bluuebunny/crossref_metadata_2025_split huggingface dataset:
MO‐FG‐BRA‐06: Material Optimisation for Nanoparticle‐Sensitized Radiotherapy
Authors: McMahon S , Paganetti H , Prise K 
Published: June 2015

Purpose:To evaluate the potential applications of nanoparticles of different elemental compositions as X‐ray radiosensitizers.Methods:Geant4 Monte Carlo models were used to model the interactions of 10 nm radius nanoparticles of elements ranging from silicon to mercury with either monoenergetic keV X‐rays or a 6 MV Linac spectrum. Secondary electron spectra emitted from nanoparticles and resulting radial doses were calculated. Potential biological impacts were evaluated through the use of the Local Effect Model.Results:For keV radiation, fluoresence photons and energetic photoelectrons deposit the majority of energy emitted by nanoparticles, over long ranges. However, short‐range energy deposition is dominated by Auger electrons. While total energy deposition increases with atomic number, short‐range energy deposition has a more complex behaviour, with local maxima around germanium (Z=32) and gadolinium (Z=64). For megavoltage radiation, Compton electrons deposit the majority of energy, but short‐ range depositions are dominated by interactions with secondary electrons. As a Result, energy deposition following ionising events following Linac irradiation is largely independent of atomic number.When analysed through the Local Effect Model, these effects translate into significant variations in predicted cell killing with atomic number for keV exposures, but comparatively limited effects for megavoltage exposures.Conclusions:Although heavier elements typically lead to greater increases in dose within target volumes, on the nanometre scale effects are typically dominated by low‐energy secondary electrons, whose distribution has a complex relationship with both nanoparticle composition and beam energy.These results suggest that maximum radiosensitization may not necessarily be achieved through maximizing atomic number, but rather a balance between overall interaction cross‐section and local dose distribution. The different behaviours seen when comparing keV and MV behaviours may also provide future mechanisms for separating physical and biological mechanisms of nanoparticle sensitisation, a key step in their development as a potential therapeutic agent.
MO‐FG‐BRA‐08: A Preliminary Study of Gold Nanoparticles Enhanced Diffuse Optical Tomography
Authors: Xu K , Dogan N , Yang Y 
Published: June 2015

Purpose:To develop an imaging method by using gold nanoparticles (GNP) to enhance diffuse optical tomography (DOT) for better tumor detection.Methods:Experiments were performed on a tissue‐simulating cylindrical optical phantom (30mm diameter, 60mm length). The GNP used are gold nanorods (10nm diameter, 44nm length) with peak light absorption at 840nm. 0.085ml GNP colloid of 96nM concentration was loaded into a 6mm diameter cylindrical hole in the phantom. An 856nm laser beam (14mW) was used as light source to irradiate the phantom at multiple locations through rotating and elevating the phantom. A CCD camera captured the light transmission through the phantom for each irradiation with total 40 projections (8 rotation angles in 45degree steps and 5 elevations with 3mm apart). Cone beam CT of the phantom was used to generate the three‐dimensional mesh for DOT reconstruction and to identify the true location of the GNP volume. A forward simulation was performed with known phantom optical properties to establish a relationship between the absorption coefficient and concentration of the GNP by matching the simulated and measured transmission. DOT image reconstruction was performed to restore the GNP within the phantom. In addition, a region‐constrained reconstruction was performed by confining the solutions within the GNP volume detected from CT.Results:The position of the GNP volume was reconstructed with <2mm error. The reconstructed average GNP concentration within an identical volume was 104nM, 8% difference from the truth. When the CT was used as “a priori”, the reconstructed average GNP concentration was 239nM, about 2.5 times of the true concentration.Conclusion:This study is the first to demonstrate GNP enhanced DOT with phantom imaging. The GNP can be differentiated from their surrounding background. However, the reconstruction methods needs to be improved for better spatial and quantification accuracy.
MO‐FG‐303‐03: Demonstration of Universal Knowledge‐Based 3D Dose Prediction
Authors: Shiraishi S , Moore K L 
Published: June 2015

