bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐F‐R‐36: Validating Quantitative Radiomic Texture Features for Oncologic PET: A Digital Phantom Study
Authors: Yang F , Yang Y , Young L
Published: July 2016

Purpose:

Radiomic texture features derived from the oncologic PET have recently been brought under intense investigation within the context of patient stratification and treatment outcome prediction in a variety of cancer types; however, their validity has not yet been examined. This work is aimed to validate radiomic PET texture metrics through the use of realistic simulations in the ground truth setting.

Methods:

Simulation of FDG‐PET was conducted by applying the Zubal phantom as an attenuation map to the SimSET software package that employs Monte Carlo techniques to model the physical process of emission imaging. A total of 15 irregularly‐shaped lesions featuring heterogeneous activity distribution were simulated. For each simulated lesion, 28 texture features in relation to the intensity histograms (GLIH), grey‐level co‐occurrence matrices (GLCOM), neighborhood difference matrices (GLNDM), and zone size matrices (GLZSM) were evaluated and compared with their respective values extracted from the ground truth activity map.

Results:

In reference to the values from the ground truth images, texture parameters appearing on the simulated data varied with a range of 0.73–3026.2% for GLIH‐based, 0.02–100.1% for GLCOM‐based, 1.11–173.8% for GLNDM‐based, and 0.35–66.3% for GLZSM‐based. For majority of the examined texture metrics (16/28), their values on the simulated data differed significantly from those from the ground truth images (P‐value ranges from <0.0001 to 0.04). Features not exhibiting significant difference comprised of GLIH‐based standard deviation, GLCO‐based energy and entropy, GLND‐based coarseness and contrast, and GLZS‐based low gray‐level zone emphasis, high gray‐level zone emphasis, short zone low gray‐level emphasis, long zone low gray‐level emphasis, long zone high gray‐level emphasis, and zone size nonuniformity.

Conclusion:

The extent to which PET imaging disturbs texture appearance is feature‐dependent and could be substantial. It is thus advised that use of PET texture parameters for predictive and prognostic measurements in oncologic setting awaits further systematic and critical evaluation.
SU‐F‐R‐41: Regularized PCA Can Model Treatment‐Related Changes in Head and Neck Patients Using Daily CBCTs
Authors: Chetvertkov M , Siddiqui F , Chetty I , Kumarasiri A , Liu C , Gordon J
Published: July 2016

Purpose:

To use daily cone beam CTs (CBCTs) to develop regularized principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients, to guide replanning decisions in adaptive radiation therapy (ART).

Methods:

Known deformations were applied to planning CT (pCT) images of 10 H&N patients to model several different systematic anatomical changes. A Pinnacle plugin was used to interpolate systematic changes over 35 fractions, generating a set of 35 synthetic CTs for each patient. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CTs and random fraction‐to‐fraction changes were superimposed on the DVFs. Standard non‐regularized and regularized patient‐specific PCA models were built using the DVFs. The ability of PCA to extract the known deformations was quantified. PCA models were also generated from clinical CBCTs, for which the deformations and DVFs were not known. It was hypothesized that resulting eigenvectors/eigenfunctions with largest eigenvalues represent the major anatomical deformations during the course of treatment.

Results:

As demonstrated with quantitative results in the supporting document regularized PCA is more successful than standard PCA at capturing systematic changes early in the treatment. Regularized PCA is able to detect smaller systematic changes against the background of random fraction‐to‐fraction changes. To be successful at guiding ART, regularized PCA should be coupled with models of when anatomical changes occur: early, late or throughout the treatment course.

Conclusion:

The leading eigenvector/eigenfunction from the both PCA approaches can tentatively be identified as a major systematic change during radiotherapy course when systematic changes are large enough with respect to random fraction‐to‐fraction changes. In all cases the regularized PCA approach appears to be more reliable at capturing systematic changes, enabling dosimetric consequences to be projected once trends are established early in the treatment course.

This work is supported in part by a grant from Varian Medical Systems, Palo Alto, CA
SU‐F‐R‐43: Recursive K‐Means Filter for Preserving Signals of Interest
Authors: Chu A , Yan P , Shih R , Wuu C
Published: July 2016

Purpose:

To use recursive cluster analysis to filter noises and artifacts out of useful signals. The method was applied to CT or CBCT for preserving interested low Hounsfield Unit (HU) signals such as lung tissues while the HU of lung tissues is often overlapped with CT (or CBCT) artifacts.

