Percent Volume Difference Research Articles

Reducing scan angle using adaptive prior knowledge for a limited-angle intrafraction verification (LIVE) system for conformal arc radiotherapy

The purpose of this study is to develop an adaptive prior knowledge guided image estimation technique to reduce the scan angle needed in the limited-angle intrafraction verification (LIVE) system for 4D-CBCT reconstruction.The LIVE system has been previously developed to reconstruct 4D volumetric images on-the-fly during arc treatment for intrafraction target verification and dose calculation. In this study, we developed an adaptive constrained free-form deformation reconstruction technique in LIVE to further reduce the scanning angle needed to reconstruct the 4D-CBCT images for faster intrafraction verification. This technique uses free form deformation with energy minimization to deform prior images to estimate 4D-CBCT based on kV-MV projections acquired in extremely limited angle (orthogonal 3°) during the treatment. Note that the prior images are adaptively updated using the latest CBCT images reconstructed by LIVE during treatment to utilize the continuity of the respiratory motion.The 4D digital extended-cardiac-torso (XCAT) phantom and a CIRS 008A dynamic thoracic phantom were used to evaluate the effectiveness of this technique. The reconstruction accuracy of the technique was evaluated by calculating both the center-of-mass-shift (COMS) and 3D volume-percentage-difference (VPD) of the tumor in reconstructed images and the true on-board images. The performance of the technique was also assessed with varied breathing signals against scanning angle, lesion size, lesion location, projection sampling interval, and scanning direction.In the XCAT study, using orthogonal-view of 3° kV and portal MV projections, this technique achieved an average tumor COMS/VPD of 0.4 ± 0.1 mm/5.5 ± 2.2%, 0.6 ± 0.3 mm/7.2 ± 2.8%, 0.5 ± 0.2 mm/7.1 ± 2.6%, 0.6 ± 0.2 mm/8.3 ± 2.4%, for baseline drift, amplitude variation, phase shift, and patient breathing signal variation, respectively. In the CIRS phantom study, this technique achieved an average tumor COMS/VPD of 0.7 ± 0.1 mm/7.5 ± 1.3% for a 3 cm lesion and 0.6 ± 0.2 mm/11.4 ± 1.5% for a 2 cm lesion in the baseline drift case. The average tumor COMS/VPD were 0.5 ± 0.2 mm/10.8 ± 1.4%, 0.4 ± 0.3 mm/7.3 ± 2.9%, 0.4 ± 0.2 mm/7.4 ± 2.5%, 0.4 ± 0.2 mm/7.3 ± 2.8% for the four real patient breathing signals, respectively. Results demonstrated that the adaptive prior knowledge guided image estimation technique with LIVE system is robust against scanning angle, lesion size, location and scanning direction. It can estimate on-board images accurately with as little as 6 projections in orthogonal-view 3° angle.In conclusion, adaptive prior knowledge guided image reconstruction technique accurately estimates 4D-CBCT images using extremely-limited angle and projections. This technique greatly improves the efficiency and accuracy of LIVE system for ultrafast 4D intrafraction verification of lung SBRT treatments.

3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models.

Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment. This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach. To account for CT images' inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance. To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family. The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels' appearances in neighboring spatial voxel locations. Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration. Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach.

Ultrafast Volumetric Cine Magnetic Resonance Imaging (UVC-MRI) for Real-Time 3-Dimensional Target Localization/Tracking

