bluuebunny/crossref_metadata_2025_split huggingface dataset:
TU‐F‐BRF‐06: 3D Pancreas MRI Segmentation Using Dictionary Learning and Manifold Clustering
Authors: Gou S , Rapacchi S , Hu P , Sheng K 
Published: July 2014

Purpose:The recent advent of MRI guided radiotherapy machines has lent an exciting platform for soft tissue target localization during treatment. However, tools to efficiently utilize MRI images for such purpose have not been developed. Specifically, to efficiently quantify the organ motion, we develop an automated segmentation method using dictionary learning and manifold clustering (DLMC).Methods:Fast 3D HASTE and VIBE MR images of 2 healthy volunteers and 3 patients were acquired. A bounding box was defined to include pancreas and surrounding normal organs including the liver, duodenum and stomach. The first slice of the MRI was used for dictionary learning based on mean‐shift clustering and K‐SVD sparse representation. Subsequent images were iteratively reconstructed until the error is less than a preset threshold. The preliminarily segmentation was subject to the constraints of manifold clustering. The segmentation results were compared with the mean shift merging (MSM), level set (LS) and manual segmentation methods.Results:DLMC resulted in consistently higher accuracy and robustness than comparing methods. Using manual contours as the ground truth, the mean Dices indices for all subjects are 0.54, 0.56 and 0.67 for MSM, LS and DLMC, respectively based on the HASTE image. The mean Dices indices are 0.70, 0.77 and 0.79 for the three methods based on VIBE images. DLMC is clearly more robust on the patients with the diseased pancreas while LS and MSM tend to over‐segment the pancreas. DLMC also achieved higher sensitivity (0.80) and specificity (0.99) combining both imaging techniques. LS achieved equivalent sensitivity on VIBE images but was more computationally inefficient.Conclusion:We showed that pancreas and surrounding normal organs can be reliably segmented based on fast MRI using DLMC. This method will facilitate both planning volume definition and imaging guidance during treatment.
TU‐F‐BRF‐07: Accuracy of Routine Treatment Planning 4D and DIBH CT Delineation of the Left Anterior Descending Artery in Radiotherapy
Authors: White B , Vennarini S , Lin L , Freedmen G , Both S 
Published: July 2014

Purpose:To assess the feasibility of routine treatment planning 4DCT and deep inspiration breath‐hold (DIBH) to accurately contour the left anterior descending artery (LAD), a primary indicator of cardiac toxicity, for radiotherapy treatment planning of breast cancer.Methods:Ten subjects were imaged with a cardiac‐gated MRI protocol to determine the displacement of a ROI that included the LAD. The subjects performed a series of breath‐hold maneuvers to obtain short‐axis and radial views, which were resampled to create a 3D‐volume. Tissue motion was determined using a multi‐resolution 3D optical flow deformable image registration algorithm. The ROI motion was then used as a spatial boundary to characterize the blurring motion of the LAD in ten patients during clinical 4DCT and DIBH protocols. A radiologist contoured the LAD. Coronary motion‐induced blurring artifacts were quantified by applying an unsharp filter to accentuate the LAD despite motion‐blurring. The 4DCT maximum inhalation and exhalation respiratory phases were co‐registered to determine the LAD displacement during tidal respiration, as visualized in 4DCT.Results:The average 90th percentile heart motion for the ROI was 0.7±0.1mm(LR), 1.3±0.6mm(SI), 0.6±0.2mm(AP) in the cardiac‐gated MRI cohort. The average relative increase in the number of voxels comprising the LAD contour was 69.4±4.5% for the DIBH. During tidal respiration, the average relative increase in the LAD contour was 69.3±5.9% and 67.9±4.6% for inhalation and exhalation respiratory phases respectively. The average 90th percentile LAD motion was 4.8±1.1mm(LR), 0.9±0.4mm(SI), 1.9±0.6mm(AP) for the 4DCT cohort, in the absence of cardiac‐gating.Conclusion:Uncompensated coronary motion was the dominant form of motion blurring present in the CT images due to the high frequency of the cardiac cycle relative to the respiratory cycle. The 4D and DIBH CT contour delineation of the LAD was consistently overestimated without cardiac‐gating, which could have led to inaccurate dose volume histogram indicators in clinical practice.
TU‐F‐17A‐03: A 4D Lung Phantom for Coupled Registration/Segmentation Evaluation
Authors: Markel D , El Naqa I , Levesque I 
Published: July 2014

