bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐D‐207B‐04: Morphological Features of MRI as a Correlate of Capsular Contracture in Breast Cancer Patients with Implant‐Based Reconstructions
Authors: Tyagi N , Sutton E , Hunt M , Apte A , Zhang J , Oh J , Mechalakos J , Mehrara B , Matros E , Ho A 
Published: July 2016

Purpose:Capsular contracture (CC) is a serious complication in patients receiving implant‐based reconstruction for breast cancer. The goal of this study was to identify image‐based correlates of CC using MRI imaging in breast cancer patients who received both MRI and clinical evaluation following reconstructive surgery.Methods:We analyzed a retrospective dataset of 50 patients who had both a diagnostic MR and a plastic surgeon's evaluations of CC score (Baker's score) within a six month period following mastectomy and reconstructive surgery. T2w sagittal MRIs (TR/TE = 3500/102 ms, slice thickness = 4 mm) were used for morphological shape features (roundness, eccentricity, solidity, extent and ratio‐length) and histogram features (median, skewness and kurtosis) of the implant and the pectoralis muscle overlying the implant. Implant and pectoralis muscles were segmented in 3D using Computation Environment for Radiological Research (CERR) and shape and histogram features were calculated as a function of Baker's score.Results:Shape features such as roundness and eccentricity were statistically significant in differentiating grade 1 and grade 2 (p = 0.009; p = 0.06) as well as grade 1 and grade 3 CC (p = 0.001; p = 0.006). Solidity and extent were statistically significant in differentiating grade 1 and grade 3 CC (p = 0.04; p = 0.04). Ratio‐length was statistically significant in differentiating all grades of CC except grade 2 and grade 3 that showed borderline significance (p = 0.06). The muscle thickness, median intensity and kurtosis were significant in differentiating between grade 1 and grade 3 (p = 0.02), grade 1 and grade 2 (p = 0.03) and grade 1 and grade 3 (p = 0.01) respectively.Conclusion:Morphological shape features described on MR images were associated with the severity of CC. MRI may be important in objectively evaluating outcomes in breast cancer patients who undergo implant reconstruction.
SU‐D‐207B‐06: Predicting Breast Cancer Malignancy On DCE‐MRI Data Using Pre‐Trained Convolutional Neural Networks
Authors: Antropova N , Huynh B , Giger M 
Published: July 2016

Purpose:We investigate deep learning in the task of distinguishing between malignant and benign breast lesions on dynamic contrast‐enhanced MR images (DCE‐MRIs), eliminating the need for lesion segmentation and extraction of tumor features. We evaluate convolutional neural network (CNN) after transfer learning with ImageNet, a database of thousands of non‐medical images.Methods:Under a HIPAA‐compliant IRB protocol, a database of 551 (357 malignant and 194 benign) breast MRI cases was collected. ROIs around each lesion were extracted from the DCE‐MRI slices at the second post‐contrast time point. Depending on the size of the tumor, the dimensions of ROIs varied between 1 and 1.5 times the maximum diameter of the lesion, which were further upsampled or downsampled to yield 256×256‐pixel ROIs. Next, these ROIs were directly input to the convolutional neural network ConvNet, which had been pre‐trained by AlexNet on the ImageNet database. The internal layer of ConvNet output 4,096 features, which were subsequently used as the input to a support vector machine (SVM) to classify the lesions as malignant or benign. Area under the receiver operating characteristic curve (AUC) was used as a figure of merit for the classification task. We performed 10‐fold cross‐validation with training and testing sets consisting of 90% and 10% of the database, respectively.Results:The CNN with transfer learning and subsequent SVM yielded an an AUC value of 0.85, demonstrating the predictive value of the CNN. In future work, we will compare the results obtained with ConvNet with the results obtained using conventional tumor radiomics features. Our approach returns the prediction within minutes, due to incorporation of transfer learning.Conclusion:A CNN pre‐trained on non‐medical images can be used to extract image characteristics from breast DCE‐MR images relevant to diagnostic decision‐making. Our work shows that transfer learning can aid in prediction of breast cancer malignancy.M. Giger is a stockholder in R2/Hologic, co‐founder and equity holder in Quantitative Insights, and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba
SU‐D‐207B‐07: Development of a CT‐Radiomics Based Early Response Prediction Model During Delivery of Chemoradiation Therapy for Pancreatic Cancer
Authors: Klawikowski S , Christian J , Schott D , Zhang M , Li X 
Published: July 2016