Recently we developed a VC MRI technique to generate quasi-real-time (3 frames/s) 3D MRI based on motion modeling and 2D cine MRI for target localization. In this study, we hypothesize that the frame rate of VC MRI can be substantially accelerated using sparsely sampled 2D cine MRI. This Ultrafast VC MRI (UVC MRI) can minimize the motion artifacts for irregular breathing and provide truly real-time 3D guidance for gating/target tracking. The purpose of this study is to validate the feasibility of UVC MRI and evaluate the effects of different scanning parameters on UVC MRI. The 4D MRI acquired during patient simulation are used as prior images. Principal component analysis is used to extract 3 major respiratory deformation patterns from the deformation field maps (DFM) registered between end expiration phase and all other phases of the 4D MRI. Onboard UVC MRI at any instant is considered as a deformation of the prior MRI at the end expiration phase. The DFM for UVC MRI is represented by a linear combination of the 3 major deformation patterns. Coefficients of the deformation patterns are solved by matching the corresponding 2D slice of UVC MRI with the acquired onboard 2D cine MRI. The ultrafast onboard 2D cine MRI are acquired at 30 frames/s by sampling only 10% of the k-space on Cartesian grid, with 85% of that taken at the central k-space and the other 15% randomly sampled elsewhere. UVC MRI was evaluated using both XCAT (computerized patient model) simulation of lung cancer patients and MRI data from liver cancer patients. Three scenarios were simulated from prior to onboard volume in XCAT, including phase shift, synchronous and asynchronous motion amplitude change between tumor and body motion. The accuracy of UVC MRI was evaluated using volume percent difference (VPD) and center of mass shift (COMS) of the estimated tumor volume. Effects of region of interest (ROI) selection, 2D cine slice orientation, slice location, and slice number on the estimation accuracy were evaluated. In XCAT study, the UVC MRI estimated using a single sparsely sampled sagittal 2D cine MRI with a ROI around tumor achieved VPD/COMS of 8.54±2.27%/0.24±0.05 mm among all scenarios. Using the entire cine image instead of ROI reduced the accuracy to 37.37±43.45%/4.03±6.02 mm. Changing the 2D cine orientation to axial or coronal view reduced the accuracy to 9.26±1.30%/0.76±0.72 mm and 15.56±2.91%/1.80±0.56 mm, respectively. Estimation using the sagittal cine was the most robust against slice location change among all views. Using multiple parallel or orthogonal cine images did not further improve UVC MRI estimation as single cine was sufficient to fit the motion model. In patient study, profiles in the estimated and ground truth UVC MRI agreed within 5% error. Preliminary studies showed it is feasible to generate UVC MRI up to 30 frames/s to provide truly real-time 3D target verification. UVC MRI can potentially minimize/eliminate treatment errors in liver or lung SBRT to improve treatment outcome.

Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling.

To accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub-models with an adaptive lung shape submodel. The first-order appearance submodel accounts for both the original CT image and its Gaussian scale space (GSS) filtered version to specify local and global signal properties, respectively. Each empirical marginal probability distribution of signals is closely approximated with a linear combination of discrete Gaussians (LCDG), containing two positive dominant and multiple sign-alternate subordinate DGs. The approximation is separated into two LCDGs to describe individually the lungs and their background, i.e., all other chest tissues. The second-order appearance submodel quantifies conditional pairwise intensity dependencies in the nearest voxel 26-neighborhood in both the original and GSS-filtered images. The shape submodel is built for a set of training data and is adapted during segmentation using both the lung and chest appearances. The accuracy of the proposed segmentation framework is quantitatively assessed using two public databases (ISBI VESSEL12 challenge and MICCAI LOLA11 challenge) and our own database with, respectively, 20, 55, and 30 CT images of various lung pathologies acquired with different scanners and protocols. Quantitative assessment of our framework in terms of Dice similarity coefficients, 95-percentile bidirectional Hausdorff distances, and percentage volume differences confirms the high accuracy of our model on both our database (98.4±1.0%, 2.2±1.0 mm, 0.42±0.10%) and the VESSEL12 database (99.0±0.5%, 2.1±1.6 mm, 0.39±0.20%), respectively. Similarly, the accuracy of our approach is further verified via a blind evaluation by the organizers of the LOLA11 competition, where an average overlap of 98.0% with the expert's segmentation is yielded on all 55 subjects with our framework being ranked first among all the state-of-the-art techniques compared.

Predicting Peak Flows in Real Time through Event Based Hydrologic Modeling for a Trans-Boundary River Catchment

Investigating the hydrological response of an area to adverse climate changes and extreme rainfall events is crucial for managing land and water resources and mitigating the natural hazards like floods. Limited availability of the in situ data, especially in case of Transboundary Rivers, further highlights the need to develop and evaluate decision support systems which may predict the flows in real time using open source rainfall data. This paper presents the study conducted in Chenab River catchment, Pakistan, to develop and evaluate a hydrologic model using HEC-HMS for predicting flows based on TRMM rainfall data. The catchment was analyzed for hydro-morphological properties using SRTM DEM in HEC-GeoHMS. To rely on open source data as much as possible, digital soil map of the world developed by FAO and global land cover map developed by European Space Agency were utilized to compute Curve Number grid data for the catchment. These preliminary data analyses were employed to set initial values of different parameters to be used for model calibration. The model was calibrated for five rainfall events occurred in the rainy seasons of 2006, 2010 and 2013. The calibrated model was then validated for four other rainfall events of similar type in the same years. Consistency in simulated and observed flows was found with percent difference in volume ranging from −6.17 % to 5.47 % and percent difference in peak flows to be in the range of 6.96 % to 7.28 %. Values of Nash-Sutcliffe Efficiency were ranging from 0.299 to 0.909 with an average value of 0.586 for all flow events. The model was found well capable of capturing the hydrologic response of the catchment due to rainfall events and can be helpful in providing alerts of peak flows in real time based on real time/forecasted rainfall data.