Purpose:To demonstrate a knowledge‐based 3D dose prediction methodology that can accurately predict achievable radiotherapy distributions.Methods:Using previously treated plans as input, an artificial neural network (ANN) was trained to predict 3D dose distributions based on 14 patient‐specific anatomical parameters including the distance (r) to planning target volume (PTV) boundary, organ‐at‐risk (OAR) boundary distances, and angular position ( θ,φ). 23 prostate and 49 stereotactic radiosurgery (SRS) cases with ≥1 nearby OARs were studied. All were planned with volumetric‐modulated arc therapy (VMAT) to prescription doses of 81Gy for prostate and 12–30Gy for SRS. Site‐specific ANNs were trained using all prostate 23 plans and using a 24 randomly‐selected subset for the SRS model. The remaining 25 SRS plans were used to validate the model. To quantify predictive accuracy, the dose difference between the clinical plan and prediction were calculated on a voxel‐by‐voxel basis δD(r,θ,φ)=Dclin(r,θ,φ)‐Dpred(r, θ,φ). Grouping voxels by boundary distance, the mean <δ Dr>=(1/N)Σ _θ,φ D(r,θ,φ) and inter‐quartile range (IQR) quantified the accuracy of this method for deriving DVH estimations. The standard deviation (σ) of δ D quantified the 3D dose prediction error on a voxel‐by‐voxel basis.Results:The ANNs were highly accurate in predictive ability for both prostate and SRS plans. For prostate, <δDr> ranged from −0.8% to +0.6% (max IQR=3.8%) over r=0–32mm, while 3D dose prediction accuracy averaged from σ=5–8% across the same range. For SRS, from r=0–34mm the training set <δDr> ranged from −3.7% to +1.5% (max IQR=4.4%) while the validation set <δDr> ranged from −2.2% to +5.8% (max IQR=5.3%). 3D dose prediction accuracy averaged σ=2.5% for the training set and σ=4.0% over the same interval.Conclusion:The study demonstrates this technique's ability to predict achievable 3D dose distributions for VMAT SRS and prostate. Future investigations will attempt to separate and quantify modeling error and plan quality variations in the statistical noise.Research funding support from Varian Medical Systems
MO‐FG‐303‐05: A Feasibility Study of Using a Cherenkov Detector Material with the Prompt Gamma Range Verification Technique in Proton Therapy
Authors: Lau A , Ahmad S , Chen Y 
Published: June 2015

Purpose:To simulate the feasibility of a Cherenkov glass material for the determination of the penetration depth of therapeutic proton beams in water.Methods:Proton pencil beams of various energies incident onto a water phantom with dimensions of 5 × 5 × 30 cm3 were used for simulation with the Geant4 toolkit. The model used standard electromagnetic packages, packages based on binary‐cascade nuclear model, several decay modules (G4Decay, G4DecayPhysics, and G4RadioactiveDecayPhysics), and optical photon components (G4OpticalPhysics). A Cherenkov glass material was modeled as the detector medium (7.2 g of In2O3 + 90 g cladding, density of 2.82 g/cm3, Zeff = 33.7, index of refraction n(600 nm) = 1.56, and energy threshold of production Eth = 156 keV ). The emitted secondary particles are analyzed characterizing their timing, energy, and angular distributions. A feasibility analysis was conducted for a simplistic detector system using this material to locate the position of the Bragg Peak.Results:The escaping neutrons have energies ranging from thermal to the incident proton energy and the escaping photons have energies >10 MeV. Photon peaks between 4 and 6 MeV were attributed to originate from direct proton interactions with 12C (∼ 4.4 MeV) and 1⁶O (∼ 6 MeV), respectively. The escaping photons are emitted isotropically, while low (≤10 MeV) and high (>10 MeV) neutrons are isotropic and forward‐directional, respectively. The emissions of photons are categorized into prompt (∼ns) and delayed (∼min) where the prompt photons include the 4.4 and 6 MeV. The Cherenkov material had on average <2% of neutron interactions while LYSO and BGO scintillators had a minimum of ∼50%. Our simplistic detector system was capable of discerning Bragg Peak locations using a timing discrimination of ∼50 ns.Conclusion:We investigate the viability of using the Cherenkov material for MeV photon detection medium for the prompt gamma technique.
MO‐FG‐303‐06: Evaluation of the Performance of Very High‐Energy Electron (VHEE) Beams in Radiotherapy: Five Clinical Cases
Authors: Palma B , Bazalova‐Carter M , Hardemark B , Hynning E , Qu B , Loo B , Maxim P 
Published: June 2015

