrigid registration between the images. Finally, motion-compensated image reconstruction is performed at a target phase by incorporating the estimated motion model. The target phase is selected as that corresponding to a view angle that is orthogonal to the center view angles of two conjugate PAR images. To evaluate the proposed algorithm, digital XCAT and physical dynamic cardiac phantom datasets are used. The XCAT phantom datasets were generated with heart rates of 70 and 100 bpm, respectively, by assuming a system rotation time of 300 ms. A physical dynamic cardiac phantom was scanned using a slowly rotating XCT system so that the effective heart rate will be 70 bpm for a system rotation speed of 300 ms. Results: In the XCAT phantom experiment, motion-compensated 3D images obtained from the proposed algorithm show coronary arteries with fewer motion artifacts for all phases. Moreover, object boundaries contaminated by motion are well restored. Even though object positions and boundary shapes are still somewhat different from the ground truth in some cases, the authors see that visibilities of coronary arteries are improved noticeably and motion artifacts are reduced considerably. The physical phantom study also shows that the visual quality of motion-compensated images is greatly improved. Conclusions: The authors propose a novel PAR image-based cardiac motion estimation and compensation algorithm. The algorithm requires an angular scan range of less than 360°. The excellent performance of the proposed algorithm is illustrated by using digital XCAT and physical dynamic cardiac phantom datasets.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-I-40: Phantom Research On Monochromatic Images Taken by Dual CBCT with Multiple Energy Sets

Purpose: To evaluate the quality of monochromatic images at the same virtual monochromatic energy using dual cone-beam computed tomography (CBCT) with either kV/kV or MV/kV or MV/MV energy sets. Methods: CT images of Catphan 504 phantom were acquired using four different KV and MV settings: 80kV, 140kV, 4MV, 6MV. Three sets of monochromatic images were calculated: 80kV-140kV, 140kV-4MV and 4MV-6MV. Each set of CBCT images were reconstructed from the same selected virtual monochromatic energy of 1MeV. Contrast-to-Noise Ratios (CNRs) were calculated and compared between each pair of images with different energy sets. Results: Between kV/MV and MV/MV images, the CNRs are comparable for all inserts. However, differences of CNRs were observed between the kV/kV and kV/MV images. Delrin’s CNR ratio between kV/kV image and kV/MV image is 1.634. LDPE’s (Low-Density Polyethylene) CNR ratio between kV/kV and kV/MV images is 0.509. Polystyrene’s CNR ratio between kV/kV image and kV/MV image is 2.219. Conclusion: Preliminary results indicated that the CNRs calculated from CBCT images reconstructed from either kV/MV projections or MV/MV projections for the same selected virtual monochromatic energy may be comparable.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-D-213-06: Quantitative Image Quality Metrics Are for Physicists, Not Radiologists: How to Communicate to Your Radiologists Using Their Language

Purpose: A framework for explaining differences in image quality to non-technical audiences in medial imaging is needed. Currently, this task is something that is learned “on the job.” The lack of a formal methodology for communicating optimal acquisition parameters into the clinic effectively mitigates many technological advances. As a community, medical physicists need to be held responsible for not only advancing image science, but also for ensuring its proper use in the clinic. This work outlines a framework that bridges the gap between the results from quantitative image quality metrics like detectability, MTF, and NPS and their effect on specific anatomical structures present in diagnostic imaging tasks. Methods: Specific structures of clinical importance were identified for a body, an extremity, a chest, and a temporal bone protocol. Using these structures, quantitative metrics were used to identify the parameter space that should yield optimal image quality constrained within the confines of clinical logistics and dose considerations. The reading room workflow for presenting the proposed changes for imaging each of these structures is presented. The workflow consists of displaying images for physician review consisting of different combinations of acquisition parameters guided by quantitative metrics. Examples of using detectability index, MTF, NPS, noise and noise non-uniformity are provided. During review, the physician was forced to judge the image quality solely on those features they need for diagnosis, not on the overall “look” of the image. Results: We found that in many cases, use of this framework settled mis-agreements between physicians. Once forced to judge images on the ability to detect specific structures inter reader agreement was obtained. Conclusion: This framework will provide consulting, research/industrial, or in-house physicists with clinically relevant imaging tasks to guide reading room image review. This framework avoids use of the overall “look” or “feel” to dictate acquisition parameter selection. Equipment grants GE Healthcare.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-F-CAMPUS-J-05: Verification for Prompt Gamma Ray Imaging During Proton Boron Fusion Therapy