TU‐AB‐BRA‐09: A Novel Method of Generating Ultrafast Volumetric Cine MRI (VC‐MRI) Using Prior 4D‐MRI and On‐Board Phase‐Skipped Encoding Acquisition for Radiotherapy Target Localization

Purpose:To develop a technique generating ultrafast on‐board VC‐MRI using prior 4D‐MRI and on‐board phase‐skipped encoding k‐space acquisition for real‐time 3D target tracking of liver and lung radiotherapy.Methods:The end‐of‐expiration (EOE) volume in 4D‐MRI acquired during the simulation was selected as the prior volume. 3 major respiratory deformation patterns were extracted through the principal component analysis of the deformation field maps (DFMs) generated between EOE and all other phases. The on‐board VC‐MRI at each instant was considered as a deformation of the prior volume, and the deformation was modeled as a linear combination of the extracted 3 major deformation patterns. To solve the weighting coefficients of the 3 major patterns, a 2D slice was extracted from VC‐MRI volume to match with the 2D on‐board sampling data, which was generated by 8‐fold phase skipped‐encoding k‐space acquisition (i.e., sample 1 phase‐encoding line out of every 8 lines) to achieve an ultrafast 16–24 volumes/s frame rate. The method was evaluated using XCAT digital phantom to simulate lung cancer patients. The 3D volume of end‐ofinhalation (EOI) phase at the treatment day was used as ground‐truth onboard VC‐MRI with simulated changes in 1) breathing amplitude and 2) breathing amplitude/phase change from the simulation day. A liver cancer patient case was evaluated for in‐vivo feasibility demonstration.Results:The comparison between ground truth and estimated on‐board VC‐MRI shows good agreements. In XCAT study with changed breathing amplitude, the volume‐percent‐difference(VPD) between ground‐truth and estimated tumor volumes at EOI was 6.28% and the Center‐of‐Mass‐Shift(COMS) was 0.82mm; with changed breathing amplitude and phase, the VPD was 8.50% and the COMS was 0.54mm. The study of liver patient case also demonstrated a promising in vivo feasibility of the proposed methodConclusion:Preliminary results suggest the feasibility to estimate ultrafast VC‐MRI for on‐board target localization with phase skipped‐encoding k‐space acquisition.Research grant from NIH R01‐184173

SU‐F‐J‐115: Target Volume and Artifact Evaluation of a New Device‐Less 4D CT Algorithm

Purpose:4DCT is often used in radiation therapy treatment planning to define the extent of motion of the visible tumor (IGTV). Recent available software allows 4DCT images to be created without the use of an external motion surrogate. This study aims to compare this device‐less algorithm to a standard device‐driven technique (RPM) in regards to artifacts and the creation of treatment volumes.Methods:34 lung cancer patients who had previously received a cine 4DCT scan on a GE scanner with an RPM determined respiratory signal were selected. Cine images were sorted into 10 phases based on both the RPM signal and the device‐less algorithm. Contours were created on standard and device‐less maximum intensity projection (MIP) images using a region growing algorithm and manual adjustment to remove other structures. Variations in measurements due to intra‐observer differences in contouring were assessed by repeating a subset of 6 patients 2 additional times. Artifacts in each phase image were assessed using normalized cross correlation at each bed position transition. A score between +1 (artifacts “better” in all phases for device‐less) and −1 (RPM similarly better) was assigned for each patient based on these results.Results:Device‐less IGTV contours were 2.1 ± 1.0% smaller than standard IGTV contours (not significant, p = 0.15). The Dice similarity coefficient (DSC) was 0.950 ± 0.006 indicating good similarity between the contours. Intra‐observer variation resulted in standard deviations of 1.2 percentage points in percent volume difference and 0.005 in DSC measurements. Only two patients had improved artifacts with RPM, and the average artifact score (0.40) was significantly greater than zero.Conclusion:Device‐less 4DCT can be used in place of the standard method for target definition due to no observed difference between standard and device‐less IGTVs. Phase image artifacts were significantly reduced with the device‐less method.

