bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐F‐R‐57: Validation of Quantitative Radiomic Texture Features for Oncologic MRI: A Simulation Study
Authors: Yang F , Ford J , Dogan N 
Published: July 2016

Purpose:Radiomic texture features extracted from diverse MRI modalities have been investigated regarding their predictive and prognostic values in a variety of cancers. However, their validity has not yet been fully assessed. With the aid of a digital MRI phantom, the objective of this study was to examine the validity and reliability of MRI texture metrics.Methods:MR signal of the employed digital phantom was simulated in a multiple coil setting with realistic acquisition noise. Three iterative algorithms based respectively on conjugate gradient (CG), total variation (TV), and wavelet regularization (WL) were used for image reconstruction. For each algorithm, 80 independent simulations were carried out with different levels of noise perturbation. 22 features related to grey‐level co‐occurrence matrices (GLCOM), zone size matrices (GLZSM), and neighborhood difference matrices (GLNDM) were evaluated for each resultant image within two ROIs featuring heterogeneous patterns of signal intensity on the ground truth image. Texture features extracted from these simulated images were compared to those from the ground truth image, and differences were identified.Results:In comparison to the ground truth data, texture features appearing on images reconstructed with CG, TV, and WL from signal with no noise perturbation varied with a range of 0.18–3.3×104%, 0.57–3.6×104%, and 0.84–3.5×104%, respectively while varying more significantly for noisy data with largest variation of 2.6×105% for CG, 2.2×105% for TV, and 2.8×105% for WL. Texture differences between ROIs also revealed considerable extent of variation from those on the ground truth image with a range of 1.91–4.9×104% for CG‐based data, 27.50–3.0×104% for TV‐based, and 11.99‐3.2×104% for WL‐based.Conclusion:Variability of texture appearance on MRI with respect to the choice of reconstruction algorithm and noise level is significant and feature‐dependent. Certain texture features may be preserved by MR imaging; however adequate precautions need to be taken on their validity and reliability.
SU‐F‐I‐02: Comparative Analysis and Constancy Check of Image Quality Parameters for Three Linear Accelerators Per TG 142 Protocol
Authors: Altundal Y , Pokhrel D , Jiang H , Badkul R 
Published: July 2016

Purpose:To compare image quality parameters and assessing the image stability of three different linear accelerators (linac) for 2D and 3D imaging modalities: planar kV, MV images and cone‐beam CT (CBCT).Methods:QCkV1, QC‐3 and Cathpan‐600 phantoms were utilized to acquire kV, MV and CBCT images respectively on monthly basis per TG142 QA protocol for over 2 years on 21Ex, NovalisTx and TrueBeam linacs. DICOM images were analyzed with the help of QA analysis software: PIPsPro from Standard Imaging. For planar kV and MV images, planar spatial resolution, contrast to noise ratio (CNR) and noise; for CBCT, HU values were collected and analyzed.Results:Two years of monthly QA measurements were analyzed for the planar and CBCT images. Values were normalized to the mean and the standard deviations (STD) are presented. For the kV planar radiographic images the STD of spatial resolution for f30, f40, f50, CNR and noise for 21Ex are 0.006, 0.011, 0.013, 0.046, 0.026; Novalis‐Tx are 0.009, 0.016, 0.016, 0.067, 0.053; TrueBeam are 0.007, 0.005, 0.009, 0.017, 0.016 respectively. For the MV planar radiographic images, the STD of spatial resolution for f30, f40, f50, CNR and noise for 21Ex are 0.009, 0.010, 0.008, 0.023, 0.023; for Novalix‐Tx are 0.012, 0.010, 0.008, 0.029, 0.023 and for TrueBeam are 0.010, 0.010, 0.007, 0.022, 0.022 respectively. For the CBCT images, HU constancies of Air, Polystyrene, Teflon, PMP, LDPE and Delrin for 21Ex are 0.014, 0.070, 0.031, 0.053, 0.076, 0.087; for Novalis Tx are 0.019, 0.047, 0.035, 0.059, 0.077, 0.087 and for TrueBeam are 0.011, 0.044, 0.025, 0.044, 0.056, 0.020 respectively.Conclusion:These Imaging QA results demonstrated that the TrueBeam, performed better in terms of image quality stability for both kV planer and CBCT images as well as EPID MV images, however other two linacs were also satisfied TG142 guidelines.
SU‐F‐I‐06: Evaluation of Imaging Dose for Modulation Layer Based Dual Energy Cone‐Beam CT
Authors: Ju Eunbin , Ahn SoHyun , Cho Samju , Keum Ki Chang , Lee Rena 
Published: July 2016