Purpose:To evaluate the performance of 100–120 MeV very‐high energy electron (VHEE) scanning pencil beams to radiotherapy by means of Monte Carlo (MC) simulations.Methods:We selected five clinical cases with target sizes of 1.2 cm3 to 990.4 cm3. We calculated VHEE treatment plans using the MC EGSnrc code implemented in a MATLAB‐based graphical user interface developed by our group. We generated phase space data for beam energies: 100 and 120 MeV and pencil beam spot sizes of 1, 3, and 5 mm at FWHM. The number of equidistant beams considered in this work was 16 or 32. Dose was calculated and then imported into a research version of RayStation where treatment plan optimization was performed. We compared the VHEE plans with the clinically delivered volumetric modulated arc therapy (VMAT) plan to evaluate VHEE plans performance.Results:VHEE plans provided the same PTV coverage and dose homogeneity than VMAT plans for all the cases. In average, the mean dose to organs at risk (OARs) was 24% lower for the VHEE plans. The structures that benefited the most from using VHEE were: large bowel for the esophagus case, chest wall for the liver case, brainstem for the acoustic case, carina for the lung case, and genitalia for the anal case, with 23.7–34.6% lower dose. VHEE dose distributions were more conformal than VMAT solution as confirmed by conformity indices CI100 and CI50. Integral dose to the body was in average 19.6% (9.2%–36.5%) lower for the VHEE plans.Conclusion:We have shown that VHEE plans resulted in similar or superior dose distributions compared to clinical VMAT plans for five different cases and a wide range of target volumes, including a case with a small target (1.2 cm3), which represents a challenge for VMAT planning and might require the use of more complex non‐coplanar VMAT plans.B Palma: None. M Bazalova: None. B Hardemark: Employee, RaySearch Laboratories AB. E Hynning: Employee, RaySearch Laboratories AB. B Qu: None. B Loo Jr.: Research support, RaySearch, Varian. P Maxim: Research support, RaySearch, Varian
MO‐FG‐303‐07: Quality Assurance of OAR Segmentation Using Machine Learning And Statistical Shape Models
Authors: Schreibmann E , Elder E 
Published: June 2015

Purpose:Automating the quality assurance of OAR segmentations is technically a challenging task due to the natural anatomical variations that cannot be easily quantified by computer algorithms. We report on QA method for OAR segmentations that uses recent advances in machine learning to automate the currently‐used manual interpretation of segmentation quality.Materials and Methods:The approach relies on building a statistical model of an organ's shapes and variation across patients from a set of high‐quality contours representative for the anatomy. When presented with a new OAR segmentation for evaluation, the QA procedure fits the shape to the statistical model to mark outlier contours and suggest a correction for their location based on the statistics inferred from the training datasets. Technically, the statistical model of OAR variation is build using a probabilistic PCA model where deformations between learning shapes are modeled as a Gaussian process to create a general non‐parametric model that is flexible in capturing inter‐patient variations.Results:The algorithm was tested in clinical practice by creating custom software that intercepts DICOM RT segmentation files send to the treatment planning system, verifies them against the statistical model, and presents to original and corrected segmentations side‐by‐side in the treatment planning for evaluating changes suggested by the QA procedure. Segmentation quality is quantified through the Dice and Hausdorff distance between the initial and corrected shape.Conclusion:Abnormalities in critical structures delineation can be quantified by a shape model derived from a database of verified segmentation to create a software tool that detects and quantifies point‐by point variations from a normal segmentation. The approach was used to verify OAR segmentation and streamline clinical practice.
MO‐FG‐204‐02: Reference Image Selection in the Presence of Multiple Scan Realizations
Authors: Ruan D , Dou T , Thomas D , Low D 
Published: June 2015