SU‐F‐T‐42: MRI and TRUS Image Fusion as a Mode of Generating More Accurate Prostate Contours

Purpose:Transrectal Ultrasound (TRUS) imaging is utilized intra‐operatively for LDR permanent prostate seed implant treatment planning. Prostate contouring with TRUS can be challenging at the apex and base. This study attempts to improve accuracy of prostate contouring with MRI‐TRUS fusion to prevent over‐ or under‐estimation of the prostate volume.Methods:14 patients with previous MRI guided prostate biopsy and undergone an LDR permanent prostate seed implant have been selected. The prostate was contoured on the MRI images (1 mm slice thickness) by a radiologist. The prostate was also contoured on TRUS images (5 mm slice thickness) during LDR procedure by a urologist. MRI and TRUS images were rigidly fused manually and the prostate contours from MRI and TRUS were compared using Dice similarity coefficient, percentage volume difference and length, height and width differences.Results:The prostate volume was overestimated by 8 ± 18% (range: 34% to −25%) in TRUS images compared to MRI. The mean Dice was 0.77 ± 0.09 (range: 0.53 to 0.88). The mean difference (TRUS‐MRI) in the prostate width was 0 ± 4 mm (range: −11 to 5 mm), height was −3 ± 6 mm (range: −13 to 6 mm) and length was 6 ± 6 (range: −10 to 16 mm). Prostate was overestimated with TRUS imaging at the base for 6 cases (mean: 8 ± 4 mm and range: 5 to 14 mm), at the apex for 6 cases (mean: 11 ± 3 mm and range: 5 to 15 mm) and 1 case was underestimated at both base and apex by 4 mm.Conclusion:Use of intra‐operative TRUS and MRI image fusion can help to improve the accuracy of prostate contouring by accurately accounting for prostate over‐ or under‐estimations, especially at the base and apex. The mean amount of discrepancy is within a range that is significant for LDR sources.

SU‐G‐JeP3‐04: Estimating 4D CBCT from Prior Information and Extremely Limited Angle Projections Using Structural PCA and Weighted Free‐Form Deformation

Purpose:To investigate the feasibility of using structure‐based principal component analysis (PCA) motion‐modeling and weighted free‐form deformation to estimate on‐board 4D‐CBCT using prior information and extremely limited angle projections for potential 4D target verification of lung radiotherapy.Methods:A technique for lung 4D‐CBCT reconstruction has been previously developed using a deformation field map (DFM)‐based strategy. In the previous method, each phase of the 4D‐CBCT was generated by deforming a prior CT volume. The DFM was solved by a motion‐model extracted by global PCA and a free‐form deformation (GMM‐FD) technique, using data fidelity constraint and the deformation energy minimization. In this study, a new structural‐PCA method was developed to build a structural motion‐model (SMM) by accounting for potential relative motion pattern changes between different anatomical structures from simulation to treatment. The motion model extracted from planning 4DCT was divided into two structures: tumor and body excluding tumor, and the parameters of both structures were optimized together. Weighted free‐form deformation (WFD) was employed afterwards to introduce flexibility in adjusting the weightings of different structures in the data fidelity constraint based on clinical interests. XCAT (computerized patient model) simulation with a 30 mm diameter lesion was simulated with various anatomical and respirational changes from planning 4D‐CT to onboard volume. The estimation accuracy was evaluated by the Volume‐Percent‐Difference (VPD)/Center‐of‐Mass‐Shift (COMS) between lesions in the estimated and “ground‐truth” on board 4D‐CBCT.Results:Among 6 different XCAT scenarios corresponding to respirational and anatomical changes from planning CT to on‐board using single 30° on‐board projections, the VPD/COMS for SMM‐WFD was reduced to 10.64±3.04%/1.20±0.45mm from 21.72±9.24%/1.80±0.53mm for GMM‐FD. Using 15° orthogonal projections, the VPD/COMS was further reduced to 1.91±0.86%/0.31±0.42mm based on SMM‐WFD.Conclusion:Compared to GMM‐FD technique, the SMM‐WFD technique can substantially improve the 4D‐CBCT estimation accuracy using extremely small scan angles to provide ultra‐fast 4D verification.This work was supported by the National Institutes of Health under Grant No. R01‐CA184173 and a research grant from Varian Medical Systems

TH‐EF‐BRA‐08: A Novel Technique for Estimating Volumetric Cine MRI (VC‐MRI) From Multi‐Slice Sparsely Sampled Cine Images Using Motion Modeling and Free Form Deformation