Purpose:Pilot study developing a CT‐texture based model for early assessment of treatment response during the delivery of chemoradiation therapy (CRT) for pancreatic cancer.Methods:Daily CT data acquired for 24 pancreatic head cancer patients using CT‐on‐rails, during the routine CT‐guided CRT delivery with a radiation dose of 50.4 Gy in 28 fractions, were analyzed. The pancreas head was contoured on each daily CT. Texture analysis was performed within the pancreas head contour using a research tool (IBEX). Over 1300 texture metrics including: grey level co‐occurrence, run‐length, histogram, neighborhood intensity difference, and geometrical shape features were calculated for each daily CT. Metric‐trend information was established by finding the best fit of either a linear, quadratic, or exponential function for each metric value verses accumulated dose. Thus all the daily CT texture information was consolidated into a best‐fit trend type for a given patient and texture metric. Linear correlation was performed between the patient histological response vector (good, medium, poor) and all combinations of 23 patient subgroups (statistical jackknife) determining which metrics were most correlated to response and repeatedly reliable across most patients. Control correlations against CT scanner, reconstruction kernel, and gated/nongated CT images were also calculated. Euclidean distance measure was used to group/sort patient vectors based on the data of these trend‐response metrics.Results:We found four specific trend‐metrics (Gray Level Coocurence Matrix311‐1InverseDiffMomentNorm, Gray Level Coocurence Matrix311‐1InverseDiffNorm, Gray Level Coocurence Matrix311‐1 Homogeneity2, and Intensity Direct Local StdMean) that were highly correlated with patient response and repeatedly reliable. Our four trend‐metric model successfully ordered our pilot response dataset (p=0.00070). We found no significant correlation to our control parameters: gating (p=0.7717), scanner (p=0.9741), and kernel (p=0.8586).Conclusion:We have successfully created a CT‐texture based early treatment response prediction model using the CTs acquired during the delivery of chemoradiation therapy for pancreatic cancer. Future testing is required to validate the model with more patient data.
SU‐F‐SPS‐03: Direct Measurement of Organ Doses Resulting From Head and Cervical Spine Trauma CT Protocols
Authors: Carranza C , Lipnharski I , Quails N , Correa N , Rill L , Arreola M 
Published: July 2016

Purpose:This retrospective study analyzes the exposure history of emergency department (ED) patients undergoing head and cervical spine trauma computed tomography (CT) studies. This study investigated dose levels received by trauma patients and addressed any potential concerns regarding radiation dose issues.Methods:Under proper IRB approval, a cohort of 300 trauma cases of head and cervical spine trauma CT scans received in the ED was studied. The radiological image viewing software of the hospital was used to view patient images and image data. The following parameters were extracted: the imaging history of patients, the reported dose metrics from the scanner including the volumetric CT Dose Index (CTDIvol) and Dose Length Product (DLP). A postmortem subject was scanned using the same scan techniques utilized in a standard clinical head and cervical spine trauma CT protocol with 120 kVp and 280 mAs. The CTDIvol was recorded for the subject and the organ doses were measured using optically stimulated luminescent (OSL) dosimeters. Typical organ doses to the brain, thyroid, lens, salivary glands, and skin, based on the cadaver studies, were then calculated and reported for the cohort.Results:The CTDIvol reported by the CT scanner was 25.5 mGy for the postmortem subject. The average CTDIvol from the patient cohort was 34.1 mGy. From these metrics, typical average organ doses in mGy were found to be: Brain (44.57), Thyroid (33.40), Lens (82.45), Salivary Glands (61.29), Skin (47.50). The imaging history of the cohort showed that on average trauma patients received 26.1 scans over a lifetime.Conclusion:The average number of scans received on average by trauma ED patients shows that radiation doses in trauma patients may be a concern. Available dose tracking software would be helpful to track doses in trauma ED patients, highlighting the importance of minimizing unnecessary scans and keeping doses ALARA.
SU‐F‐E‐02: A Feasibility Study for Application of Metal Artifact Reduction Techniques in MR‐Guided Brachytherapy Gynecological Cancer with Titanium Applicators
Authors: Kadbi M 
Published: July 2016

