bluuebunny/crossref_metadata_2025_split huggingface dataset:
TU‐CD‐BRB‐07: Identification of Associations Between Radiologist‐Annotated Imaging Features and Genomic Alterations in Breast Invasive Carcinoma, a TCGA Phenotype Research Group Study
Authors: Rao A , Net J , Brandt K , Huang E , Freymann J , Burnside E , Kirby J , Morris E , Bonaccio E , Giger M , Jaffe C , Ganott M , Sutton E , Le‐Petross H , Zuley M , Dogan B , Whitman G 
Published: June 2015

Purpose:To determine associations between radiologist‐annotated MRI features and genomic measurements in breast invasive carcinoma (BRCA) from the Cancer Genome Atlas (TCGA).Methods:98 TCGA patients with BRCA were assessed by a panel of radiologists (TCGA Breast Phenotype Research Group) based on a variety of mass and non‐mass features according to the Breast Imaging Reporting and Data System (BI‐RADS). Batch corrected gene expression data was obtained from the TCGA Data Portal. The Kruskal‐Wallis test was used to assess correlations between categorical image features and tumor‐derived genomic features (such as gene pathway activity, copy number and mutation characteristics). Image‐derived features were also correlated with estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2/neu) status. Multiple hypothesis correction was done using Benjamini‐Hochberg FDR. Associations at an FDR of 0.1 were selected for interpretation.Results:ER status was associated with rim enhancement and peritumoral edema. PR status was associated with internal enhancement. Several components of the PI3K/Akt pathway were associated with rim enhancement as well as heterogeneity. In addition, several components of cell cycle regulation and cell division were associated with imaging characteristics.TP53 and GATA3 mutations were associated with lesion size. MRI features associated with TP53 mutation status were rim enhancement and peritumoral edema. Rim enhancement was associated with activity of RB1, PIK3R1, MAP3K1, AKT1,PI3K, and PIK3CA. Margin status was associated with HIF1A/ARNT, Ras/ GTP/PI3K, KRAS, and GADD45A. Axillary lymphadenopathy was associated with RB1 and BCL2L1. Peritumoral edema was associated with Aurora A/GADD45A, BCL2L1, CCNE1, and FOXA1. Heterogeneous internal nonmass enhancement was associated with EGFR, PI3K, AKT1, HF/MET, and EGFR/Erbb4/neuregulin 1. Diffuse nonmass enhancement was associated with HGF/MET/MUC20/SHIP, and HGF/MET/RANBP9. Linear nonmass enhancement was associated with PIK3R1 and AKT activity.Conclusion:MRI‐genomic association analysis revealed that several BRCA‐associated gene features were associated with radiologist‐annotated image features.
TU‐CD‐BRB‐10: 18F‐FDG PET Image‐Derived Tumor Features Highlight Altered Pathways Identified by Trancriptomic Analysis in Head and Neck Cancer
Authors: Tixier F , Cheze‐Le‐Rest C , Hatt M , Dufour X , Valette G , Potard G , Corcos L , Visvikis D 
Published: June 2015

Purpose:Several quantitative features can be extracted from 18F‐FDG PET images, such as standardized uptake values (SUVs), metabolic tumor volume (MTV), shape characterization (SC) or intra‐tumor radiotracer heterogeneity quantification (HQ). Some of these features calculated from baseline 18F‐FDG PET images have shown a prognostic and predictive clinical value. It has been hypothesized that these features highlight underlying tumor patho‐physiological processes at smaller scales. The objective of this study was to investigate the ability of recovering alterations of signaling pathways from FDG PET image‐derived features.Methods:52 patients were prospectively recruited from two medical centers (Brest and Poitiers). All patients underwent an FDG PET scan for staging and biopsies of both healthy and primary tumor tissues. Biopsies went through a transcriptomic analysis performed in four spates on 4×44k chips (Agilent™). Primary tumors were delineated in the PET images using the Fuzzy Locally Adaptive Bayesian algorithm and characterized using 10 features including SUVs, SC and HQ. A module network algorithm followed by functional annotation was exploited in order to link PET features with signaling pathways alterations.Results:Several PET‐derived features were found to discriminate differentially expressed genes between tumor and healthy tissue (fold‐change >2, p<0.01) into 30 co‐regulated groups (p<0.05). Functional annotations applied to these groups of genes highlighted associations with well‐known pathways involved in cancer processes, such as cell proliferation and apoptosis, as well as with more specific ones such as unsaturated fatty acids.Conclusion:Quantitative features extracted from baseline 18F‐FDG PET images usually exploited only for diagnosis and staging, were identified in this work as being related to specific altered pathways and may show promise as tools for personalizing treatment decisions.
TU‐CD‐BRB‐11: A Spatiotemporal Image Phenotyping Pipeline for Radiogenomic Profiling of Breast Cancer with DCE MRI
Authors: Kim J , Kim Y , Yang Z , Hong B , Kuo M , Han W , Yamamoto S , Jamshidi N 
Published: June 2015