Purpose:Coupling the processes of segmentation and registration (regmentation) is a recent development that allows improved efficiency and accuracy for both steps and may improve the clinical feasibility of online adaptive radiotherapy. Presented is a multimodality animal tissue model designed specifically to provide a ground truth to simultaneously evaluate segmentation and registration errors during respiratory motion.Methods:Tumor surrogates were constructed from vacuum sealed hydrated natural sea sponges with catheters used for the injection of PET radiotracer. These contained two compartments allowing for two concentrations of radiotracer mimicking both tumor and background signals. The lungs were inflated to different volumes using an air pump and flow valve and scanned using PET/CT and MRI. Anatomical landmarks were used to evaluate the registration accuracy using an automated bifurcation tracking pipeline for reproducibility. The bifurcation tracking accuracy was assessed using virtual deformations of 2.6 cm, 5.2 cm and 7.8 cm of a CT scan of a corresponding human thorax. Bifurcations were detected in the deformed dataset and compared to known deformation coordinates for 76 points.Results:The bifurcation tracking accuracy was found to have a mean error of −0.94, 0.79 and −0.57 voxels in the left‐right, anterior‐posterior and inferior‐superior axes using a 1×1×5 mm3 resolution after the CT volume was deformed 7.8 cm. The tumor surrogates provided a segmentation ground truth after being registered to the phantom image.Conclusion:A swine lung model in conjunction with vacuum sealed sponges and a bifurcation tracking algorithm is presented that is MRI, PET and CT compatible and anatomically and kinetically realistic. Corresponding software for tracking anatomical landmarks within the phantom shows sub‐voxel accuracy. Vacuum sealed sponges provide realistic tumor surrogate with a known boundary. A ground truth with minimal uncertainty is thus realized that can be used for comparing the performance of registration and segmentation algorithms.
TU‐F‐17A‐04: Respiratory Phase‐Resolved 3D MRI with Isotropic High Spatial Resolution: Determination of the Average Breathing Motion Pattern for Abdominal Radiotherapy Planning
Authors: Deng Z , Pang J , Yang W , Yue Y , Tuli R , Fraass B , Li D , Fan Z 
Published: July 2014

Purpose:To develop a retrospective 4D‐MRI technique (respiratory phase‐resolved 3D‐MRI) for providing an accurate assessment of tumor motion secondary to respiration.Methods:A 3D projection reconstruction (PR) sequence with self‐gating (SG) was developed for 4D‐MRI on a 3.0T MRI scanner. The respiration‐induced shift of the imaging target was recorded by SG signals acquired in the superior‐inferior direction every 15 radial projections (i.e. temporal resolution 98 ms). A total of 73000 radial projections obtained in 8‐min were retrospectively sorted into 10 time‐domain evenly distributed respiratory phases based on the SG information. Ten 3D image sets were then reconstructed offline. The technique was validated on a motion phantom (gadolinium‐doped water‐filled box, frequency of 10 and 18 cycles/min) and humans (4 healthy and 2 patients with liver tumors). Imaging protocol included 8‐min 4D‐MRI followed by 1‐min 2D‐realtime (498 ms/frame) MRI as a reference.Results:The multiphase 3D image sets with isotropic high spatial resolution (1.56 mm) permits flexible image reformatting and visualization. No intra‐phase motion‐induced blurring was observed. Comparing to 2D‐realtime, 4D‐MRI yielded similar motion range (phantom: 10.46 vs. 11.27 mm; healthy subject: 25.20 vs. 17.9 mm; patient: 11.38 vs. 9.30 mm), reasonable displacement difference averaged over the 10 phases (0.74mm; 3.63mm; 1.65mm), and excellent cross‐correlation (0.98; 0.96; 0.94) between the two displacement series.Conclusion:Our preliminary study has demonstrated that the 4D‐MRI technique can provide high‐quality respiratory phase‐resolved 3D images that feature: a) isotropic high spatial resolution, b) a fixed scan time of 8 minutes, c) an accurate estimate of average motion pattern, and d) minimal intra‐phase motion artifact. This approach has the potential to become a viable alternative solution to assess the impact of breathing on tumor motion and determine appropriate treatment margins. Comparison with 4D‐CT in a clinical setting is warranted to assess the value of 4D‐MRI in radiotherapy planning.This work supported in part by grant 1R03CA173273‐01
TU‐F‐17A‐05: Calculating Tumor Trajectory and Dose‐Of‐The‐Day for Highly Mobile Tumors Using Cone‐Beam CT Projections
Authors: Jones B , Miften M 
Published: July 2014

