bluuebunny/crossref_metadata_2025_split huggingface dataset:
TU‐CD‐304‐03: Dosimetric Verification and Preliminary Comparison of Dynamic Wave Arc for SBRT Treatments
Authors: Burghelea M , Poels K , Gevaert T , Tournel K , Dhont J , Hung C , Eriksson K , Simon V , De Ridder M , Verellen D 
Published: June 2015

Purpose:To evaluate the potential dosimetric benefits and verify the delivery accuracy of Dynamic Wave Arc, a novel treatment delivery approach for the Vero SBRT system.Methods:Dynamic Wave Arc (DWA) combines simultaneous movement of gantry/ring with inverse planning optimization, resulting in an uninterrupted non‐coplanar arc delivery technique. Thirteen SBRT complex cases previously treated with 8–10 conformal static beams (CRT) were evaluated in this study. Eight primary centrally‐located NSCLC (prescription dose 4×12Gy or 8×7.5Gy) and five oligometastatic cases (2×2 lesions, 10×5Gy) were selected. DWA and coplanar VMAT plans, partially with dual arcs, were generated for each patient using identical objective functions for target volumes and OARs on the same TPS (RayStation, RaySearch Laboratories). Dosimetric differences and delivery time among these three planning schemes were evaluated. The DWA delivery accuracy was assessed using the Delta4 diode array phantom (ScandiDos AB). The gamma analysis was performed with the 3%/3mm dose and distance‐to‐agreement criteria.Results:The target conformity for CRT, VMAT and DWA were 0.95±0.07, 0.96±0.04 and 0.97±0.04, while the low dose spillage gradient were 5.52±1.36, 5.44±1.11, and 5.09±0.98 respectively. Overall, the bronchus, esophagus and spinal cord maximum doses were similar between VMAT and DWA, but highly reduced compared with CRT. For the lung cases, the mean dose and V20Gy were lower for the arc techniques compares with CRT, while for the liver cases, the mean dose and the V30Gy presented slightly higher values. The average delivery time of VMAT and DWA were 2.46±1.10 min and 4.25±1.67 min, VMAT presenting shorter treatment time in all cases. The DWA dosimetric verification presented an average gamma index passing rate of 95.73±1.54% (range 94.2%–99.8%).Conclusion:Our preliminary data indicated that the DWA is deliverable with clinically acceptable accuracy and has the potential to further improve the plan quality.This collaborative work was supported by the Flemish government through the Hercules foundation and corporate funding from BrainLab AG. The first and the sixth author are financially supported by Brainlab AG. The other authors have no conflict of interest.
TU‐CD‐304‐06: Using FFF Beams Improves Tumor Control in Radiotherapy of Lung Cancers
Authors: Vassiliev O , Wang H 
Published: June 2015

Purpose:Electron disequilibrium at the lung‐tumor interface results in an under‐dosage of tumor regions close to its surface. This under‐dosage is known to be significant and can compromise tumor control. Previous studies have shown that in FFF beams, disequilibrium effects are less pronounced, which is manifested in an increased skin dose. In this study we investigate the improvement in tumor dose coverage that can be achieved with FFF beams. The significance of this improvement is evaluated by comparing tumor control probabilities of FFF beams and conventional flattened beams.Methods:The dosimetric coverage was investigated in a virtual phantom representing the chest wall, lung tissue and the tumor. A range of tumor sizes was investigated, and two tumor locations – central and adjacent to the chest wall. Calculations were performed with BEAMnrc Monte Carlo code. Parallel‐opposed and multiple coplanar 6‐MV beams were simulated. The tumor control probabilities were calculated using the logistic model with parameters derived from clinical data for non‐small lung cancer patients.Results:FFF beams were not entirely immune to disequilibrium effects. They nevertheless consistently delivered more uniform dose distribution throughout the volume of the tumor, and eliminated up to ∼15% of under‐dosage in the most affected by disequilibrium 1‐mm thick surface region of the tumor. A voxel‐by‐voxel comparison of tumor control probabilities between FFF and conventional flattened beams showed an advantage of FFF beams that, depending on the set up, was from a few to ∼9 percent.Conclusion:A modest improvement in tumor control probability on the order of a few percent can be achieved by replacing conventional flattened beams with FFF beams. However, given the large number of lung cancer patients undergoing radiotherapy, these few percent can potentially prevent local tumor recurrence for a significant number of patients.
TU‐CD‐BRB‐01: Normal Lung CT Texture Features Improve Predictive Models for Radiation Pneumonitis
Authors: Krafft S , Briere T , Court L , Martel M 
Published: June 2015