Purpose:Fusing information from multiple correlated realizations (e.g., 4DCT) can improve image quality. This process often involves ill‐conditioned and asymmetric nonlinear registration and the proper selection of a reference image is important. This work proposes to examine post‐registration variation indirectly for such selection, and develops further insights to reduce the number of cross‐registrations needed.Methods:We consider each individual scan as a noisy point in the vicinity of an image manifold, related by motion. Nonrigid registration “transports” a scan along the manifold to the reference neighborhood, and the residual is a surrogate for local variation. To test this conjecture, 10 thoracic scans from the same session were reconstructed from a recently developed low‐dose helical 4DCT protocol. Pairwise registration was repeated bi‐directionally (81 times) and fusion was performed with each candidate reference. The fused image quality was assessed with SNR and CNR. Registration residuals in SSD, harmonic energy, and deformation Jacobian behavior were examined. The semi‐symmetry is further utilized to reduce the number of registration needed.Results:The comparison of image quality between single image and fused ones identified reduction of local intensity variance as the major contributor of image quality, boosting SNR and CNR by 5 to 7 folds. This observation further suggests the criticality of good agreement across post‐registration images. Triangle inequality on the SSD metric provides a proficient upper‐bound and surrogate on such disagreement. Empirical observation also confirms that fused images with high residual SSD have lower SNR and CNR than the ones with low or intermediate SSDs. Registration SSD is structurally close enough to symmetry for reduced computation.Conclusion:Registration residual is shown to be a good predictor of post‐fusion image quality and can be used to identify good reference centers. Semi‐symmetry of the registration residual further reduces computation cost.Supported by in part by NIH R01 CA096679.
MO‐FG‐204‐03: Using Edge‐Preserving Algorithm for Significantly Improved Image‐Domain Material Decomposition in Dual Energy CT
Authors: Zhao W , Niu T , Xing L , Xiong G , Elmore K , Zhu J , Wang L , Min J 
Published: June 2015

Purpose:To significantly improve dual energy CT (DECT) imaging by establishing a new theoretical framework of image‐domain material decomposition with incorporation of edge‐preserving techniques.Methods:The proposed algorithm, HYPR‐NLM, combines the edge‐preserving non‐local mean filter (NLM) with the HYPR‐LR (Local HighlY constrained backPRojection Reconstruction) framework. Image denoising using HYPR‐LR framework depends on the noise level of the composite image which is the average of the different energy images. For DECT, the composite image is the average of high‐ and low‐energy images. To further reduce noise, one may want to increase the window size of the filter of the HYPR‐LR, leading resolution degradation. By incorporating the NLM filtering and the HYPR‐LR framework, HYPR‐NLM reduces the boost material decomposition noise using energy information redundancies as well as the non‐local mean. We demonstrate the noise reduction and resolution preservation of the algorithm with both iodine concentration numerical phantom and clinical patient data by comparing the HYPR‐NLM algorithm to the direct matrix inversion, HYPR‐LR and iterative image‐domain material decomposition (Iter‐DECT).Results:The results show iterative material decomposition method reduces noise to the lowest level and provides improved DECT images. HYPR‐NLM significantly reduces noise while preserving the accuracy of quantitative measurement and resolution. For the iodine concentration numerical phantom, the averaged noise levels are about 2.0, 0.7, 0.2 and 0.4 for direct inversion, HYPR‐LR, Iter‐ DECT and HYPR‐NLM, respectively. For the patient data, the noise levels of the water images are about 0.36, 0.16, 0.12 and 0.13 for direct inversion, HYPR‐LR, Iter‐DECT and HYPR‐NLM, respectively. Difference images of both HYPR‐LR and Iter‐DECT show edge effect, while no significant edge effect is shown for HYPR‐NLM, suggesting spatial resolution is well preserved for HYPR‐NLM.Conclusion:HYPR‐NLM provides an effective way to reduce the generic magnified image noise of dual–energy material decomposition while preserving resolution.This work is supported in part by NIH grants 7R01HL111141 and 1R01‐EB016777. This work is also supported by the Natural Science Foundation of China (NSFC Grant No. 81201091), Fundamental Research Funds for the Central Universities in China, and Fund Project for Excellent Abroad Scholar Personnel in Science and Technology.
MO‐FG‐204‐04: How Iterative Reconstruction Algorithms Affect the NPS of CT Images
Authors: Li G , Liu X , Dodge C , Jensen C , Rong J 
Published: June 2015

