bluuebunny/crossref_metadata_2025_split huggingface dataset:
WE‐G‐204‐08: Optimized Digital Radiographic Technique for Lost Surgical Devices/Needle Identification
Authors: Gorman A , Seabrook G , Brakken A , Dubois M , Marn C , Wilson C , Jacobson D , Liu Y 
Published: June 2015

Purpose:Small surgical devices and needles are used in many surgical procedures. Conventionally, an x‐ray film is taken to identify missing devices/needles if post procedure count is incorrect. There is no data to indicate smallest surgical devices/needles that can be identified with digital radiography (DR), and its optimized acquisition technique.Methods:In this study, the DR equipment used is a Canon RadPro mobile with CXDI‐70c wireless DR plate, and the same DR plate on a fixed Siemens Multix unit. Small surgical devices and needles tested include Rubber Shod, Bulldog, Fogarty Hydrogrip, and needles with sizes 3‐0 C‐T1 through 8‐0 BV175‐6. They are imaged with PMMA block phantoms with thickness of 2–8 inch, and an abdomen phantom. Various DR techniques are used. Images are reviewed on the portable x‐ray acquisition display, a clinical workstation, and a diagnostic workstation.Results:all small surgical devices and needles are visible in portable DR images with 2–8 inch of PMMA. However, when they are imaged with the abdomen phantom plus 2 inch of PMMA, needles smaller than 9.3 mm length can not be visualized at the optimized technique of 81 kV and 16 mAs. There is no significant difference in visualization with various techniques, or between mobile and fixed radiography unit. However, there is noticeable difference in visualizing the smallest needle on a diagnostic reading workstation compared to the acquisition display on a portable x‐ray unit.Conclusion:DR images should be reviewed on a diagnostic reading workstation. Using optimized DR techniques, the smallest needle that can be identified on all phantom studies is 9.3 mm. Sample DR images of various small surgical devices/needles available on diagnostic workstation for comparison may improve their identification. Further in vivo study is needed to confirm the optimized digital radiography technique for identification of lost small surgical devices and needles.
WE‐G‐207‐02: Full Sequential Projection Onto Convex Sets (FS‐POCS) for X‐Ray CT Reconstruction
Authors: Liu L , Han Y , Jin M 
Published: June 2015

Purpose:To develop an iterative reconstruction method for X‐ray CT, in which the reconstruction can quickly converge to the desired solution with much reduced projection views.Methods:The reconstruction is formulated as a convex feasibility problem, i.e. the solution is an intersection of three convex sets: 1) data fidelity (DF) set – the L2 norm of the difference of observed projections and those from the reconstructed image is no greater than an error bound; 2) non‐negativity of image voxels (NN) set; and 3) piecewise constant (PC) set ‐ the total variation (TV) of the reconstructed image is no greater than an upper bound. The solution can be found by applying projection onto convex sets (POCS) sequentially for these three convex sets. Specifically, the algebraic reconstruction technique and setting negative voxels as zero are used for projection onto the DF and NN sets, respectively, while the projection onto the PC set is achieved by solving a standard Rudin, Osher, and Fatemi (ROF) model. The proposed method is named as full sequential POCS (FS‐POCS), which is tested using the Shepp‐Logan phantom and the Catphan600 phantom and compared with two similar algorithms, TV‐POCS and CP‐TV.Results:Using the Shepp‐Logan phantom, the root mean square error (RMSE) of reconstructed images changing along with the number of iterations is used as the convergence measurement. In general, FS‐ POCS converges faster than TV‐POCS and CP‐TV, especially with fewer projection views. FS‐POCS can also achieve accurate reconstruction of cone‐beam CT of the Catphan600 phantom using only 54 views, comparable to that of FDK using 364 views.Conclusion:We developed an efficient iterative reconstruction for sparse‐view CT using full sequential POCS. The simulation and physical phantom data demonstrated the computational efficiency and effectiveness of FS‐POCS.
WE‐G‐207‐04: Non‐Local Total‐Variation (NLTV) Combined with Reweighted L1‐Norm for Compressed Sensing Based CT Reconstruction
Authors: Kim H , Chen J , Pouliot J 
Published: June 2015