Purpose:Existing normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) traditionally rely on dosimetric and clinical data but are limited in terms of performance and generalizability. Extraction of pre‐treatment image features provides a potential new category of data that can improve NTCP models for RP. We consider quantitative measures of total lung CT intensity and texture in a framework for prediction of RP.Methods:Available clinical and dosimetric data was collected for 198 NSCLC patients treated with definitive radiotherapy. Intensity‐ and texture‐based image features were extracted from the T50 phase of the 4D‐CT acquired for treatment planning. A total of 3888 features (15 clinical, 175 dosimetric, and 3698 image features) were gathered and considered candidate predictors for modeling of RP grade≥3. A baseline logistic regression model with mean lung dose (MLD) was first considered. Additionally, a least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the set of clinical and dosimetric features, and subsequently to the full set of clinical, dosimetric, and image features. Model performance was assessed by comparing area under the curve (AUC).Results:A simple logistic fit of MLD was an inadequate model of the data (AUC∼0.5). Including clinical and dosimetric parameters within the framework of the LASSO resulted in improved performance (AUC=0.648). Analysis of the full cohort of clinical, dosimetric, and image features provided further and significant improvement in model performance (AUC=0.727).Conclusions:To achieve significant gains in predictive modeling of RP, new categories of data should be considered in addition to clinical and dosimetric features. We have successfully incorporated CT image features into a framework for modeling RP and have demonstrated improved predictive performance. Validation and further investigation of CT image features in the context of RP NTCP modeling is warranted.This work was supported by the Rosalie B. Hite Fellowship in Cancer research awarded to SPK
TU‐CD‐BRB‐09: Prediction of Chemo‐Radiation Outcome for Rectal Cancer Based On Radiomics of Tumor Clinical Characteristics and Multi‐Parametric MRI
Authors: Nie K , Shi L , Hu X , Chen Q , Yue N , Sun X , Niu T 
Published: June 2015

Purpose:To evaluate the tumor clinical characteristics and quantitative multi‐parametric MR imaging features for prediction of response to chemo‐radiation treatment (CRT) in locally advanced rectal cancer (LARC).Methods:Forty‐three consecutive patients (59.7±6.9 years, from 09/2013 – 06/2014) receiving neoadjuvant CRT followed by surgery were enrolled. All underwent MRI including anatomical T1/T2, Dynamic Contrast Enhanced (DCE)‐MRI and Diffusion‐Weighted MRI (DWI) prior to the treatment. A total of 151 quantitative features, including morphology/Gray Level Co‐occurrence Matrix (GLCM) texture from T1/T2, enhancement kinetics and the voxelized distribution from DCE‐MRI, apparent diffusion coefficient (ADC) from DWI, along with clinical information (carcinoembryonic antigen CEA level, TNM staging etc.), were extracted for each patient. Response groups were separated based on down‐staging, good response and pathological complete response (pCR) status. Logistic regression analysis (LRA) was used to select the best predictors to classify different groups and the predictive performance were calculated using receiver operating characteristic (ROC) analysis.Results:Individual imaging category or clinical charateristics might yield certain level of power in assessing the response. However, the combined model outperformed than any category alone in prediction. With selected features as Volume, GLCM AutoCorrelation (T2), MaxEnhancementProbability (DCE‐MRI), and MeanADC (DWI), the down‐staging prediciton accuracy (area under the ROC curve, AUC) could be 0.95, better than individual tumor metrics with AUC from 0.53–0.85. While for the pCR prediction, the best set included CEA (clinical charateristics), Homogeneity (DCE‐MRI) and MeanADC (DWI) with an AUC of 0.89, more favorable compared to conventional tumor metrics with an AUC ranging from 0.511–0.79.Conclusion:Through a systematic analysis of multi‐parametric MR imaging features, we are able to build models with improved predictive value over conventional imaging or clinical metrics. This is encouraging, suggesting the wealth of imaging radiomics should be further explored to help tailor the treatment into the era of personalized medicine.This work is supported by the National Science Foundation of China (NSFC Grant No. 81201091), National High Technology Research and Development Program of China (863 program, Grant No. 2015AA020917), and Fund Project for Excellent Abroad Scholar Personnel in Science and Technology.
TU‐CD‐BRB‐12: Radiogenomics of MRI‐Guided Prostate Cancer Biopsy Habitats
Authors: Stoyanova R , Tachar M , Erho N , Lynne C , Abraham S , Patel M , Buerki C , Lam L , Jorda M , Kryvenko O , Ishkanian A , Abramowitz M , Davicioni E , Pollack A 
Published: June 2015