Purpose:To develop a technique to estimate on‐board VC‐MRI using multi‐slice sparsely‐sampled cine images, patient prior 4D‐MRI, motion‐modeling and free‐form deformation for real‐time 3D target verification of lung radiotherapy.Methods:A previous method has been developed to generate on‐board VC‐MRI by deforming prior MRI images based on a motion model(MM) extracted from prior 4D‐MRI and a single‐slice on‐board 2D‐cine image. In this study, free‐form deformation(FD) was introduced to correct for errors in the MM when large anatomical changes exist. Multiple‐slice sparsely‐sampled on‐board 2D‐cine images located within the target are used to improve both the estimation accuracy and temporal resolution of VC‐MRI. The on‐board 2D‐cine MRIs are acquired at 20–30frames/s by sampling only 10% of the k‐space on Cartesian grid, with 85% of that taken at the central k‐space. The method was evaluated using XCAT(computerized patient model) simulation of lung cancer patients with various anatomical and respirational changes from prior 4D‐MRI to onboard volume. The accuracy was evaluated using Volume‐Percent‐Difference(VPD) and Center‐of‐Mass‐Shift(COMS) of the estimated tumor volume. Effects of region‐of‐interest(ROI) selection, 2D‐cine slice orientation, slice number and slice location on the estimation accuracy were evaluated.Results:VCMRI estimated using 10 sparsely‐sampled sagittal 2D‐cine MRIs achieved VPD/COMS of 9.07±3.54%/0.45±0.53mm among all scenarios based on estimation with ROI_MM‐ROI_FD. The FD optimization improved estimation significantly for scenarios with anatomical changes. Using ROI‐FD achieved better estimation than global‐FD. Changing the multi‐slice orientation to axial, coronal, and axial/sagittal orthogonal reduced the accuracy of VCMRI to VPD/COMS of 19.47±15.74%/1.57±2.54mm, 20.70±9.97%/2.34±0.92mm, and 16.02±13.79%/0.60±0.82mm, respectively. Reducing the number of cines to 8 enhanced temporal resolution of VC‐MRI by 25% while maintaining the estimation accuracy. Estimation using slices sampled uniformly through the tumor achieved better accuracy than slices sampled non‐uniformly.Conclusions:Preliminary studies showed that it is feasible to generate VC‐MRI from multi‐slice sparsely‐sampled 2D‐cine images for real‐time 3D‐target verification.This work was supported by the National Institutes of Health under Grant No. R01‐CA184173 and a research grant from Varian Medical Systems.

A Technique for Generating Volumetric Cine-Magnetic Resonance Imaging

The purpose of this study was to develop a techique to generate on-board volumetric cine-magnetic resonance imaging (VC-MRI) using patient prior images, motion modeling, and on-board 2-dimensional cine MRI. One phase of a 4-dimensional MRI acquired during patient simulation is used as patient prior images. Three major respiratory deformation patterns of the patient are extracted from 4-dimensional MRI based on principal-component analysis. The on-board VC-MRI at any instant is considered as a deformation of the prior MRI. The deformation field is represented as a linear combination of the 3 major deformation patterns. The coefficients of the deformation patterns are solved by the data fidelity constraint using the acquired on-board single 2-dimensional cine MRI. The method was evaluated using both digital extended-cardiac torso (XCAT) simulation of lung cancer patients and MRI data from 4 real liver cancer patients. The accuracy of the estimated VC-MRI was quantitatively evaluated using volume-percent-difference (VPD), center-of-mass-shift (COMS), and target tracking errors. Effects of acquisition orientation, region-of-interest (ROI) selection, patient breathing pattern change, and noise on the estimation accuracy were also evaluated. Image subtraction of ground-truth with estimated on-board VC-MRI shows fewer differences than image subtraction of ground-truth with prior image. Agreement between normalized profiles in the estimated and ground-truth VC-MRI was achieved with less than 6% error for both XCAT and patient data. Among all XCAT scenarios, the VPD between ground-truth and estimated lesion volumes was, on average, 8.43±1.52% and the COMS was, on average, 0.93±0.58mm across all time steps for estimation based on the ROI region in the sagittal cine images. Matching to ROI in the sagittal view achieved better accuracy when there was substantial breathing pattern change. The technique was robust against noise levels up to SNR = 20. Forpatient data, average tracking errors were less than 2mm in all directions for all patients. Preliminary studies demonstrated the feasibility of generating real-time VC-MRI for on-board localization of moving targets in radiation therapy.