Purpose:To evaluate how the third generation model based iterative reconstruction (MBIR) compares with filtered back‐projection (FBP), adaptive statistical iterative reconstruction (ASiR), and the second generation MBIR based on noise power spectrum (NPS) analysis over a wide range of clinically applicable dose levels.Methods:The Catphan 600 CTP515 module, surrounded by an oval, fat‐equivalent ring to mimic patient size/shape, was scanned on a GE HD750 CT scanner at 1, 2, 3, 6, 12 and 19mGy CTDIvol levels with typical patient scan parameters: 120kVp, 0.8s, 40mm beam width, large SFOV, 0.984 pitch and reconstructed thickness 2.5mm (VEO3.0: Abd/Pelvis with Texture and NR05). At each CTDIvol level, 10 repeated scans were acquired for achieving sufficient data sampling. The images were reconstructed using Standard kernel with FBP; 20%, 40% and 70% ASiR; and two versions of MBIR (VEO2.0 and 3.0). For evaluating the effect of the ROI spatial location to the Result of NPS, 4 ROI groups were categorized based on their distances from the center of the phantom.Results:VEO3.0 performed inferiorly comparing to VEO2.0 over all dose levels. On the other hand, at low dose levels (less than 3 mGy), it clearly outperformed ASiR and FBP, in NPS values. Therefore, the lower the dose level, the relative performance of MBIR improves. However, the shapes of the NPS show substantial differences in horizontal and vertical sampling dimensions. These differences may determine the characteristics of the noise/texture features in images, and hence, play an important role in image interpretation.Conclusion:The third generation MBIR did not improve over the second generation MBIR in term of NPS analysis. The overall performance of both versions of MBIR improved as compared to other reconstruction algorithms when dose was reduced. The shapes of the NPS curves provided additional value for future characterization of the image noise/texture features.
MO‐FG‐204‐08: Optimization‐Based Image Reconstruction From Unevenly Distributed Sparse Projection Views
Authors: Xie Huiqiao , Niu Tianye , Ren Yi , Yang Yi , Tang Xiangyang 
Published: June 2015

Purpose:Optimization‐based reconstruction has been proposed and investigated for reconstructing CT images from sparse views, as such the radiation dose can be substantially reduced while maintaining acceptable image quality. The investigation has so far focused on reconstruction from evenly distributed sparse views. Recognizing the clinical situations wherein only unevenly sparse views are available, e.g., image guided radiation therapy, CT perfusion and multi‐cycle cardiovascular imaging, we investigate the performance of optimization‐based image reconstruction from unevenly sparse projection views in this work.Methods:The investigation is carried out using the FORBILD and an anthropomorphic head phantoms. In the study, 82 views, which are evenly sorted out from a full (360°) axial CT scan consisting of 984 views, form sub‐scan I. Another 82 views are sorted out in a similar manner to form sub‐scan II. As such, a CT scan with sparse (164) views at 1:6 ratio are formed. By shifting the two sub‐scans relatively in view angulation, a CT scan with unevenly distributed sparse (164) views at 1:6 ratio are formed. An optimization‐based method is implemented to reconstruct images from the unevenly distributed views. By taking the FBP reconstruction from the full scan (984 views) as the reference, the root mean square (RMS) between the reference and the optimization‐based reconstruction is used to evaluate the performance quantitatively.Results:In visual inspection, the optimization‐based method outperforms the FBP substantially in the reconstruction from unevenly distributed, which are quantitatively verified by the RMS gauged globally and in ROIs in both the FORBILD and anthropomorphic head phantoms. The RMS increases with increasing severity in the uneven angular distribution, especially in the case of anthropomorphic head phantom.Conclusion:The optimization‐based image reconstruction can save radiation dose up to 12‐fold while providing acceptable image quality for advanced clinical applications wherein only unevenly distributed sparse views are available.Research Grants: W81XWH‐12‐1‐0138 (DoD), Sinovision Technologies.
MO‐FG‐207‐02: Technological Advances and Challenges: Experience with Time‐Of‐Flight PET Combined with 3‐Tesla MRI
Authors: Jansen F. 
Published: June 2015

The use of integrated PET/MRI systems in clinical applications can best benefit from understanding their technological advances and limitations. The currently available clinical PET/MRI systems have their own characteristics. Thorough analyses of existing technical data and evaluation of necessary performance metrics for quality assurances could be conducted to optimize application‐specific PET/MRI protocols. This Symposium will focus on technical advances and limitations of clinical PET/MRI systems, and how this exciting imaging modality can be utilized in applications that can benefit from both PET and MRI.Learning Objectives:

To understand the technological advances of clinical PET/MRI systems
To correctly identify clinical applications that can benefit from PET/MRI
To understand ongoing work to further improve the current PET/MRI technology
Floris Jansen is a GE Healthcare employee.