Purpose:Objective and quantitative radiophenotyping from cancer patients is a core requirement of radiogenomic research. We present our quantitative image phenotyping pipeline which is tuned to extract spatiotemporal tumor traits from DCE breast MRI and initial results of radiogenomic association between spatiotemporal imaging features and RNA expression in breast cancerMethods:DCE MRI with 5 enhancement phases of 61 breast cancer patients were analyzed. A unified energy functional was defined for a joint shape and temporal motion estimation which incorporates intensity variation within a region along with kinetic signature. A level set evolution was carried out under the guidance of the unified energy functional to generate tumor probability maps, which is then thresholded to produce tumor masks. Among them, the observer selected the largest one. A total of 47 features were extracted from the segmented tumor comprising geometric, statistical, and spatiotemporal features. The whole procedure was carried out independently by two observers. Features from 19 cases were used as a training set to discover a prognostic imaging biomarker indicative of poor metastasis‐free survival, and the remaining 39 cases were used for validation.In addition, association between the predictive imaging biomarker and genomic profiles were assessed using RNA expression data of these matched patients obtained with next‐generation RNA sequencing and RT‐PCR.Results:All imaging features from two independent analysis were highly consistent (r > 0.78, p<0.001). Among them, a spatiotemporal feature (enhancing rim fraction score, ERF) was shown to be strongly predictive of early metastasis (P =.009; hazard ratio,16.3; 95% confidence interval: 1.99). Radiogenomic analysis revealed five named lncRNAs significantly associated with high ERF score.Conclusion:A quantitative image phenotyping pipeline was developed which provides objective spatiotemporal features and could be successfully utilized in radiogenomic profiling of breast cancer.
TU‐CD‐303‐01: Memorial to Warren Sinclair ‐ Memorial Lecturer
Authors: Grissom M 
Published: June 2015

The Medical Physics and Radiation Protection communities lost one of their true pioneers in May, 2014 with the passing of Warren K. Sinclair, Ph.D. He received his Doctorate in Physics at the University of London, U.K. He was a fellow of the Institute of Physics and a Certified Health Physicist. After a number of posts at Hospitals and Universities in the U.K. and New Zealand, he became the Head, Physics Department at the M.D. Anderson Hospital, Houston, TX. He held a number of academic physics positions with the University of Texas, Austin at the main campus and at the Postgraduate School of Medicine, Houston. He later served as a Senior Biophysicist, Division Director, and then Associate Laboratory Director for Biomedical Division Director, and then Associate Laboratory Director for Biomedical and Environmental Research at the Argonne National Laboratory, IL. He had additional academic appointments at the University of California, Berkeley; Northern Illinois University; the University of Illinois (Chicago Circle Campus); and the University of Chicago.Warren was a charter member of the AAPM and a leader in many communities: President of the AAPM from 1960 to 1961 and the Journal Editor from 1964 to 1969, the 2nd President of the NCRP from 1977 to 1991, and the President of the Radiation Research Society from 1978 to 1979. His many awards included the William D. Coolidge Award in 1986. Even this barely scratches the surface of the many other organizations he served with and influenced including the ICRP, IOP, RSNA, SNM, and UNSCEAR. His research interests resulted in publications from 1948 to the 1990s in many aspects (physical and biological) of radiotherapy and the effects of ionizing radiation in general, leading to early definitions of RBE values for megavoltage radiotherapy as well as contributing to the development of the modern framework for radiation protection worldwide.Warren was not a retiring person and even after moving to NCRP President Emeritus status continued to provide advice and comments on publications and to mentor others until the time of his passing. Sadly, Warren's long‐time wife Joy passed away in December, 2014 but he is survived by his son Bruce and daughter Roslyn, 2 grandchildren and 4 great‐grandchildren.
TU‐EF‐303‐00: Bridging the Scales From Molecules and Cells to Clinical Applications
Authors: Jeraj Robert 
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‐303‐02: Fluorescence‐Guided Neurosurgery
Authors: Pogue B. 
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‐303‐04: Multi‐Modal in Vivo Cellular Imaging of Embryonic Heart Morphogenesis
Authors: Liebling M. 
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‐204‐04: Feasibility Study of a Novel Stationary Spectral CT Scanner
Authors: Xu P , Xing X , Zheng J , Chen S , Zhang Y , Yuan G , Sun M 
Published: June 2015