Purpose:Dual energy cone beam CT system is finding a variety of promising applications in diagnostic CT, both in imaging of endogenous materials and exogenous materials across a range of body sites. Dual energy cone beam CT system to suggest in this study acquire image by rotating 360 degree with half of the X‐ray window covered using copper modulation layer. In the region that covered by modulation layer absorb the low energy X‐ray by modulation layer. Relative high energy X‐ray passes through the layer and contributes to image reconstruction. Dose evaluation should be carried out in order to utilize such an imaging acquirement technology for clinical use.Methods:For evaluating imaging dose of modulation layer based dual energy cone beam CT system, Prototype cone beam CT that configured X‐ray tube (D054SB, Toshiba, Japan) and detector (PaxScan 2520V, Varian Medical Systems, Palo Alto, CA) is used. A range of 0.5–2.0 mm thickness of modulation layer is implemented in Monte Carlo simulation (MCNPX, ver. 2.6.0, Los Alamos National Laboratory, USA) with half of X‐ray window covered. In‐house phantom using in this study that has 3 cylindrical phantoms configured water, Teflon air with PMMA covered for verifying the comparability the various material in human body and is implemented in Monte Carlo simulation. The actual dose with 2.0 mm copper covered half of X‐ray window is measured using Gafchromic EBT3 film with 5.0 mm bolus for compared with simulative dose.Results:Dose in phantom reduced 33% by copper modulation layer of 2.0 mm. Scattering dose occurred in modulation layer by Compton scattering effect is 0.04% of overall dose.Conclusion:Modulation layer of that based dual energy cone beam CT has not influence on unnecessary scatter dose.This study was supported by the Radiation Safety Research Programs (1305033) through the Nuclear Safety and Security Commission.
SU‐F‐I‐10: Spatially Local Statistics for Adaptive Image Filtering
Authors: Iliopoulos AS , Floros D , Zhang Y , Pitsianis N , Sun X , Yin FF , Ren L 
Published: July 2016

Purpose:To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone‐beam projections, where physical effects such as X‐ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression.Methods:We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter‐scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi‐ or un‐supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non‐adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi‐scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures.Results:We demonstrate the impact of local statistics for adaptive image processing and analysis using cone‐beam projections of a Catphan phantom, fitted within an annulus to increase X‐ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi‐scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low‐contrast regions.Conclusion:Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi‐scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration.NIH Grant No. R01‐184173
SU‐F‐I‐12: Region‐Specific Dictionary Learning for Low‐Dose X‐Ray CT Reconstruction
Authors: Xu Q , Han H , Xing L 
Published: July 2016

Purpose:Dictionary learning based method has attracted more and more attentions in low‐dose CT due to the superior performance on suppressing noise and preserving structural details. Considering the structures and noise vary from region to region in one imaging object, we propose a region‐specific dictionary learning method to improve the low‐dose CT reconstruction.Methods:A set of normal‐dose images was used for dictionary learning. Segmentations were performed on these images, so that the training patch sets corresponding to different regions can be extracted out. After that, region‐specific dictionaries were learned from these training sets. For the low‐dose CT reconstruction, a conventional reconstruction, such as filtered back‐projection (FBP), was performed firstly, and then segmentation was followed to segment the image into different regions. Sparsity constraints of each region based on its dictionary were used as regularization terms. The regularization parameters were selected adaptively according to different regions. A low‐dose human thorax dataset was used to evaluate the proposed method. The single dictionary based method was performed for comparison.Results:Since the lung region is very different from the other part of thorax, two dictionaries corresponding to lung region and the rest part of thorax respectively were learned to better express the structural details and avoid artifacts. With only one dictionary some artifact appeared in the body region caused by the spot atoms corresponding to the structures in the lung region. And also some structure in the lung regions cannot be recovered well by only one dictionary. The quantitative indices of the result by the proposed method were also improved a little compared to the single dictionary based method.Conclusion:Region‐specific dictionary can make the dictionary more adaptive to different region characteristics, which is much desirable for enhancing the performance of dictionary learning based method.
SU‐F‐I‐16: Short Breast MRI with High‐Resolution T2‐Weighted and Dynamic Contrast Enhanced T1‐Weighted Images
Authors: Ma J , Son J , Arun B , Hazle J , Hwang K , Wang K , Bayram E , Madewell J , Yang W , Dogan B 
Published: July 2016