Purpose:Compressed sensing (CS) has been used for CT (4DCT/CBCT) reconstruction with few projections to reduce dose of radiation. Total‐variation (TV) in L1‐minimization (min.) with local information is the prevalent technique in CS, while it can be prone to noise. To address the problem, this work proposes to apply a new image processing technique, called non‐local TV (NLTV), to CS based CT reconstruction, and incorporate reweighted L1‐norm into it for more precise reconstruction.Methods:TV minimizes intensity variations by considering two local neighboring voxels, which can be prone to noise, possibly damaging the reconstructed CT image. NLTV, contrarily, utilizes more global information by computing a weight function of current voxel relative to surrounding search area. In fact, it might be challenging to obtain an optimal solution due to difficulty in defining the weight function with appropriate parameters. Introducing reweighted L1‐min., designed for approximation to ideal L0‐min., can reduce the dependence on defining the weight function, therefore improving accuracy of the solution. This work implemented the NLTV combined with reweighted L1‐min. by Split Bregman Iterative method. For evaluation, a noisy digital phantom and a pelvic CT images are employed to compare the quality of images reconstructed by TV, NLTV and reweighted NLTV.Results:In both cases, conventional and reweighted NLTV outperform TV min. in signal‐to‐noise ratio (SNR) and root‐mean squared errors of the reconstructed images. Relative to conventional NLTV, NLTV with reweighted L1‐norm was able to slightly improve SNR, while greatly increasing the contrast between tissues due to additional iterative reweighting process.Conclusion:NLTV min. can provide more precise compressed sensing based CT image reconstruction by incorporating the reweighted L1‐norm, while maintaining greater robustness to the noise effect than TV min.
WE‐G‐207‐05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis
Authors: Lee J , Nishikawa R , Reiser I , Boone J 
Published: June 2015

Purpose:Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis.Methods:A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty‐three image features were extracted after segmentation using a semi‐automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave‐one‐out‐cross‐validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist's drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared.Results:Moderate correlation (Pearson's rho = 0.43, p‐value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance.Conclusion:There are certain images that yield better segmentation or classification performance. The best segmentation Result does not necessarily lead to the best classification Result.This work has been supported in part by grants from the NIH R21‐EB015053. R Nishikawa is receives royalties form Hologic, Inc.
WE‐G‐207‐07: Iterative CT Shading Correction Method with No Prior Information
Authors: Wu P , Mao T , Xie S , Sheng K , Niu T 
Published: June 2015

Purpose:Shading artifacts are caused by scatter contamination, beam hardening effects and other non‐ideal imaging condition. Our Purpose is to propose a novel and general correction framework to eliminate low‐frequency shading artifacts in CT imaging (e.g., cone‐beam CT, low‐kVp CT) without relying on prior information.Methods:Our method applies general knowledge of the relatively uniform CT number distribution in one tissue component. Image segmentation is applied to construct template image where each structure is filled with the same CT number of that specific tissue. By subtracting the ideal template from CT image, the residual from various error sources are generated. Since the forward projection is an integration process, the non‐continuous low‐frequency shading artifacts in the image become continuous and low‐frequency signals in the line integral. Residual image is thus forward projected and its line integral is filtered using Savitzky‐Golay filter to estimate the error. A compensation map is reconstructed on the error using standard FDK algorithm and added to the original image to obtain the shading corrected one. Since the segmentation is not accurate on shaded CT image, the proposed scheme is iterated until the variation of residual image is minimized.Results:The proposed method is evaluated on a Catphan600 phantom, a pelvic patient and a CT angiography scan for carotid artery assessment. Compared to the one without correction, our method reduces the overall CT number error from >200 HU to be <35 HU and increases the spatial uniformity by a factor of 1.4.Conclusion:We propose an effective iterative algorithm for shading correction in CT imaging. Being different from existing algorithms, our method is only assisted by general anatomical and physical information in CT imaging without relying on prior knowledge. Our method is thus practical and attractive as a general solution to CT shading correction.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.
WE‐G‐207‐09: A Practical Bowtie Ring Artifact Correction Algorithm for Cone‐Beam CT
Authors: Wu P , Mao T , Xie S , Sheng K , Niu T , Niu T 
Published: June 2015