Atlas based brain volumetry: How to distinguish regional volume changes due to biological or physiological effects from inherent noise of the methodology

Fully-automated regional brain volumetry based on structural magnetic resonance imaging (MRI) plays an important role in quantitative neuroimaging. In clinical trials as well as in clinical routine multiple MRIs of individual patients at different time points need to be assessed longitudinally. Measures of inter- and intrascanner variability are crucial to understand the intrinsic variability of the method and to distinguish volume changes due to biological or physiological effects from inherent noise of the methodology.To measure regional brain volumes an atlas based volumetry (ABV) approach was deployed using a highly elastic registration framework and an anatomical atlas in a well-defined template space. We assessed inter- and intrascanner variability of the method in 51 cognitively normal subjects and 27 Alzheimer dementia (AD) patients from the Alzheimer's Disease Neuroimaging Initiative by studying volumetric results of repeated scans for 17 compartments and brain regions.Median percentage volume differences of scan–rescans from the same scanner ranged from 0.24% (whole brain parenchyma in healthy subjects) to 1.73% (occipital lobe white matter in AD), with generally higher differences in AD patients as compared to normal subjects (e.g., 1.01% vs. 0.78% for the hippocampus). Minimum percentage volume differences detectable with an error probability of 5% were in the one-digit percentage range for almost all structures investigated, with most of them being below 5%. Intrascanner variability was independent of magnetic field strength. The median interscanner variability was up to ten times higher than the intrascanner variability.

The nasal septum deviation index (NSDI) based on CBCT data

To assess whether three-dimensional morphometric parameters could be useful in nasal septal deviation (NSD) diagnosis and, secondarily, whether CBCT could be considered an adequate imaging technique for the proposed task. We analysed images of 46 subjects who underwent CBCT for reasons not related to this study. Two experienced operators divided all the images into healthy and NSD subjects. Subsequently, the images were segmented using ITK Snap in order to obtain the three-dimensional model of the nasal airways and compute four morphological parameters: septal deviation angle (SDA), percentage of volume difference between right and left side of the nasal airways, nasal airway total volume and a new synthetic septal deviation index (SDI). Principal component analysis (PCA) was used to unveil relationships between each variable and the global nasal airway variability. Differences between the groups were found in SDA (p < 0.001), in volume percentage difference (p < 0.05) and in SDI (p < 0.001). PCA showed high correlation between the SDI and the first principal component (0.97, p < 0.001). Among the analysed parameters, SDI seemed to be the most suitable for the quantitative assessment of NSD, and CBCT allowed accurate assessment of airway morphology.

Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans

BackgroundManual lesion delineation by an expert is the standard for lesion identification in MRI scans, but it is time-consuming and can introduce subjective bias. Alternative methods often require multi-modal MRI data, user interaction, scans from a control population, and/or arbitrary statistical thresholding. New methodWe present an approach for automatically identifying stroke lesions in individual T1-weighted MRI scans using naïve Bayes classification. Probabilistic tissue segmentation and image algebra were used to create feature maps encoding information about missing and abnormal tissue. Leave-one-case-out training and cross-validation was used to obtain out-of-sample predictions for each of 30 cases with left hemisphere stroke lesions. ResultsOur method correctly predicted lesion locations for 30/30 un-trained cases. Post-processing with smoothing (8mm FWHM) and cluster-extent thresholding (100 voxels) was found to improve performance. Comparison with existing methodQuantitative evaluations of post-processed out-of-sample predictions on 30 cases revealed high spatial overlap (mean Dice similarity coefficient=0.66) and volume agreement (mean percent volume difference=28.91; Pearson's r=0.97) with manual lesion delineations. ConclusionsOur automated approach agrees with manual tracing. It provides an alternative to automated methods that require multi-modal MRI data, additional control scans, or user interaction to achieve optimal performance. Our fully trained classifier has applications in neuroimaging and clinical contexts.

Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context.

To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI). SVM is proposed to segment the brainstem on MRI in multicenter brain cancer context. A dataset composed by 14 adult brain MRI scans is used to evaluate its performance. In addition to spatial and probabilistic information, five different image intensity values (IIVs) configurations are evaluated as features to train the SVM classifier. Segmentation accuracy is evaluated by computing the Dice similarity coefficient (DSC), absolute volumes difference (AVD) and percentage volume difference between automatic and manual contours. Mean DSC for all proposed IIVs configurations ranged from 0.89 to 0.90. Mean AVD values were below 1.5 cm(3), where the value for best performing IIVs configuration was 0.85 cm(3), representing an absolute mean difference of 3.99% with respect to the manual segmented volumes. Results suggest consistent volume estimation and high spatial similarity with respect to expert delineations. The proposed approach outperformed presented methods to segment the brainstem, not only in volume similarity metrics, but also in segmentation time. Preliminary results showed that the approach might be promising for adoption in clinical use.