Purpose:Cone‐beam CT (CBCT) projection images provide anatomical data in real‐time over several respiratory cycles, forming a comprehensive picture of tumor movement. We developed a method using these projections to determine the trajectory and dose of highly mobile tumors during each fraction of treatment.Methods:CBCT images of a respiration phantom were acquired, where the trajectory mimicked a lung tumor with high amplitude (2.4 cm) and hysteresis. A template‐matching algorithm was used to identify the location of a steel BB in each projection. A Gaussian probability density function for tumor position was calculated which best fit the observed trajectory of the BB in the imager geometry. Two methods to improve the accuracy of tumor track reconstruction were investigated: first, using respiratory phase information to refine the trajectory estimation, and second, using the Monte Carlo method to sample the estimated Gaussian tumor position distribution. 15 clinically‐drawn abdominal/lung CTV volumes were used to evaluate the accuracy of the proposed methods by comparing the known and calculated BB trajectories.Results:With all methods, the mean position of the BB was determined with accuracy better than 0.1 mm, and root‐mean‐square (RMS) trajectory errors were lower than 5% of marker amplitude. Use of respiratory phase information decreased RMS errors by 30%, and decreased the fraction of large errors (>3 mm) by half. Mean dose to the clinical volumes was calculated with an average error of 0.1% and average absolute error of 0.3%. Dosimetric parameters D90/D95 were determined within 0.5% of maximum dose. Monte‐Carlo sampling increased RMS trajectory and dosimetric errors slightly, but prevented over‐estimation of dose in trajectories with high noise.Conclusions:Tumor trajectory and dose‐of‐the‐day were accurately calculated using CBCT projections. This technique provides a widely‐available method to evaluate highly‐mobile tumors, and could facilitate better strategies to mitigate or compensate for motion during SBRT.
TU‐F‐17A‐07: Real‐Time Personalized Margins
Authors: Rottmann J , Keall P , Berbeco R 
Published: July 2014

Purpose:To maximize normal tissue sparing for treatments requiring motion encompassing margins. Motion mitigation techniques including DMLC or couch tracking can freeze tumor motion within the treatment aperture potentially allowing for smaller treatment margins and thus better sparing of normal tissue. To enable for a safe application of this concept in the clinic we propose adapting margins dynamically in real‐time during radiotherapy delivery based on personalized tumor localization confidence. To demonstrate technical feasibility we present a phantom study.Methods:We utilize a realistic anthropomorphic dynamic thorax phantom with a lung tumor model embedded close to the spine. The tumor, a 3D‐printout of a patient's GTV, is moved 15mm peak‐to‐peak by diaphragm compression and monitored by continuous EPID imaging in real‐time. Two treatment apertures are created for each beam, one representing ITV ‐based and the other GTV‐based margin expansion. A soft tissue localization (STiL) algorithm utilizing the continuous EPID images is employed to freeze tumor motion within the treatment aperture by means of DMLC tracking. Depending on a tracking confidence measure (TCM), the treatment aperture is adjusted between the ITV and the GTV leaf.Results:We successfully demonstrate real‐time personalized margin adjustment in a phantom study. We measured a system latency of about 250 ms which we compensated by utilizing a respiratory motion prediction algorithm (ridge regression). With prediction in place we observe tracking accuracies better than 1mm. For TCM=0 (as during startup) an ITV‐based treatment aperture is chosen, for TCM=1 a GTV‐based aperture and for 0<TCM<1 a linear interpolation between the two apertures is chosen.Conclusions:We have proposed and demonstrated a concept to safely shrink treatment margins during radiotherapy and adapt to tracking confidence in real‐time. Normal tissue sparing is maximized. The worst case scenario results in delivering a plan with standard margins used in the clinic today.
TU‐F‐18A‐01: Preliminary Results of a Prototype Quality Control Process for Spectral CT
Authors: Nute J , Jacobsen M , Pennington J , Chandler A , Imai Y , Baiu C , Cody D 
Published: July 2014