Methods:

Cluster analysis is to search for the similarity among data points, which could employ multi‐dimensional techniques. For our purpose, we searched for a robust, fast, and automatic process for sorting interested low HU object in CT, Therefore, 1‐D cluster analysis is suitable for the purpose. However, the 1‐D cluster analysis might not be able to sensitively separate the groups with overlap in values, for example, lung tissue and low‐HU CT artifacts. We found the recursive k‐mean (initialized by k‐mean++ over Euclidean distance) with division of small size groups can efficiently strip the low noises out and reveal the interested group by a repetitive fashion.

Results:

The algorithm of repetitively regrouping and filtering with a size of small division can effectively remove the low‐HU values of artifacts. Because artifacts are usually scattered separate groups, the lung tissues buried in the low‐HU groups can be revealed with an appropriate recursive steps.

Conclusion:

The filtering technique is efficient, robust and can be applied to many different applications as long as the distribution of interested signals can be considered as a cluster in value.
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)
SU‐F‐I‐34: How Does Longitudinal Dose Profile Change with Tube Current Distribution in CT?
Authors: Li X , Yang K , Liu B
Published: July 2016

Purpose:

To investigate how longitudinal dose profile DL(z) in 30 cm‐diameter water cylinder change with tube current (mA) distribution and scan length.

Methods:

A constant and four variable mA distributions from two previous papers [Dixon et al., Med. Phys. 40, 111920 (14pp.) (2013); Zhang et al., Med. Phys. 41, 091911 (9pp.) (2014)] were adopted in three scan lengths of 10, 28.6, and 50 cm, and all mA distributions had the same average mA over scan ranges. Using the symmetry based dose calculation algorithms and the previously published CT dose equilibration data [Li et al., Med. Phys. 40, 031903 (10pp.) (2013); 41, 111910 (5pp.) (2014)], the authors calculated DL(z) on the phantom central and peripheral axes. Kolmogorov‐Smirnov (K‐S) test was used to compare the lineshapes of two arbitrary distributions.

Results:

In constant mA scans, DL(z) was “bell‐shaped”. In variable mA scans, DL(z) approximately followed the mA lineshape, and the K‐S distance generally changed with mA distribution. The distance decreased with scan length, and was larger on the central axis than on the peripheral axis. However, the opposite trends were found in the K‐S distance between the DL(z) distributions of constant and variable mA distributions.

Conclusion:

Radiation dose from TCM scan is best evaluated using the specific tube current distribution. A constant mA based evaluation may lead to inconsistent longitudinal dose profile with that of TCM scan. Their difference in lineshape is larger on the phantom peripheral axis than on the central axis and increases with scan length. This work confirms that radiation dose in CT depends on not only local mA but also the overall mA distribution and scan length. On the other hand, the concept of regional tube current may be useful when scan length is large, tube current peaks near scan range edge, or the target site is superficial.
SU‐F‐I‐36: In‐Utero Dose Measurements Within Postmortem Subjects for Estimating Fetal Doses in Pregnant Patients Examined with Pulmonary Embolism, Trauma, and Appendicitis CT
Authors: Lipnharski I , Quails N , Carranza C , Correa N , Bidari S , Bickelhaup M , Rill L , Arreola M
Published: July 2016

Purpose:

The imaging of pregnant patients is medically necessary in certain clinical situations. The purpose of this work was to directly measure uterine doses in a cadaver scanned with CT protocols commonly performed on pregnant patients in order to estimate fetal dose and assess potential risk.

Method:

One postmortem subject was scanned on a 320‐slice CT scanner with standard pulmonary embolism, trauma, and appendicitis protocols. All protocols were performed with the scan parameters and ranges currently used in clinical practice. Exams were performed both with and without iterative reconstruction to highlight the dose savings potential. Optically stimulated luminescent dosimeters (OSLDs) were inserted into the uterus in order to approximate fetal doses.

Results:

In the pulmonary embolism CT protocol, the uterus is outside of the primary beam, and the dose to the uterus was under 1 mGy. In the trauma and appendicitis protocols, the uterus is in the primary beam, the fetal dose estimates were 30.5 mGy for the trauma protocol, and 20.6 mGy for the appendicitis protocol. Iterative reconstruction reduced fetal doses by 30%, with uterine doses at 21.3 for the trauma and 14.3 mGy for the appendicitis protocol.