Purpose: The purpose of this study is to verify the acquisition of the three dimensional single photon emission computed tomography (SPECT) image using prompt gamma ray originated from proton boron fusion therapy (PBFT). Methods: The real-time imaging system during the PBFT was simulated to acquire the tomographic image of the prompt gamma ray from treated tumor by the proton boron reaction, using Monte Carlo simulation (MCNPX). We acquired percentage depth dose (PDD) of the proton beam in the water phantom including the boron uptake region (BUR), the energy spectra of the prompt gamma ray and tomographic image which can show treated tumor regions. The prompt gamma ray image was reconstructed using maximum likelihood estimation maximization (MLEM) reconstruction algorithm with 64 projections. In addition, in order to evaluate the reconstructed image, the image profiles between BURs were extracted from the image. Results: In the PDD results, the Bragg-peak was amplified definitely when the proton’s maximum dose level was located at the BUR. This amplification is based on the generation of alpha particles. In addition, the prompt gamma ray peak of 719 keV was observed from the energy spectrum. Through the previous process, the tomographic image of prompt gamma ray from the BUR was reconstructed. The line profile was extracted from image including BURs, and it shows high signal-to-noise ratio. Conclusion: We confirmed that the real-time prompt gamma ray image during the PBFT was successfully deducted, and results of quantitative image analysis show good agreement with the original pattern of the BUR. This study first verified the imaging capability of the PBFT-SPECT system. In conclusion, the PBFT-SPECT system can realize the treated tumor monitoring during the PBFT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-214: Dose Reduction by Spatially Optimized Image Quality Via Fluence Modulated Proton CT (FMpCT)

Purpose: Proton CT (pCT) is a promising imaging modality for reducing range uncertainty in image-guided proton therapy. Range uncertainties partially originate from X-ray CT number conversion to stopping power ratio (SPR) and are limiting the exploitation of the full potential of proton therapy. In this study we explore the concept of spatially dependent fluence modulated proton CT (FMpCT), for achieving optimal image quality in a clinical region of interest (ROI), while reducing significantly the imaging dose to the patient. Methods: The study was based on simulated ideal pCT using pencil beam (PB) scanning. A set of 250 MeV protons PBs was used to create 360 projections of a cylindrical water phantom and a head and neck cancer patient. The tomographic images were reconstructed using a filtered backprojection (FBP) as well as an iterative algorithm (ITR). Different fluence modulation levels were investigated and their impact on the image was quantified in terms of SPR accuracy as well as noise within and outside selected ROIs, as a function of imaging dose. The unmodulated image served as reference. Results: Both FBP reconstruction and ITR without total variation (TV) yielded image quality in the ROIs similar to the reference images, for modulation down to 0.1 of the full proton fluence. The average dose was reduced by 75% for the water phantom and by 40% for the patient. FMpCT does not improve the noise for ITR with TV and modulation 0.1. Conclusion: This is the first work proposing and investigating FMpCT for producing optimal image quality for treatment planning and image guidance, while simultaneously reducing imaging dose. Future work will address spatial resolution effects and the impact of FMpCT on the quality of proton treatment plans for a prototype pCT scanner capable of list mode data acquisition. Acknowledgement: DFG-MAP DFG - Munich-Centre for Advanced Photonics (MAP)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-TeP4-13: Interfraction Treatment Monitoring Using Integrated Invivo EPID Images

Purpose: To investigate inter-fraction differences of dose delivery by analyzing portal images acquired during treatment and implement an automated system to generate a report for each fraction. Large differences in images between fractions can alert the physicist of possible machine performance issues or patient set-up errors. Methods: A Varian Novalis Tx equipped with a HD120 MLC and aS1000 electronic portal imaging device (EPID) was used in our study. EPID images are acquired in continuous acquisition mode for 32 volumetric arc therapy (VMAT) patients. The images are summed to create an image for each arc and a single image for each fraction. The first fraction is designated as the reference unless a machine error prevented acquisition of all images. The images for each beam as well as the fraction image are compared using gamma analysis at 1%/1mm, 2%/2mm and 3%/3mm. A report is then generated using an in house MatLab program containing the comparison for the current fraction as well as a history of previous fractions. The reports are automatically sent via email to the physicist for review. Fractions in which the total number of images was not within 5% of the reference number of images were not included in the results. Results: 91 of the 182 fractions recorded an image count within 5% of the reference. Gamma averages over all fractions and patients were 96.2% ±0.8% at 3%/3mm, 92.9% ±1% at 2%/2mm and 80.6% ±1.8% at 1%/1mm. The SD between fractions for each patient ranged from .004% to 10.4%. Of the 91 fractions 3 flagged due to low gamma values. After further investigation no significant errors were found. Conclusion: This toolkit can be used for in-vivo monitoring of treatment plan delivery an alert the physics staff of any inter-fraction discrepancies that may require further investigation.

60 APPLIED LIFE SCIENCES↗