Purpose:Shaded ring artifact in cone‐beam CT (CBCT) is caused by scatter contamination from bowtie‐modulated primary beam and presents a unique ring pattern in CBCT image. Previous shading correction methods, which suppress the artifact as conventional scatter correction in projection domain, are complicated due to the non‐trivial scatter estimation. In this work, we propose a practical and readily implementable algorithm to correct for the severe shaded ring in CBCT without relying on prior information and directly in image domain.Methods:Due to the correlation between the ring pattern and bowtie modulator, an initial bowtie mask was reconstructed from bowtie‐modulated air‐scan projections as if they were acquired from a flat‐field exposure penetrating the bowtie modulator. The shape of the bowtie mask matches well with that of the shaded ring in CBCT, while its intensity needs to be scaled to fully compensate for the shading ring. To find a correct scaling factor, we start from the anatomical knowledge that the same tissue has comparable CT number. This knowledge indicates a sharp peak in histogram of that specific tissue. One way to achieve this goal is to maximize the peak value of the histogram of that tissue. The above concept is formulated as a mathematical optimization problem which is solved using a standard Simplex method. The shaded ring artifact in CBCT is finally corrected for by adding a scaled bowtie mask.Results:The proposed method is evaluated on one pelvis patient. Severe shaded ring artifact is greatly suppressed. Our method reduces the CT number error from >200 HU to be ∼50 HU, and increases the spatial uniformity by 1.3 times.Conclusion:We propose a practical image‐domain algorithm for shaded ring artifact correction in CBCT. It is computationally efficient and does not rely on prior knowledge. It is thus attractive for clinical use.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.
WE‐G‐201‐02: Image Quality Characteristics of Handheld Medical Image Viewing Devices
Authors: Badano A. 
Published: June 2015

Many health care organizations are adopting mobile applications for patient care. These tools can provide the opportunity for improvements in timely access to patient images. With new possibilities for patient image viewing, it is sensible to take a closer look at what handheld devices might provide and how they are being used.Three speakers: a radiologist, an imaging research scientist, and a clinical medical physicist provide input into different aspects of image viewing on handheld devices.1. Patrick Luetmer, M.D., RadiologistProvides a radiologist's perspective of how mobile technologies best fit into patient care workflows, including how patient image viewing is done differently on a mobile platform compared to a PACS workstation and what types of decisions are being made from handheld devices.2. Aldo Badano, Ph.D., Imaging Research ScientistProvides an overview of technological properties of handheld devices and describes how image viewing with handheld devices impacts the image information that is visible under ideal and clinical conditions.3. Dr. Alisa Walz‐Flannigan, Ph.D., Clinical Medical PhysicistProvides an overview of different strategies for setup and quality control as relevant for handheld devices.This session is related to the ongoing work of Task Group No. 260 “Considerations for the Use of Handheld Devices for Viewing Medical Images”.Learning Objectives:

To understand how mobile devices for image viewing are being used to enhance patient care
To understand how technological properties of various mobile devices combine with device usage to impact the information visible in a medical image
To gain awareness of different options for configuring and testing handheld devices for medical image viewing
Dr. Aldo Badano has the following: Cooperative Research and Development Agreement (CRADA #123‐07) with BARCO.
WE‐G‐201‐04: Panel Discussion
Authors: Walz‐Flannigan A. 
Published: June 2015

Many health care organizations are adopting mobile applications for patient care. These tools can provide the opportunity for improvements in timely access to patient images. With new possibilities for patient image viewing, it is sensible to take a closer look at what handheld devices might provide and how they are being used.Three speakers: a radiologist, an imaging research scientist, and a clinical medical physicist provide input into different aspects of image viewing on handheld devices.1. Patrick Luetmer, M.D., RadiologistProvides a radiologist's perspective of how mobile technologies best fit into patient care workflows, including how patient image viewing is done differently on a mobile platform compared to a PACS workstation and what types of decisions are being made from handheld devices.2. Aldo Badano, Ph.D., Imaging Research ScientistProvides an overview of technological properties of handheld devices and describes how image viewing with handheld devices impacts the image information that is visible under ideal and clinical conditions.3. Dr. Alisa Walz‐Flannigan, Ph.D., Clinical Medical PhysicistProvides an overview of different strategies for setup and quality control as relevant for handheld devices.This session is related to the ongoing work of Task Group No. 260 “Considerations for the Use of Handheld Devices for Viewing Medical Images”.Learning Objectives:

To understand how mobile devices for image viewing are being used to enhance patient care
To understand how technological properties of various mobile devices combine with device usage to impact the information visible in a medical image
To gain awareness of different options for configuring and testing handheld devices for medical image viewing
Dr. Aldo Badano has the following: Cooperative Research and Development Agreement (CRADA #123‐07) with BARCO.
WE‐G‐210‐03: Mammographically Configured, Automated Ultrasound: Methods and Potential Applications
Authors: Carson P. 
Published: June 2015