Purpose:A prototype quality control (QC) phantom and analysis process has been designed specifically to monitor dual‐energy CT and address the current lack of quantitative oversight of the spectral capabilities of these scanners.Methods:A prototype solid water phantom was designed with multiple material inserts, and to support both head and body protocols. Inserts included tissue equivalent and material rods (iodine, iron, calcium) at various concentrations. The oval body phantom, measuring 30cm×40cm×15cm, was scanned using four dual‐energy protocols with CTDIvol ranges of 19.6–62mGy (0.516 pitch) and 10.3–32.5mGy (0.984 pitch), and rotation times ranging from 0.5‐1sec. The circular head phantom, measuring 22cm in diameter by 15cm, was scanned using three dual‐energy protocols with CTDIvol ranges of 67–132.6mGy (0.531 pitch) and 36.7–72.7mGy (0.969 pitch), and rotation times ranging from 0.5–0.9sec. All images were reconstructed at 50, 70, 110 and 140 keV, and using a water‐iodine material basis pair. The images were evaluated for iodine quantification accuracy and stability of monoenergetic reconstructions. The phantom was scanned twice on ten GE 750HD CT scanners to evaluate inter‐scanner agreement, as well as ten times on a single scanner over a oneweek period to evaluate intra‐scanner repeatability.Results:Preliminary analysis revealed consistent (inter‐ and intra‐scanner) iodine quantification accuracy within 10% was only achieved for protocols in the upper half of dose levels assessed when grouped by pitch. Although all scanners undergo rigorous daily single‐energy QC, iodine quantification accuracy from one scanner unexpectedly deviated from the other nine substantially. In general, inter‐scanner agreement and intra‐scanner repeatability varied with dose, rotation time and reconstructed keV.Conclusion:Preliminary results indicate the need for a dual‐energy QC process to ensure inter‐scanner agreement and intra‐scanner repeatability. In particular, iodine quantification accuracy within 10% may not be achievable using lower dose techniques. Future plans include longer term dual‐energy CT QC data collection.In‐kind financial support was provided by GE Healthcare.
TU‐F‐18A‐02: Iterative Image‐Domain Decomposition for Dual‐Energy CT
Authors: Niu T , Dong X , Petrongolo M , Zhu L 
Published: July 2014

Purpose:Dual energy CT (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Direct decomposition via matrix inversion suffers from significant degradation of image signal‐to‐noise ratios, which reduces clinical value. Existing de‐noising algorithms achieve suboptimal performance since they suppress image noise either before or after the decomposition and do not fully explore the noise statistical properties of the decomposition process. We propose an iterative image‐domain decomposition method for noise suppression in DECT, using the full variance‐covariance matrix of the decomposed images.Methods:The proposed algorithm is formulated in the form of least‐square estimation with smoothness regularization. It includes the inverse of the estimated variance‐covariance matrix of the decomposed images as the penalty weight in the least‐square term. Performance is evaluated using an evaluation phantom (Catphan 600) and an anthropomorphic head phantom. Results are compared to those generated using direct matrix inversion with no noise suppression, a de‐noising method applied on the decomposed images, and an existing algorithm with similar formulation but with an edge‐preserving regularization term.Results:On the Catphan phantom, our method retains the same spatial resolution as the CT images before decomposition while reducing the noise standard deviation of decomposed images by over 98%. The other methods either degrade spatial resolution or achieve less low‐contrast detectability. Also, our method yields lower electron density measurement error than direct matrix inversion and reduces error variation by over 97%. On the head phantom, it reduces the noise standard deviation of decomposed images by over 97% without blurring the sinus structures.Conclusion:We propose an iterative image‐domain decomposition method for DECT. The method combines noise suppression and material decomposition into an iterative process and achieves both goals simultaneously. The proposed algorithm shows superior performance on noise suppression with high image spatial resolution and low‐contrast detectability.This work is supported by a Varian MRA grant.
WE‐D‐18A‐07: Development and Evaluation of Automated Noise Measurement Technique in CT Images
Authors: Chun M , Kim J 
Published: July 2014