Purpose:To develop and demonstrate a short breast (sb) MRI protocol that acquires both T2‐weighted and dynamic contrast‐enhanced T1‐weighted images in approximately ten minutes.Methods:The sb‐MRI protocol consists of two novel pulse sequences. The first is a flexible fast spin‐echo triple‐echo Dixon (FTED) sequence for high‐resolution fat‐suppressed T2‐weighted imaging, and the second is a 3D fast dual‐echo spoiled gradient sequence (FLEX) for volumetric fat‐suppressed T1‐weighted imaging before and post contrast agent injection. The flexible FTED sequence replaces each single readout during every echo‐spacing period of FSE with three fast‐switching bipolar readouts to produce three raw images in a single acquisition. These three raw images are then post‐processed using a Dixon algorithm to generate separate water‐only and fat‐only images. The FLEX sequence acquires two echoes using dual‐echo readout after each RF excitation and the corresponding images are post‐processed using a similar Dixon algorithm to yield water‐only and fat‐only images. The sb‐MRI protocol was implemented on a 3T MRI scanner and used for patients who had undergone concurrent clinical MRI for breast cancer screening.Results:With the same scan parameters (eg, spatial coverage, field of view, spatial and temporal resolution) as the clinical protocol, the total scan‐time of the sb‐MRI protocol (including the localizer, bilateral T2‐weighted, and dynamic contrast‐enhanced T1‐weighted images) was 11 minutes. In comparison, the clinical breast MRI protocol took 43 minutes. Uniform fat suppression and high image quality were consistently achieved by sb‐MRI.Conclusion:We demonstrated a sb‐MRI protocol comprising both T2‐weighted and dynamic contrast‐enhanced T1‐weighted images can be performed in approximately ten minutes. The spatial and temporal resolution of the images easily satisfies the current breast MRI accreditation guidelines by the American College of Radiology. The protocol has the potential of making breast MRI more widely accessible to and more tolerable by the patients.JMA is the inventor of United States patents that are owned by the University of Texas Board of Regents and currently licensed to GE Healthcare and Siemens Gmbh.
SU‐F‐I‐23: 1H Magnetic Resonance Spectroscopy Baseline Correction WithSingular Value Decomposition Method
Authors: Feng W , Yang S , Hu J 
Published: July 2016

Purpose:Baseline distortions in NMR spectra are caused by the corruption of the first few data points in Free Induction Decay (FID) or originated from macromolecules and lipids. To study the Singular Value Decomposition (SVD) based baseline correction method.Methods:In‐house Singular Value Decomposition (SVD) program was developed with Matlab version 7.6 (Mathworks.com). Hankel SVD method (singular value decomposition of the acquired FID signal arranged in a Hankel matrix) is used to compute the signal poles and amplitude, and from them the signal frequencies and damping factors. The estimated FID signals are constructed from the quantification parameters; residue error spectrum was calculated by FFT of the difference of original FID and estimated FID. In order to study the baseline correction effect, known simulated FID with added noise to mimic known metabolites concentration was analyzed with SVD, quantification results were compared with known reference; then baseline distortion was introduced by removing the first 5 data points, after baseline corrected with SVD method, the quantification results were compared with original reference without baseline distortion.Known patient FID acquired with Siemens Verio 3T scanner was introduced baseline distortion by removing the first 3 data points, after baseline corrected with SVD method, corrected FID were compared with original results without baseline distortion.Results:Quantification results for simulated data were compared with reference, all within 10% deviation with SNR 20.For FID acquired with Siemens Verio 3T scanner, distorted FID and calculated baseline with SVD method was compared, the baseline matches the distortion well. Corrected FID were used for quantification, the quantification results match the corrected FID well.Conclusion:As demonstrated, SVD based baseline correction method can be used to correct the FID data point missing type of distortion, results are satisfactory for both simulated data and acquired patient data.
SU‐F‐I‐25: Reproducibility of Phosphorus MRS and Work Measurements for Evaluating Skeletal Muscle Metabolism
Authors: Settles F , Ripley E , Shengwen D , Clarke G 
Published: July 2016