Purpose:Utilization of Titanium Tandem and Ring (T&R) applicators in MR‐guided brachytherapy has become widespread for gynecological cancer treatment. However, Titanium causes magnetic field disturbance and susceptibility artifact, which complicate image interpretation. In this study, metal artifact reduction techniques were employed to improve the image quality and reduce the metal related artifacts.Methods:Several techniques were employed to reduce the metal artifact caused by titanium T&R applicator. These techniques include Metal Artifact Reduction Sequence (MARS), View Angle Tilting (VAT) to correct in‐plane distortion, and Slice Encoding for Metal Artifact Correction (SEMAC) for through‐plane artifact correction. Moreover, MARS can be combined with VAT to further reduce the in‐plane artifact by reapplying the selection gradients during the readout (MARS+VAT). SEMAC uses a slice selective excitation but acquires additional z‐encodings in order to resolve off‐resonant signal and to reduce through‐plane distortions.Results:Comparison between the clinical sequences revealed that increasing the bandwidth reduces the error in measured diameter of T&R. However, the error is larger than 4mm for the best case with highest bandwidth and spatial resolution. MARS+VAT with isotropic resolution of 1mm reduced the error to 1.9mm which is the least among the examined 2D sequences. The measured diameter of tandem from SEMAC+VAT has the closest value to the actual diameter of tandem (3.2mm) and the error was reduced to less than 1mm. In addition, SEMAC+VAT significantly reduces the blooming artifact in the ring compared to clinical sequences.Conclusion:A higher bandwidth and spatial resolution sequence reduces the artifact and diameter of applicator with a slight compromise in SNR. Metal artifact reduction sequences decrease the distortion associated with titanium applicator. SEMAC+VAT sequence in combination with VAT revealed promising results for titanium imaging and can be utilized for MR‐guided brachytherapy in gynecological cancer.The author is employee with Philips Healthcare
SU‐F‐E‐09: Respiratory Signal Prediction Based On Multi‐Layer Perceptron Neural Network Using Adjustable Training Samples
Authors: Sun W , Jiang M , Yin F 
Published: July 2016

Purpose:Dynamic tracking of moving organs, such as lung and liver tumors, under radiation therapy requires prediction of organ motions prior to delivery. The shift of moving organ may change a lot due to huge transform of respiration at different periods. This study aims to reduce the influence of that changes using adjustable training signals and multi‐layer perceptron neural network (ASMLP).Methods:Respiratory signals obtained using a Real‐time Position Management(RPM) device were used for this study. The ASMLP uses two multi‐layer perceptron neural networks(MLPs) to infer respiration position alternately and the training sample will be updated with time. Firstly, a Savitzky‐Golay finite impulse response smoothing filter was established to smooth the respiratory signal. Secondly, two same MLPs were developed to estimate respiratory position from its previous positions separately. Weights and thresholds were updated to minimize network errors according to Leverberg‐Marquart optimization algorithm through backward propagation method. Finally, MLP 1 was used to predict 120∼150s respiration position using 0∼120s training signals. At the same time, MLP 2 was trained using 30∼150s training signals. Then MLP is used to predict 150∼180s training signals according to 30∼150s training signals. The respiration position is predicted as this way until it was finished.Results:In this experiment, the two methods were used to predict 2.5 minute respiratory signals. For predicting 1s ahead of response time, correlation coefficient was improved from 0.8250(MLP method) to 0.8856(ASMLP method). Besides, a 30% improvement of mean absolute error between MLP(0.1798 on average) and ASMLP(0.1267 on average) was achieved. For predicting 2s ahead of response time, correlation coefficient was improved from 0.61415 to 0.7098.Mean absolute error of MLP method(0.3111 on average) was reduced by 35% using ASMLP method(0.2020 on average).Conclusion:The preliminary results demonstrate that the ASMLP respiratory prediction method is more accurate than MLP method and can improve the respiration forecast accuracy.
SU‐F‐E‐10: Student‐Driven Exploration of Radiographic Material Properties, Phantom Construction, and Clinical Workflows Or: The Extraordinary Life of CANDY MAN
Authors: Mahon RN , Riblett MJ , Hugo GD 
Published: July 2016