Purpose:To develop and evaluate an automated technique which measures the noise level in CT images.Methods:Candidate regions of interests (ROIs) with circular shape were selected with a criteria where the pixel intensities on Gaussian filtered image lie within a predefined range. The ROIs were further refined with the sum of absolute gradients within each candidate ROI being used as a ROI feature to distinguish homogeneous ROIs. A histogram of ROI feature was created, and the ROIs with feature values less than the mode of histogram were selected as homogeneous ROIs. Connected component analysis was then used to remove the non‐clustered ROIs to restrict the location of ROI in desired organ. Standard deviation of pixel intensities was calculated for each cluster of homogeneous ROIs to represent the local noise level, and its mode value was used to represent the noise level. Fifty abdomen CT images were collected, and five observers participated to manually measure the noise level with a guidance to draw same size ROIs. Manual results were then averaged to establish a reference value, and a correlation coefficient(r) between a reference value and our automatic result. Intra‐ and inter‐rater agreement were compared to demonstrate the performance of our proposed technique.Results:We confirmed successful operation of automatic homogeneous region localization and noise level measurement. Intra‐ and inter‐rater agreement(r) ranged from 0.70 to 0.95, and from 0.79 to 0.94, respectively. Agreements between reference and manual measurements by raters ranged from 0.85 to 0.95, and those between a reference and an automated result by our proposed method was 0.90.Conclusion:Our developed technique has successfully performed the noise measurement task in all the test images by showing an agreement of 0.90 with the reference measurement. Our proposed method enables the automated assessment of CT image noise.
WE‐D‐9A‐02: Automated Landmark‐Guided CT to Cone‐Beam CT Deformable Image Registration
Authors: Kearney V , Gu X , Chen S , Jiang L , Liu H , Chiu T , Yordy J , Nedzi L , Mao W 
Published: July 2014

Purpose:The anatomical changes that occur between the simulation CT and daily cone‐beam CT (CBCT) are investigated using an automated landmark‐guided deformable image registration (LDIR) algorithm with simultaneous intensity correction. LDIR was designed to be accurate in the presence of tissue intensity mismatch and heavy noise contamination.Method:An auto‐landmark generation algorithm was used in conjunction with a local small volume (LSV) gradient matching search engine to map corresponding landmarks between the CBCT and planning CT. The LSVs offsets were used to perform an initial deformation, generate landmarks, and correct local intensity mismatch. The landmarks act as stabilizing controlpoints in the Demons objective function. The accuracy of the LDIR algorithm was evaluated on one synthetic case with ground truth and data of ten head and neck cancer patients. The deformation vector field (DVF) accuracy was accessed using a synthetic case. The Root mean square error of the 3D canny edge (RMSECE), mutual information (MI), and feature similarity index metric (FSIM) were used to access the accuracy of LDIR on the patient data. The quality of the corresponding deformed contours was verified by an attending physician.Results:The resulting 90 percentile DVF error for the synthetic case was within 5.63mm for the original demons algorithm, 2.84mm for intensity correction alone, 2.45mm using controlpoints without intensity correction, and 1.48 mm for the LDIR algorithm. For the five patients the mean RMSECE of the original CT, Demons deformed CT, intensity corrected Demons CT, control‐point stabilized deformed CT, and LDIR CT was 0.24, 0.26, 0.20, 0.20, and 0.16 respectively.Conclusion:LDIR is accurate in the presence of multimodal intensity mismatch and CBCT noise contamination. Since LDIR is GPU based it can be implemented with minimal additional strain on clinical resources.This project has been supported by a CPRIT individual investigator award RP11032.