Purpose:To evaluate a cadmium zinc telluride (CZT) detector module and to develop a proof‐of‐concept bench‐top system to examine the concept and feasibility of a stationary spectral scanner that would consist of a CZT detector ring and a distributed source for rotationless imaging.Methods:The bench‐top system employs a double‐rotation set‐up with two concentric but independent rotating stages, one for turning the object at sparse angles to mimic the imaging from a sequentially activated source distributed on a ring, while the other for translating the detector module to simulate a projection arc along a virtual detector ring. A source of 30‐degree fan is used in the set‐up, and the ring is designed to be 85cm in diameter in order to provide a 22cm field‐of‐view that is large enough for an average human head. The CZT detector module has 16×16 0.5mm pixels and eight global energy thresholds, and is attached to an experimental data acquisition board from the same vendor (Nova R&D). Its pixel gain is first calibrated using radioisotope Tc‐99m to align the photo‐peaks. Count rate performance is assessed using the X‐ray source. Air and offset corrections are done prior to each CT scan. A 10cm acrylic phantom with 5 different inserts is imaged at 100kVp, 2mA, with an effective exposure window of 150ms per projection. Reconstruction is done using a total variation‐type iterative method.Results:The detector module exhibited large gain variations among pixels. Average count rate was over 1Mcps per pixel. Parallax effect was observed along the arcs but was corrected. Reconstructed images from two energy bins demonstrated good contrast, spatial resolution and image uniformity, with little artifacts.Conclusion:The CZT detector tested is suitable for imaging relatively small objects. A stationary spectral CT appears feasible should we have enough buttable modules and a distributed source.This work was supported by Ministry of Science and Technology of China through grants 11YQ040082 and 2012BAI13B04.
TU‐EF‐204‐05: Task‐Driven Cerebral CT Perfusion Imaging
Authors: Li Ke , Niu Kai , Wu Yijing , Yang Pengfei , Chen Guang‐Hong 
Published: June 2015

Purpose:This work introduces a task‐driven framework to cerebral CT perfusion (CTP) imaging to improve the image quality and quantification accuracy of parametric perfusion maps used in the clinical assessment of acute ischemic strokes and stroke mimics.Methods:First, the concepts of cerebral anatomical noise, noise power spectrum (NPS), MTF, and generalized task‐based detectability index were developed for CTP imaging. Second, a spatial‐temporal (4D) cascaded system model was developed to establish an analytical and quantitative correspondence between system parameters and CTP imaging performance. Third, a library of CTP imaging tasks such as the detection of ischemic lesions with different sizes and severities of perfusion deficiencies were created under the guidance of a neurosurgeon. Finally, data acquisition, reconstruction, and processing parameters were prospectively optimized for each CTP imaging task for improved imaging performance. The proposed method was validated in both digital anthropomorphic 4D perfusion phantoms and in vivo canine stroke models.Results:The NPS, MTF, and the generalized task‐based detectability index predicted by the 4D cascaded model matched exactly with the experimental results. For both non‐deconvolution‐based and deconvolution‐based perfusion calculation methods, as long as the signal and noise properties of the input source CT images are known, the image quality and bias of the final CTP maps can be predicted. Guided by the tasked‐driven framework, a novel tube current modulation technique for the acquisition of source images was developed, and parameters involved in parametric perfusion map calculations were optimized. Through these innovations, perfusion maps of the canines demonstrated 80% reduction in image noise as well as significant improvement in the detectability of ischemic lesions and the quantitative accuracy of perfusion measurements.Conclusion:The task‐driven approach has successfully guided the improvement of conventional CTP imaging techniques, potentially enabling a leap in the image quality and quantification accuracy of parametric perfusion maps.K. Li, K. Niu, Y. Wu, P. Yang: Nothing to disclose. G.‐H. Chen: Research funded, GE Healthcare; Research funded, Siemens AX.
TU‐EF‐204‐06: Waveform Measurements On a Fast‐KV Switching CT System
Authors: Nute J , Shepard S , Jacobsen M , Cody D 
Published: June 2015