TH-CD-303-06: Deformable Registration-Based Image Estimation Method for 4D CBCT Using Region-Based PCA

Purpose: To investigate the feasibility of using region-based principal component analysis (PCA) for motion modeling to estimate on-board 4D-CBCT using prior information and limited angle projections for potential 4D target verification of lung radiotherapy. Methods: A technique for lung 4D-CBCT reconstruction has been previously developed using a deformation field map (DFM)-based strategy. In this method, each phase of the 4D-CBCT is generated by deforming a prior CT volume. The DFM is solved by a motion modeling based on global PCA and free-form deformation (MM-FD) technique, using data fidelity constraint and the deformation energy minimization. For this study, a new region-based PCA method was developed to optimize the motion-modeling part of the MM-FD technique in order to estimate target delineation more accurately using fewer on-board projection angles. First, the DFMs are estimated using the region-based PCA divides the motion model extracted from planning 4D-CT into two regions: tumor and everything else. The motion model parameters of both regions are optimized together based on data fidelity constraint with relative weighting added to the tumor PCA model. The 4D digital extended cardiac-torso phantom was used to evaluate the region-based PCA MM-FD technique. A lung patient with a 30 mm diameter lesion was simulated with various anatomical and respirational changes from planning 4D CT to onboard volume. The reconstruction accuracy was evaluated by the Volume-Percent-Difference(VPD)/Center-of-Mass-Shift(COMS) between lesions in the estimated and “ground-truth” on board 4D CBCT. Results: For patient scenarios with various shifts of tumor from planning CT to on-board acquisition with 15 degree orthogonal projections, the average VPD/COMS for the MM-FD technique was 5.04%/0.26 mm and 23.23%/2.4mm for region-based and global PCA MM-FD, respectively. Conclusion: The region-based PCA MM-FD technique can potentially further reduces the scan angle needed for onboard 4D CBCT reconstruction for ultra-fast 4D verification. National Institutes of Health Grant No. R01-CA184173 Varian Medical System

Electronic properties of double wall BN nanotube under hydrostatic pressure: an ab initio study

Ab initio calculation was performed to study the structural transformation on a double wall boron nitride nanotubes bundle under hydrostatic pressure. The (10,0)@(17,0) zigzag DWBNNTs disposed into an hexagonal arrangement were chosen. Under compression the hexagonal arrangement as well as the circular cross section of the tubes were preserved up to a critical pressure value. At under this pressure, the tubes deform to an elliptic cross section and the bundle shape elongates. The discontinuity of percentage difference in volume clearly demonstrates the discontinuous nature of the structural transition. For pressure values above the critical, the electronic properties were observed to modify continuously. The energy gap suffers a continuous decrease up to the pressure of breakdown of the tubes.

Evaluation of Hippocampal Volume Based on Various Inversion Time in Normal Adults by Manual Tracing and Automated Segmentation Methods

Magnetic resonance imaging (MRI) enables non-invasive examinations of the Purpose: To investigate the value of image post-processing software (FreeSurfer, IBASPM [individual brain atlases using statistical parametric mapping software]) and inversion time (TI) in volumetric analyses of the hippocampus and to identify differences in comparison with manual tracing. Materials and Methods: Brain images from 12 normal adults were acquired using magnetization prepared rapid acquisition gradient echo (MPRAGE) with a slice thickness of 1.3 mm and TI of 800, 900, 1000, and 1100 ms. Hippocampal volumes were measured using FreeSurfer, IBASPM and manual tracing. Statistical differences were examined using correlation analyses accounting for spatial interpretations percent volume overlap and percent volume difference. Results: FreeSurfer revealed a maximum percent volume overlap and maximum percent volume difference at TI = 800 ms (77.1 ± 2.9%) and TI = 1100 ms (13.1 ± 2.1%), respectively. The respective values for IBASPM were TI = 1100 ms (55.3 ± 9.1%) and TI = 800 ms (43.1 ± 10.7%). FreeSurfer presented a higher correlation than IBASPM but it was not statistically significant. Conclusion: FreeSurfer performed better in volumetric determination than IBASPM. Given the subjective nature of manual tracing, automated image acquisition and analysis image is accurate and preferable.

Assessment of final product dosing accuracy when using volumetric technique in the preparation of chemotherapy.