This session focuses on recent developments in breast ultrasound imaging, covering clinical practice as well as clinical and pre‐clinical research. Current status of ultrasound screening will be reviewed, followed by discussions of emerging technologies at various stages of regulatory and technical development. The session strives to provide the attendee with a view of a continuum of imaging advances starting with the present and spanning the near and long term future.Learning Objectives:

Understand current state of ultrasound screening for breast cancer
Learn about current availability of whole breast ultrasound
Appreciate the current barriers to stronger adoption of screening ultrasound
Learn about attempts to drive adoption of screening ultrasound (e.g. breast density laws)
Learn about ultrasound tomography as an emerging technique for breast imaging
Learn about the regulatory status of emerging techniques such as ultrasound tomography
Learn about the developmental status of combined 3‐D ultrasound and Mammography systems
Learn about photo‐acoustic imaging and its potential future role in breast imaging
WE‐G‐210‐04: Photoacoustic Computed Tomography with Applications to Breast Imaging
Authors: Anastasio M. 
Published: June 2015

This session focuses on recent developments in breast ultrasound imaging, covering clinical practice as well as clinical and pre‐clinical research. Current status of ultrasound screening will be reviewed, followed by discussions of emerging technologies at various stages of regulatory and technical development. The session strives to provide the attendee with a view of a continuum of imaging advances starting with the present and spanning the near and long term future.Learning Objectives:

Understand current state of ultrasound screening for breast cancer
Learn about current availability of whole breast ultrasound
Appreciate the current barriers to stronger adoption of screening ultrasound
Learn about attempts to drive adoption of screening ultrasound (e.g. breast density laws)
Learn about ultrasound tomography as an emerging technique for breast imaging
Learn about the regulatory status of emerging techniques such as ultrasound tomography
Learn about the developmental status of combined 3‐D ultrasound and Mammography systems
Learn about photo‐acoustic imaging and its potential future role in breast imaging
TH‐AB‐304‐01: Dose‐Volume Relations for Late Rectal Bleeding in 1001 Patients From Five Prostate Cancer Cohorts
Authors: Thor M , Jackson A , Steineck G , Zelefsky M , Karlsdottir A , Hoyer M , Liu M , Nasser N , Petersen S , Moiseenko V , Deasy J 
Published: June 2015

Purpose:Normal tissue complications following radiotherapy (RT) are commonly estimated from single institutions, limiting generalizability of critical dose‐volume thresholds. In this study we use data from five cohorts/institutions to explore dose‐volume relations for late rectal bleeding (LRB) after RT for prostate cancer.Methods:The investigated cohorts included 1001 patients treated with various RT techniques for prostate cancer (922 3DCRT, 211 image‐guided RT, and 79 intensity‐modulated RT patients) to 70–86 Gy@2Gy/fraction in 1991–2007. The rectum extended from the recto‐sigmoid flexure to the anal canal, and the median (range) follow‐up for LRB was 58 (4–259) months. Rectal cross sectional area, length, and volume were compared between LRB and non‐ LRB patients (Mann‐Whitney‐test). The ability of dose metrics to predict moderate‐to‐severe LRB (prevalence: 14%) was investigated on univariate analysis, UVA, (Spearman's Rho (Rs) and p‐values calculated as medians from 10 0000 Bootstrap‐resamples). Dose‐volume metrics significantly predicting LRB on UVA (p≤0.05) were considered for multivariate logistic regression, MVA, following removal of correlated metrics (Pearson's correlation, Pr ≥0.85).Results:Patients with LRB had significantly (p<0.01) smaller and shorter rectums than non‐LRB patients (48 vs. 68 cm3 and 6 vs. 7 cm, respectively). On UVA, the relative volumes receiving 5–20 Gy, V5–V20 (Rs=0.05–0.07), V40–V65 (Rs=0.05–0.11), the minimum dose to the hottest 20–55% volumes, D20‐D55 and D100 (Rs=0.06–0.09), and mean dose (Rs=0.05) significantly (p<0.03) predicted LRB. Within a range of correlated dose metrics (Pr=0.86–0.98), the metric showing the strongest relationship with LRB was considered for MVA together with the other dose metrics, length and volume. The length, V50, mean dose, and D35% remained significant (p<0.01) predictors for LRB on MVA, MVALRB=5.2*V50+6.1*D35+9.2*mean dose+0.2*length.Conclusion:Our findings (>1000 patients) suggest that shorter rectums and intermediate to high doses predict LRB. The wide range of prescribed doses and treatment techniques applied support generalizability of our results to other prostate cancer cohorts.