Purpose:To develop a hands‐on learning experience that explores the radiological and structural properties of everyday items and applies this knowledge to design a simple phantom for radiotherapy exercises.Methods:Students were asked to compile a list of readily available materials thought to have radiation attenuation properties similar to tissues within the human torso. Participants scanned samples of suggested materials and regions of interest (ROIs) were used to characterize bulk attenuation properties. Properties of each material were assessed via comparison to a Gammex Tissue characterization phantom and used to construct a list of inexpensive near‐tissue‐equivalent materials. Critical discussions focusing on samples found to differ from student expectations were used to revise and narrow the comprehensive list. From their newly acquired knowledge, students designed and constructed a simple thoracic phantom for use in a simulated clinical workflow. Students were tasked with setting up the phantom and acquiring planning CT images for use in treatment planning and dose delivery.Results:Under engineer and physicist supervision, students were trained to use a CT simulator and acquired images for approximately 60 different foodstuffs, candies, and household items. Through peer discussion, students gained valuable insights and were made to review preconceptions about radiographic material properties. From a subset of imaged materials, a simple phantom was successfully designed and constructed to represent a human thorax. Students received hands‐on experience with clinical treatment workflows by learning how to perform CT simulation, create a treatment plan for an embedded tumor, align the phantom for treatment, and deliver a treatment fraction.Conclusion:In this activity, students demonstrated their ability to reason through the radiographic material selection process, construct a simple phantom to specifications, and exercise their knowledge of clinical workflows. Furthermore, the enjoyable and inexpensive nature of this project proved to attract participant interest and drive creative exploration.Mahon and Riblett have nothing to disclose; Hugo has a research agreement with Phillips Medical systems, a license agreement with Varian Medical Systems, research grants from the National Institute of Health. Authors do not have any potential conflicts of interest to disclose.
SU‐F‐E‐17: A Dedicated Teaching and Research Linac as a Stepping Stone to Introduce Medical Physics to Students
Authors: Beaulieu L , Gingras L , Bergeron M , Archambault L 
Published: July 2016

Purpose:This work describes how a non‐clinical, research and teaching Linac is used as an extremely motivating and exciting way to introduce students to medical physics.Methods:The dedicated facility was inaugurated in 2014. The facility is composed of a fully equipped and functional state‐of‐the‐art Varian TrueBeam Linac and a complete set of physics instruments and QA phantoms for the Linac and onboard imaging. The Linac bunker and treatment console are oversized such that a class of 12–15 can comfortably fit, seated if needed for longer sessions. A 3cr undergraduate laboratory course that includes medical imaging, x‐ray source characterization (mAs, kVp, and filtering) and many others including an introductory Linac laboratory was created. The latter is composed of one general 4‐hours session and a weekly 4‐hours session for teams of two students. The general session includes a hands‐on presentation of the Linac, its environment and a formal safety and radiation protection course (with an exam).Results:Since the winter of 2015, senior undergraduate (total of 15) pursuing either the medical physics or the biomedical engineering tracks can register. At the Linac, the students are allowed full control of the experiments, including set‐up and irradiation. Supervisor intervention is limited to safety concerns for students or equipment. Measurements of output factors using two chambers (regular and small field) for various field sizes (1×1 to 30×30 cm2) and of detailed depth‐dose curves for 6 MV, 6 and 12 MeV beams are to be performed and discussed in a formal report.Conclusion:Full access to, and control of, a Linac is the high point of this course. It provides a glimpse of medical physics and generates an experimental background for those continuing to CAMPEP programs. This dedicated, non‐clinical facility further enable enhance CAMPEP graduate teaching and research activities not possible with a clinical device.
SU‐F‐E‐21: Change in Retinal Arteriole Diameter with Visual Simulation
Authors: Tellapragada N , Burns S 
Published: July 2016