Conclusion:

Fetal doses were under 1 mGy when exposed to scatter radiation, and under 50 mGy when exposed to primary radiation with the trauma and appendicitis protocols. Consistent with the National Council on Radiation Protection & Measurements (NCRP) and the International Commission on Radiological Protection (ICRP), these doses exhibit a negligible risk to the fetus, with only a small increased risk of cancer. Still, CT scans are not recommended during pregnancy unless the benefits of the exam clearly outweigh the potential risk. Furthermore, when possible, pregnant patients should be examined on CT scanners equipped with iterative reconstruction in order to keep patient doses as low as reasonable achievable.
SU‐F‐I‐37: How Fat Distribution and Table Height Affect Estimates of Patient Size in CT Scanning: A Phantom Study
Authors: Silosky M , Marsh R
Published: July 2016

Purpose:

Localizer projection radiographs acquired prior to CT scans are used to estimate patient size, affecting the function of Automatic Tube Current Modulation (ATCM) and hence CTDIvol and SSDE. Due to geometric effects, the projected patient size varies with scanner table height and with the orientation of the localizer (AP versus PA). This study sought to determine if patient size estimates made from localizer scans is affected by variations in fat distribution, specifically when the widest part of the patient is not at the geometric center of the patient.

Methods:

Lipid gel bolus material was wrapped around an anthropomorphic phantom to simulate two different body mass distributions. The first represented a patient with fairly rigid fat and had a generally oval shape. The second was bell‐shaped, representing corpulent patients more susceptible to gravity's lustful tug. Each phantom configuration was imaged using an AP localizer and then a PA localizer. This was repeated at various scanner table heights. The width of the phantom was measured from the localizer and diagnostic images using in‐house software.

Results:

1) The projected phantom width varied up to 39% as table height changed.2) At some table heights, the width of the phantom, designed to represent larger patients, exceeded the localizer field of view, resulting in an underestimation of the phantom width.3) The oval‐shaped phantom approached a normalized phantom width of 1 at a table height several centimeters lower (AP localizer) or higher (PA localizer) than did the bell‐shaped phantom.

Conclusion:

Accurate estimation of patient size from localizer scans is dependent on patient positioning with respect to scanner isocenter and is limited in large patients. Further, patient size is more accurately measured on projection images if the widest part of the patient, rather than the geometric center of the patient, is positioned at scanner isocenter.
SU‐F‐I‐38: Patient Organ Specific Dose Assessment in Coronary CT Angiograph Using Voxellaized Volume Dose Index in Monte Carlo Simulation
Authors: Fallal Mohammadi Gh. , Riyahi Alam N. , Graily Gh. , Paydar R.
Published: July 2016

Purpose:

Clinical use of multi detector computed tomography(MDCT) in diagnosis of diseases due to high speed in data acquisition and high spatial resolution is significantly increased. Regarding to the high radiation dose in CT and necessity of patient specific radiation risk assessment, the adoption of new method in the calculation of organ dose is completely required and necessary. In this study by introducing a conversion factor, patient organ dose in thorax region based on CT image data using MC system was calculated.

Methods:

The geometry of x‐ray tube, inherent filter, bow tie filter and collimator were designed using EGSnrc/BEAMnrc MC‐system component modules according to GE‐Light‐speed 64‐slices CT‐scanner geometry. CT‐scan image of patient thorax as a specific phantom was voxellised with 6.25mm3 in voxel and 64×64×20 matrix size. Dose to thorax organ include esophagus, lung, heart, breast, ribs, muscle, spine, spinal cord with imaging technical condition of prospectively‐gated‐coronary CT‐Angiography(PGT) as a step and shoot method, were calculated. Irradiation of patient specific phantom was performed using a dedicated MC‐code as DOSXYZnrc with PGT‐irradiation model. The ratio of organ dose value calculated in MC‐method to the volume CT dose index(CTDIvol) reported by CT‐scanner machine according to PGT radiation technique has been introduced as conversion factor.

Results:

In PGT method, CTDIvol was 10.6mGy and Organ Dose/CTDIvol conversion factor for esophagus, lung, heart, breast, ribs, muscle, spine and spinal cord were obtained as; 0.96, 1.46, 1.2, 3.28. 6.68. 1.35, 3.41 and 0.93 respectively.