Purpose:Diagnostic prostate biopsies are subject to sampling bias. We hypothesize that quantitative imaging with multiparametric (MP)‐MRI can more accurately direct targeted biopsies to index lesions associated with highest risk clinical and genomic features.Methods:Regionally distinct prostate habitats were delineated on MP‐MRI (T2‐weighted, perfusion and diffusion imaging). Directed biopsies were performed on 17 habitats from 6 patients using MRI‐ultrasound fusion. Biopsy location was characterized with 52 radiographic features. Transcriptome‐wide analysis of 1.4 million RNA probes was performed on RNA from each habitat. Genomics features with insignificant expression values (<0.25) and interquartile range <0.5 were filtered, leaving total of 212 genes. Correlation between imaging features, genes and a 22 feature genomic classifier (GC), developed as a prognostic assay for metastasis after radical prostatectomy was investigated.Results:High quality genomic data was derived from 17 (100%) biopsies. Using the 212 ‘unbiased’ genes, the samples clustered by patient origin in unsupervised analysis. When only prostate cancer related genomic features were used, hierarchical clustering revealed samples clustered by needle‐biopsy Gleason score (GS). Similarly, principal component analysis of the imaging features, found the primary source of variance segregated the samples into high (≥7) and low (6) GS. Pearson's correlation analysis of genes with significant expression showed two main patterns of gene expression clustering prostate peripheral and transitional zone MRI features. Two‐way hierarchical clustering of GC with radiomics features resulted in the expected groupings of high and low expressed genes in this metastasis signature.Conclusions:MP‐MRI‐targeted diagnostic biopsies can potentially improve risk stratification by directing pathological and genomic analysis to clinically significant index lesions. As determinant lesions are more reliably identified, targeting with radiotherapy should improve outcome. This is the first demonstration of a link between quantitative imaging features (radiomics) with genomic features in MRI‐directed prostate biopsies.The research was supported by NIH‐ NCI R01 CA 189295 and R01 CA 189295; E Davicioni is partial owner of GenomeDx Biosciences, Inc. M Takhar, N Erho, L Lam, C Buerki and E Davicioni are current employees at GenomeDx Biosciences, Inc.
TU‐CD‐BRA‐06: 3D Reconstruction From 2D CineMRI Orthogonal Slices: A Feasibility Study
Authors: Paganelli C , Lee D , Kipritidis J , Greer P , Riboldi M , Keall P 
Published: June 2015

Purpose:The ideal image guidance strategy to acquire complete spatio‐temporal information for the patients during therapy. MRI‐Linacs come close to this ideal, but there are spatial and temporal trade‐offs. Several authors have proposed simultaneous orthogonal images as a guidance strategy for MRI‐Linac. In order to estimate the missing 3D information from the 2D orthogonal images we present a feasibility study on the reconstruction of a 3D volume directly from 2D orthogonal CineMRI slices for MRI‐guided treatment.Methods:A 4DCT XCAT lung tumor phantom was generated in two respiratory phases (i.e. exhale and inhale) with 3cm diaphragm motion. We simulated the cine acquisition by extracting sagittal and coronal orthogonal slices from the inhale XCAT phantom. Then, we performed DIR between the selected slices at the inhale phase with the corresponding ones at exhale. An interpolation of the derived motion field was performed in the RL, AP and SI directions, thus obtaining a 3D motion field that was used to warp the exhale phase and obtain a 3D estimation of the inhale phase (i.e. estimated_inhale volume). As validation we compared the estimated_inhale volume with the inhale phase (i.e. ground truth). We also applied the method on one CineMRI lung patient image set, where inter‐leaved sagittal/coronal cineMRI images were available.Results:The estimated_inhale volume resulted comparable with the ground truth. The distance between landmarks identified in the two respiratory phases was quantified as 24.8±7.2mm for exhale/estimated _inhale, vs.21.9±9.8mm for exhale/inhale. Tumor motion between exhale and estimated_inhale was 28.3mm, consistent with the measured diaphragm motion (i.e. 30mm). The patient results confirmed that the estimated_inhale volume compensated for organ motion (i.e. diaphragm distance between inhale and estimated_inhale was within 4.7±2.4mm).Conclusion:The method provided a framework for the volumetric reconstruction from 2D orthogonal slices. The application to a patient case showed the capability to account for organ motion.
TU‐CD‐BRA‐09: Accuracy of Deformable Image Registration in Homogeneous Regions: The Impact of Regularisation
Authors: Larrue A , Cifor A , Gooding M 
Published: June 2015