Purpose:Skeletal muscle energetics were studied by phosphorus‐31 MRS (31P‐MRS) during exercise. The reproducibility of work performed and the MRS parameters were investigated.Methods:Reproducibility of the 31P‐MRS exercise parameters was validated in five healthy volunteers (2 M/3 F, age 39.2 ± 21.9 yr, BMI 25.1 ± 4.8 kg/m2). Five minute exercise of the vastus lateralis (VL) was performed on two separate occasions during MRS in a 3 Tesla MRI system. The exercise (up to 13.5 kg resistance) was measured on a strain gage. The summation of force applied (strain level) over time (Δt = 20 ms) was taken as the work output. The phosphocreatine (PCr) recovery time constant (k) following exercise was determined from nonlinear least squares fitting of MRS spectra acquired every 3 s to the Levenberg‐Marquardt equation. Maximum oxidative capacity was calculated from the baseline [PCr] and k, as previously described. The protocol was approved by the IRB.Results:The percent drop of PCr = 40.7 ± 13.4% and the rate of PCr recovery was 0.16 ± 0.03 mM•s−1 (k = 0.018 ± 0.003 s−1). The mean recovery time, τ, of PCr was 40.0 ± 8.1 s, which corresponds well with current literature values in healthy subjects (32 ± 3 s to 53 ± 25 s). Bland‐Altman analysis showed poor reproducibility (LOA ratio = 55.7) in work performed h, the k‐value for PCr was very reproducible (bias = −1.28*10−4, random error = 3.32*10−3). The % drop in PCr was negatively but significantly correlated with the oxidative phosphorylation capacity (r= −0.84, p=0.003).Conclusion:[PCr] changes during VL exercise and recovery were reproducible and not affected by inconsistent work being performed during exercise bouts. The results suggest that this system should be useful for relating work vs. metabolism differences between subjects with differing metabolic profiles.
SU‐F‐I‐26: Maximum Likelihood and Nested Model Selection Techniques for Pharmacokinetic Analysis of Dynamic Contrast Enhanced MRI in Patients with Glioblastoma Tumors
Authors: Bagher‐Ebadian H , Dehkordi A , Ewing J 
Published: July 2016

Purpose:This pilot study introduces a novel approach for estimation of pharmacokinetic parameters in dynamic contrast enhanced magnetic resonance imaging (DCE‐MRI). In this study Maximum Likelihood (ML) and Nested Model Selection (NMS) techniques are combined to construct an approximately unbiased estimator for DCE‐MRI data analysis.Methods:DCE T1‐weighted MRI using the contrast agent (CA) gadopentetate dimeglumine was performed on 20 patients with Glioblastoma tumor. ML Estimation (MLE) technique was recruited for optimizing 3 physiologically nested models constructed based on the extended Tofts model in the course of DCE‐MRI experimental. The Log‐Likelihood‐Ratio (LLR) measures for three nested models were used to choose the best model explaining the variation of the experimental DCE‐MRI data and to estimate its Pharmacokinetic (PK) parameters. The observed information matrix or the matrix of Log‐Likelihood was used to estimate the variance and co‐variance of the estimated PK parameters for each of selected models.Results:The PK parameters along with the model choice maps estimated by the MLE and NMS are highly in agreement with the physiological condition of underlying pathology and the values for the permeability parameters of the brain reported by the literature. The low variance and covariance measures of the estimated PK parameters being reasonably in‐range imply that the proposed estimator is robust for estimation of physiological parameters in DCE‐MRI studies.Conclusion:This pilot study confirms that only three parameters of the standard model are sufficient to fit the most complicated time trace of CA concentration in DCE‐T1 weighted studies for GBM tumors under the conditions of the experiment.This study is supported in part by Dykstra Family (F60570) and mentored grants (A10237)
SU‐F‐I‐30: Minimization of Resolution Loss for CT Scans of the Elbow at Lateral Position
Authors: Zhou Y , Scott A 
Published: July 2016