Conclusion:

The results showed while, underestimation of patient dose was found in dose calculation based on CTDIvol, also dose to breast is higher than the other studies. Therefore, the method in this study can be used to provide the actual patient organ dose in CT imaging based on CTDIvol in order to calculation of real effective dose(ED) based on organ dose.

This work has been supported by the research chancellor of tehran university of medical sciences(tums), school of medicine, Tehran, Iran
SU‐F‐I‐39: In Search of Infinity: Finding the Limiting Dose for An Infinite CT Scan On a Cylinder of Finite Length
Authors: Bakalyar D
Published: July 2016

Purpose:

The ICRU/TG200 phantom is a cylinder of polyethylene, 30 cm in diameter and 60 cm long. The dose h(L) in the central plane of the phantom resulting from a scan of length L increases asymptotically with increasing L to a limiting value Deq. However, even after scanning the entire length of this phantom, it is clear that the resultant dose h(60 cm) is still somewhat below the limiting value. The known behavior of h(L) provides a means for estimating the true limit.

Methods:

h(L) approaches its limiting but unknown value Deq exponentially. By estimating Deq as Deq* and plotting Deq* − h(L) as a function of L on a semi‐log scale, a straight line will result only if Deq* = Deq. If not, there will be significant curvature at the end of the plotted data. Adjusting Deq* by trial‐and‐error or by an iterative scheme will, if done correctly, accurately determine Deq.

Results:

The log of Deq* − h(L) was plotted as a function of L using Microsoft Excel. The coefficient of determination (R‐squared) was displayed and Deq* was adjusted until R‐squared equaled 1. Alternatively, iteration using the Solver tool in Excel can automatically find the best estimate of Deq. The resultant values for Deq was were an increase of around 1.5% above h(60 cm) for the center and 0.8% at the periphery.

Conclusion:

The experiments show that a 60 cm phantom is long enough for the central dose to be within a couple of percentage points of what would be achieved with an infinite phantom. Though this small underestimation of Deq is of little consequence for dose estimates, an accurate determination of Deq allows for a better parameterization of continuous functions representing h(L).
SU‐F‐I‐40: Impact of Scan Length On Patient Dose in Abdomen/pelvis CT Diagnosis
Authors: Park I , Song J , Kim K
Published: July 2016

Purpose:

To analysis the impact of scan length on patient doses in abdomen/pelvis CT diagnosis of each hospital.

Methods:

Scan length of 7 hospitals from abdomen/pelvis CT diagnosis was surveyed in Korea. Surveyed scan lengths were additional distance above diaphragm and distance below pubic symphysis except for standard scan range between diaphragm and pubic symphysis. Patient dose was estimated for adult male and female according to scan length of each hospital. CT‐Expo was used to estimate the patient dose under identical equipment settings (120 kVp, 100 mAs, 10 mm collimation width, etc.) except scan length. Effective dose was calculated by using tissue weighting factor of ICRP 103 recommendation. Increase rate of effective dose was calculated comparing with effective dose of standard scan range

Results:

Scan lengths of abdomen/pelvis CT diagnosis of each hospital were different. Also effective dose was increased with increasing the scan length. Generally increasing the distance above diaphragm caused increase of effective dose of male and female, but increasing the distance below pubic symphysis caused increase of effective dose of male.

Conclusion:

We estimated the patient dose according to scan length of each hospital in abdomen/pelvis CT diagnosis. Effective dose was increased by increasing the scan length because dose of organs with high tissue weighting factor such as lung, breast, testis were increased. Scan length is important factor on patient dose in CT diagnosis. If radiologic technologist interested in patient dose, decreasing the unnecessary scan length will decrease the risk of patients from radiation.

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI13C0004).
SU‐F‐I‐42: Evaluation of Multiple Atlas Segmentation Methods for the Abdomen
Authors: Pirozzi S , Nelson A
Published: July 2016

Purpose:

Atlas segmentation has the potential to provide time savings for contour generation in various treatment sites. Previously we evaluated the optimal number of atlas matches for head and neck, lung, liver, and prostate cancer. Our goal in the current work is to find the optimal number of atlas matches and method for combining atlas contours for an abdominal CT atlas.