Studies have compared gravimetric and volumetric dosing accuracies in chemotherapy agents, finding high accuracy in gravimetric measurements with a mean deviation of ± 0.06%, while volumetric measurements had a mean deviation of ± 3.02%. Chemotherapy doses prepared under a biological safety cabinet containing two weights from the precision scale between 15 December 2010 and 30 March 2011 were eligible for inclusion. Empty syringes attached to a closed-system transfer device were weighed prior to product manipulation. The product was then prepared using the syringe pull-back method (volumetric technique) and the same syringe containing drug was weighed (gravimetric method). A total of 1156 compounded sterile products were eligible for the study. The mean percent volume difference of preparations included was -0.53% with a range of -64.9% to 94.22% for individual doses. Of the prepared doses, 71.7% were within ± 5% and 87.4% were within ± 10% of the ordered dose. Secondary outcomes found to be associated with an increased percent volume difference were the pediatric population, smaller volumes prepared, drugs requiring reconstitution compared to already in solution, and final product dispensed to the patient in syringes. While the mean value of volumetric measurements is within the generally understood acceptable range for dispensing chemotherapy, the range of measurements is highly variable. Future studies are warranted to better understand the reasons behind the variation and to evaluate the impact of workflow changes on improving final product accuracy.

Lung 4D-CBCT Reconstruction Using Prior Information and Limited-Angle Projections: Phantom and Patient Studies

Purpose/Objective(s): Current application of 4D-CBCT in clinics is limited due to its long scan time and high imaging dose. This study is to develop and clinically evaluate a 4D-CBCT reconstruction method using limited-angle projections to reduce scan time and dose. Materials/Methods: A motion-modeling and free-form deformation (MMFD) method is proposed to reconstruct 4D-CBCT images using only orthogonal limited-angle projections. In this method, each phase of the onboard 4D-CBCT is viewed as a deformation of a prior CT image. To solve the deformation field, the MM-FD method applies a principal component analysis based motion-modeling to generate a coarse estimation of the deformation field, followed by a constrained free-form deformation algorithm to further fine-tune the field. Data acquired from a dynamic thorax physical phantom and three lung cancer patients were used to evaluate the MM-FD method. 4D-CBCT reconstructions from a full sample of projections were used as the ground truth. The accuracy of the MM-FD method was evaluated by calculating the volume percentage difference (VPD) and center-of-mass shift (COMS) of the reconstructed tumor volume. The accuracy of the FDK, MM-only and FD-only methods was also evaluated for comparison. Results: Results showed that MM-FD was substantially more accurate than FDK, MM-only and FD-only methods. For the phantom study using orthogonal 30 projections, the average ( standard deviation) VPD values for MM-only, FD-only and MM-FD methods were 223.3% ( 2.5%), 29.2% ( 24.9%) and 6.6% ( 2.6%), respectively. The corresponding COMS values were 2.0mm ( 1.6mm), 3.5mm ( 2.7mm) and 1.1mm ( 0.4mm), respectively. For the patient study, the average ( standard deviation) VPD values for MM-only, FD-only and MM-FD methods were 36.0% ( 32.2%), 21.4% ( 13.9%) and 9.6% ( 6.1%). The corresponding COMS values were 2.2mm ( 1.5mm), 1.6mm ( 0.6mm) and 1.1mm ( 0.5mm). The MM-FD method yielded similar reconstruction accuracy using different scan directions. The average ( standard deviation) VPD and COMS were 6.6% ( 2.6%) and 1.1mm ( 0.5mm) using orthogonal 30 projections from anterior-posterior and left-lateral directions, and 8.3% ( 2.5%) and 1.3mm ( 0.4mm) using projections from left-anterior-oblique and left-posterior-oblique directions. The MM-FD method was less accurate when using projections of lower sampling frequency. The average ( standard deviation) VPD and COMS were 6.6% ( 2.6%) and 1.1mm ( 0.4mm) using 1/projection, and 23.1% ( 5.3%) and 1.2mm ( 0.4mm) using 4/projection. Conclusions: The MM-FD method can accurately reconstruct 4D-CBCT images using only limited-angle projections and therefore shows great potential to reduce the scan time and dose for 4D-CBCT imaging. Author Disclosure: Y. Zhang: A. Employee; Duke University Medical Center. F. Yin: A. Employee; Duke University Medical Center. E. Research Grant; Varian Medical Systems, National Institute of Health. Q. Patent/License Fee/Copyright; Patent. S. Leadership; AAPM. T. Pan: A. Employee; The University of Texas, MD Anderson Cancer Center. E. Research Grant; Cancer Prevention Research Institute of Texas. I. Vergalasova: A. Employee; Duke University Medical Center. L. Ren: A. Employee; Duke University Medical Center. E. Research Grant; Varian Medical Systems, National Institute of Health.