Purpose:Resolution is vital in elbow CT in order to examine the details of fractures. However, it is significantly compromised when the elbow is at the lateral position due to the body attenuation and off‐center position. We attempted to find the parameters to minimize the resolution loss while keeping the dose ALARA.Methods:To simulate the medium and large patient respectively, an abdomen phantom (CIRS TE07) was used and then a fat‐ring was added. A small tissue‐equivalent phantom (CIRS TE02) simulating the elbow was connected to another smaller phantom (CIRS TE01). A resolution insert (CIRS QA) was placed inside the elbow phantom. The two‐phantom set was first placed in the superior position and helical scans were performed using a GE VCT with 120 kVp, 0.531 pitch, 0.625 mm thickness and CTDI_vol of 21 mGy. Similar scans were performed after the set was moved to the body side with varied CTDI_vol (39‐ 90 mGy). Different choices were utilized including scan modes of “plus” (thicker slice profile) and “full”, kernels of “edge”, “bone” and “detail”, and different ASIR fractions.Results:Resolution deterioration was significant when the elbow phantom was at the body side. The scans with the “plus” mode improved the resolution as compared with the “full” mode. For the medium sized phantom, the CTDI_vol increase from 40 mGy to 67 mGy did not improve the resolution. For the large phantom, the CTDI_vol increase from 67 mGy to 80 mGy and 90 mGy did not improve the resolution either. Furthermore, ASIR blending bigger than 50% deteriorated the resolution.Conclusion:Resolution loss can be minimized using scans with the “Plus” mode, bone kernel, and ASIR blending of 0%‐ 30%. CTDI_vol values larger than 40 mGy and larger than 67 mGy, for the medium sized and large sized phantoms, respectively, do not improve the resolution.
SU‐F‐I‐32: Organ Doses from Pediatric Head CT Scan
Authors: Liu H , Liu Q , Qiu J , Majer M , Knezevic Z , Miljanic S , Hrsak H , Zhuo W 
Published: July 2016

Purpose:To evaluate the organ doses of pediatric patients who undergoing head CT scan using Monte Carlo (MC) simulation and compare it with measurements in anthropomorphic child phantom..Methods:A ten years old children voxel phantom was developed from CT images, the voxel size of the phantom was 2mm*2mm*2mm. Organ doses from head CT scan were simulated using MCNPX software, 180 detectors were placed in the voxel phantom to tally the doses of the represented tissues or organs. When performing the simulation, 120 kVp and 88 mA were selected as the scan parameters. The scan range covered from the top of the head to the end of the chain, this protocol was used at CT simulator for radiotherapy. To validate the simulated results, organ doses were measured with radiophotoluminescence (RPL) detectors, placed in the 28 organs of the 10 years old CIRS ATOM phantom.Results:The organ doses results matched well between MC simulation and phantom measurements. The eyes dose was showed to be as expected the highest organ dose: 28.11 mGy by simulation and 27.34 mGy by measurement respectively. Doses for organs not included in the scan volume were much lower than those included in the scan volume, thymus doses were observed more than 10 mGy due the CT protocol for radiotherapy covered more body part than routine head CT scan.Conclusion:As the eyes are superficial organs, they may receive the highest radiation dose during the CT scan. Considering the relatively high radio sensitivity, using shielding material or organ based tube current modulation technique should be encouraged to reduce the eye radiation risks. Scan range was one of the most important factors that affects the organ doses during the CT scan. Use as short as reasonably possible scan range should be helpful to reduce the patient radiation dose.This work was supported by the National Natural Science Foundation of China(11475047)