Purpose:To evaluate the timing and shape of the kilovoltage (kV) waveform for a fast‐kV switching dual‐energy CT system using a non‐invasive method.Methods:A kV divider with mobile filter pack was connected to a digital oscilloscope and scanned using a fast‐kV switching dual‐energy CT system in service mode. The tube was stationary, so exposure time was equivalent to rotation time. In this system, the generator switches quickly between two nominal voltages of 80kV and 140kV. The predicted waveform period was calculated given 1968 projections per exposure/rotation. Two Gemstone Spectral Imaging (GSI) service settings, GSI‐2 and GSI‐3, with exposure times of 0.8sec and 0.5sec, respectively, were analyzed. The data were binned into high (>110kV) and low (<110kV) projection regions. Waveform periods were calculated using the intersections of the waveform with 110kV. The waveform was analyzed for period accuracy, minimum and maximum kV, and average kV of the high and low kV projection regions. The effective spectral separation was the difference between the average kVs of the low and high projection regions.Results:The shape of the waveform was neither square nor sinusoidal. The minimum and maximum kVs were 81.6kV and 139.6kV for GSI‐2 (0.8sec) and 84.8kV and 131.6kV for GSI‐3 (0.5sec), which were within 6% of the nominal kVs. GSI‐2 and GSI‐3 had average waveform periods of 0.8134msec and 0.5064msec, respectively, within 0.5% of the calculated periods. The average kV for the high and low projections yielded effective spectral separations of 38.3kV (89.2kV to 127.5kV) for GSI‐2 (0.8sec) and 27.2kV (93.7kV to 121.4kV) for GSI‐3 (0.5sec).Conclusion:A method for analyzing the kV waveform of a fast‐kV switching x‐ray tube was demonstrated. The pseudo‐sinusoidal shape of the kV waveform lowers spectral separation compared to that of an ideal square wave, which may impact the quality of dual‐energy CT material decomposition.This research was conducted at the MD Anderson Center for Advanced Biomedical Imaging in‐part with equipment support from General Electric Healthcare
TU‐EF‐204‐07: Add Tube Current Modulation to a Low Dose Simulation Tool for CT Systems
Authors: Ding Y. , Wen G. , Brown K. , Klahr P. , Dhanantwari A. 
Published: June 2015

Purpose:We extended the capabilities of a low dose simulation tool to model Tube‐Current Modulation (TCM). TCM is widely used in clinical practice to reduce radiation dose in CT scans. We expect the tool to be valuable for various clinical applications (e.g., optimize protocols, compare reconstruction techniques and evaluate TCM methods).Methods:The tube current is input as a function of z location, instead of a fixed value. Starting from the line integrals of a scan, a new Poisson noise realization at a lower dose is generated for each view. To validate the new functionality, we compared simulated scans with real scans in image space.Results:First we assessed noise in the difference between the low‐dose simulations and the original high‐dose scan. When the simulated tube current is a step function of z location, the noise at each segment matches the noise of 3 separate constant‐tube‐current‐simulations. Secondly, with a phantom that forces TCM, we compared a low‐dose simulation with an equivalent real low‐dose scan. The mean CT number of the simulated scan and the real low‐dose scan were 137.7±0.6 and 137.8±0.5 respectively. Furthermore, with 240 ROIs, the noise of the simulated scan and the real low‐dose scan were 24.03±0.45 and 23.99±0.43 respectively, and they were not statistically different (2‐sample t‐test, p‐value=0.28). The facts that the noise reflected the trend of the TCM curve, and that the absolute noise measurements were not statistically different validated the TCM function.Conclusion:We successfully added tube‐current modulation functionality in an existing low dose simulation tool. We demonstrated that the noise reflected an input tube‐current modulation curve. In addition, we verified that the noise and mean CT number of our simulation agreed with a real low dose scan.The authors are all employees of Philips. Yijun Ding is also supported by NIBIB P41EB002035 and NIBIB R01EB000803.