Purpose:Neural activity and blood flow in the brain are tightly coupled. This coupling allows the brain to respond to periods of increased neural activity with increased blood flow. This coupling is known as neurovascular coupling. Many vascular based imaging techniques such as Functional MRI scans provide maps of signals of brain activity but they are limited by the resolution of fMRI to a few mm. The fMRI signal is indirect because the scanner is not tracking the neural activity directly but are measuring the changes in the blood oxygen levels. Since the retina and optic tract are part of the central nerves system and they can be measured optically it should be possible to make precise measurements of the retinal vasculature of the human retina and its response to changing stimulation levels.Methods:In this study we used an adaptive optics scanning laser ophthalmoscope (AOSLO) with multiply scattered light to measure the change in arteriolar diameter when the retina was stimulated with flickering light. We hypothesized that we could use this technique to measure both arterial dilation and time course. We used information from the reflectance of the vessel to measure total vessel diameter.Results:Images were acquired at approximately 30 Hz and averaged over 3.3 second periods. Retinal arteries were measured in five observers before, during, and after presentation of a large flickering stimulus. There was a 6–10% dilation of the blood vessels during the flicker. The Vascular dilation occurred within seconds of flickering onset and constricted again following the end of flicker stimulation.Conclusion:This work shows that with modern retinal imaging methods it is possible to make precise measures of vascular constriction and its time course in response to changing tissue demand.
SU‐F‐P‐06: Moving From Computed Radiography to Digital Radiography: A Collaborative Approach to Improve Image Quality
Authors: Sandoval D , Valenti D , Bateman T , Norris V , Mlady G , Selwyn R 
Published: July 2016

Purpose:To bring together radiologists, technologists, and physicists to utilize post‐processing techniques in digital radiography (DR) in order to optimize image acquisition and improve image quality.Methods:Sub‐optimal images acquired on a new General Electric (GE) DR system were flagged for follow‐up by radiologists and reviewed by technologists and medical physicists. Various exam types from adult musculoskeletal (n=35), adult chest (n=4), and pediatric (n=7) were chosen for review. 673 total images were reviewed. These images were processed using five customized algorithms provided by GE. An image score sheet was created allowing the radiologist to assign a numeric score to each of the processed images, this allowed for objective comparison to the original images. Each image was scored based on seven properties: 1) overall image look, 2) soft tissue contrast, 3) high contrast, 4) latitude, 5) tissue equalization, 6) edge enhancement, 7) visualization of structures. Additional space allowed for additional comments not captured in scoring categories. Radiologists scored the images from 1 – 10 with 1 being non‐diagnostic quality and 10 being superior diagnostic quality. Scores for each custom algorithm for each image set were summed. The algorithm with the highest score for each image set was then set as the default processing.Results:Images placed into the PACS “QC folder” for image processing reasons decreased. Feedback from radiologists was, overall, that image quality for these studies had improved. All default processing for these image types was changed to the new algorithm.Conclusion:This work is an example of the collaboration between radiologists, technologists, and physicists at the University of New Mexico to add value to the radiology department. The significant amount of work required to prepare the processing algorithms, reprocessing and scoring of the images was eagerly taken on by all team members in order to produce better quality images and improve patient care.