PurposeDeformable Image Registration (DIR) consists of computing a deformation field to provide an anatomical mapping between the images to be aligned. The deformation field is regularized to prevent over‐fitting, ensure numerical stability, and support the assumption that natural deformations are smooth. The aim of this work is to evaluate the impact of the regularization on the accuracy of the DIR in homogeneous regions where strong image features are not present to guide the mapping.MethodsThe “DIR Validation dataset” (http://www.creatis.insa‐lyon.fr/rio/dir_validation_data) providesbenchmark data for evaluating 3D CT registration algorithms. It consists of six 4DCT lung datasets, with landmarks identified on the end‐exhalation and end‐inhalation phases. Images were registered using image‐based commercial DIR software (Mirada RTx, Mirada Medical, UK). Lungs were segmented and the contours used for structure‐based registration method, thus simulating a fully homogenous region by blinding registration to the underlying image. Target and Surface registration errors (TRE, SRE) were measured at different values of the regularization parameters.ResultsStructure‐based registration showed similar boundary alignment at low regularisation (mean SFE: 0.41 ± 0.07mm) compared to image‐based DIR (0.38±0.17mm), but internal TRE was much worse (5.1±2.1mm cf. 1.4±0.6mm). As regularisation increased internal TRE decreased slightly (minimum 4.2±1.9mm) however this corresponded to an increase in SFE (maximum: 0.9±0.18mm).ConclusionWhile image‐based DIR can align the internal structures in this case, sometimes few internal structures may be visible in clinical practice (e.g. some tumours, liver, brain). The use of structure‐based registration simulated registration within homogenous regions, while allowing comparison with internal TRE. This study demonstrates that while regularization creates a smooth deformation within such regions but it is not necessarily an accurate one, even where the structure boundary is well aligned. Assessment of the deformation grid is helpful for evaluating registrations in such areas.A. Larrue, A.Cifor, and M.Gooding are employees of Mirada Medical
TU‐CD‐204‐00: CT Scanner Hardware and Image Quality Assessment
Authors: Dong Frank 
Published: June 2015

For practicing medical physicists, CT image quality assessment is, in general, limited to the ones required by ACR and based on the axial images, such as CT number uniformity, image noise, contrast‐to‐noise ratio (CNR), and high contrast resolution. However, with more advanced technologies introduced by CT vendors, just assessing these basic image quality metrics may not be enough. Noise power spectrum (NPS), characterizing the signal noise component at each spatial frequency, is a powerful tool to predict the CT performance related to lesion detectability and noise texture. The quality of coronal and sagittal reformatted images is, in a large part, determined by the slice profile. How to accurately assess the slice profile is important for clinical medical physicists but not well recognized.Automatic tube current modulation (ATCM) has been adopted by major CT manufacturers, to maintain image quality across the scan range. The way how ATMC works varies significantly among vendors. A “standard” method is needed to evaluate the performance of this critical dose‐saving and image quality improvement technique. As dual energy CT becomes more readily available for routine clinical use, the need of image quality assessment metrics unique to the dual energy CT mode is becoming urgent.The session will facilitate the discussion of NPS and its application in the evaluation of FBP and iterative reconstruction. This session will also present the methods to measure the slice profile and characterize the performance of ATCM. The last part of this session will focus on the physics of CT dual energy mode, as well as unique image quality metrics to evaluate the performance of the dual energy mode.Learning Objectives:

Be familiar with procedures on how to correctly compute CT noise power spectrum
Understand why basic image quality metrics used for FBP may not be sufficient to characterize the performance of advanced iterative reconstruction
Understand CT slice profile and how to accurately measure it
Be familiar with ATCM from major CT vendors and how to evaluate its performance
Understand different hardware approaches for dual energy CT
Be familiar with image quality metrics relevant to dual energy mode
Schmidt: Employee of Siemens HealthcareFan: Employee of GE Healthcare
TU‐EF‐303‐03: Single Cell Measurements of Tanscription Heterogeneity of Highly Mobile Identical Genes
Authors: Gratton E. 
Published: June 2015

Many medical physics diagnostic and therapeutic developments have significant overlap with biophysical approaches to measuring and modeling molecular and cellular systems. Although these methods may differ dramatically in terms of temporal and spatial scales, many of the underlying concepts are identical, suggesting potential synergies across the scales. For example, while clinical imaging is typically in the (sub)millimeter and second regime, biophysical imaging technologies reach sub‐nanometer and sub‐picosecond scales. Despite these scale differences, the use of molecular labeling, resulting large datasets, and need for quantitative analysis methods are similar, and solutions developed for one application are easily translated to others. Similarly, mathematical models used to understand clinical responses are often based on molecular and cellular networks that have been elucidated by biophysical studies. In turn, such models are directly useful for guiding on‐going biophysical and biomedical research. Relevant biophysical applications are found in the cancer, neuroscience, and cardiac fields, which are also research focus areas for many AAPM members. Presentations by physicists working in biophysics will explore potential synergies with diagnostic and therapeutic medical physics, as well as help identify areas of common interest that would be ripe for novel collaborations.Learning Objectives:

1.To learn about differences in temporal and spatial scales of medical physics and biophysics
To learn about common methods used in medical physics and biophysics
To learn about synergistic opportunities between medical physics and biophysics
TU‐EF‐204‐01: Accurate Prediction of CT Tube Current Modulation: Estimating Tube Current Modulation Schemes for Voxelized Patient Models Used in Monte Carlo Simulations
Authors: McMillan K , Bostani M , McCollough C , McNitt‐Gray M 
Published: June 2015

Purpose:Most patient models used in Monte Carlo‐based estimates of CT dose, including computational phantoms, do not have tube current modulation (TCM) data associated with them. While not a problem for fixed tube current simulations, this is a limitation when modeling the effects of TCM. Therefore, the purpose of this work was to develop and validate methods to estimate TCM schemes for any voxelized patient model.Methods:For 10 patients who received clinically‐indicated chest (n=5) and abdomen/pelvis (n=5) scans on a Siemens CT scanner, both CT localizer radiograph (“topogram”) and image data were collected. Methods were devised to estimate the complete x‐y‐z TCM scheme using patient attenuation data: (a) available in the Siemens CT localizer radiograph/topogram itself (“actual‐topo”) and (b) from a simulated topogram (“sim‐topo”) derived from a projection of the image data. For comparison, the actual TCM scheme was extracted from the projection data of each patient. For validation, Monte Carlo simulations were performed using each TCM scheme to estimate dose to the lungs (chest scans) and liver (abdomen/pelvis scans). Organ doses from simulations using the actual TCM were compared to those using each of the estimated TCM methods (“actual‐topo” and “sim‐topo”).Results:For chest scans, the average differences between doses estimated using actual TCM schemes and estimated TCM schemes (“actual‐topo” and “sim‐topo”) were 3.70% and 4.98%, respectively. For abdomen/pelvis scans, the average differences were 5.55% and 6.97%, respectively.Conclusion:Strong agreement between doses estimated using actual and estimated TCM schemes validates the methods for simulating Siemens topograms and converting attenuation data into TCM schemes. This indicates that the methods developed in this work can be used to accurately estimate TCM schemes for any patient model or computational phantom, whether a CT localizer radiograph is available or not.Funding Support: NIH Grant R01‐EB017095; Disclosures ‐ Michael McNitt‐Gray: Institutional Research Agreement, Siemens AG; Research Support, Siemens AG; Consultant, Flaherty Sensabaugh Bonasso PLLC; Consultant, Fulbright and Jaworski; Disclosures ‐ Cynthia McCollough: Research Grant, Siemens Healthcare