Methods:

An abdominal CT atlas containing 37 subjects was utilized for atlas segmentation and was evaluated using a leave‐one‐out methodology. Each atlas subject contained manually defined contours of the aorta, left and right kidneys, large bowel, liver, spinal cord, stomach, and vertebral body. The one, three, four, five, and seven best matches from the atlas were automatically found for each subject and were deformed to the test subject. The contours were combined for each multi‐atlas method using Majority Vote (MV) and STAPLE. The average Dice Similarity Index was calculated for each method compared to manual contours.

Results:

Majority vote with five (MV‐5) and seven matches (MV‐7) had the highest dice for 6 out of the 8 structures including the aorta (0.75), left kidney (0.80), right kidney (0.83 and 0.84), liver (0.91), spinal cord (0.78), and vertebral body (0.71 and 0.72). For the large bowel, STAPLE‐5 and MV‐4 had the highest dice score (0.38 and 0.37), while MV4 provided the highest dice (0.65) for the stomach. The best method was statistically significant over the next best method for the aorta, left and right kidneys, liver, large bowel, and stomach with p‐values ranging from 0.0002 – 0.05.

Conclusion:

Atlas‐based segmentation using MV‐5 and MV‐7 provided the most accurate contours for the majority of structures, with only STAPLE‐5 and MV‐4 being more accurate for the large bowel and MV‐4 being more accurate for the stomach.

Aaron Nelson is a part owner and employee of MIM Software Inc. Sara Pirozzi is an employee of MIM Software Inc.
SU‐F‐I‐43: A Software‐Based Statistical Method to Compute Low Contrast Detectability in Computed Tomography Images
Authors: Chacko M , Aldoohan S
Published: July 2016

Purpose:

The low contrast detectability (LCD) of a CT scanner is its ability to detect and display faint lesions. The current approach to quantify LCD is achieved using vendor‐specific methods and phantoms, typically by subjectively observing the smallest size object at a contrast level above phantom background. However, this approach does not yield clinically applicable values for LCD. The current study proposes a statistical LCD metric using software tools to not only to assess scanner performance, but also to quantify the key factors affecting LCD. This approach was developed using uniform QC phantoms, and its applicability was then extended under simulated clinical conditions.

Methods:

MATLAB software was developed to compute LCD using a uniform image of a QC phantom. For a given virtual object size, the software randomly samples the image within a selected area, and uses statistical analysis based on Student's t‐distribution to compute the LCD as the minimal Hounsfield Unit's that can be distinguished from the background at the 95% confidence level. Its validity was assessed by comparison with the behavior of a known QC phantom under various scan protocols and a tissue‐mimicking phantom. The contributions of beam quality and scattered radiation upon the computed LCD were quantified by using various external beam‐hardening filters and phantom lengths.

Results:

As expected, the LCD was inversely related to object size under all scan conditions. The type of image reconstruction kernel filter and tissue/organ type strongly influenced the background noise characteristics and therefore, the computed LCD for the associated image.

Conclusion:

The proposed metric and its associated software tools are vendor‐independent and can be used to analyze any LCD scanner performance. Furthermore, the method employed can be used in conjunction with the relationships established in this study between LCD and tissue type to extend these concepts to patients’ clinical CT images.
SU‐F‐I‐44: Using the Air Signal to Characterize Image Noise in CT
Authors: Szczykutowicz T , Malkus A
Published: July 2016

Purpose:

With increasing emphasis on CT image quality and low dose, quality activities analyzing noise levels in images will need to be increasingly automated. This work describes a new automated method for characterizing noise in CT. To obviate the need to segment out anatomical structure, this work introduces the use of noise statistics of air to understand the noise in the scanned object.

Methods:

Scans of a uniform water phantom were acquired at four dose levels. Using a modified version of the global noise index, the image noise was calculated in both the air surrounding the phantom and in the phantom itself. The method involved subtracting adjacent image slices, breaking up the image into 7 mm square regions of interest (ROI), thresholding the ROIs by CT number to only include ROIs of air or water, and then taking the mean of the air or water ROIs’ standard deviation values. We refer to this metric as the meanGNI.

Results:

Not only does the meanGNI within the phantom decrease as dose increases, but also the meanGNI in the air exhibits the same behavior.

Conclusion:

The meanGNI in air is a useful proxy for meanGNI or GNI in the scanned object. Our air based method does not involve any edge detection or advanced signal processing. Therefore, no tuning of segmentation parameters is needed to be applicable to different types of anatomy (e.g. brain vs. lung).

Research and equipment support provided by GE Healthcare.