M. Diehn

SU-D-17A-04: The Impact of Audiovisual Biofeedback On Image Quality During 4D Functional and Anatomic Imaging: Results of a Prospective Clinical Trial

PURPOSE The ability of audiovisual (AV) biofeedback to improve breathing regularity has not previously been investigated for functional imaging studies. The purpose of this study was to investigate the impact of AV biofeedback on 4D-PET and 4D-CT image quality in a prospective clinical trial. We hypothesized that motion blurring in 4D-PET images and the number of artifacts in 4D-CT images are reduced using AV biofeedback. METHODS AV biofeedback is a real-time, interactive and personalized system designed to help a patient self-regulate his/her breathing using a patient-specific representative waveform and musical guides. In an IRB-approved prospective clinical trial, 4D-PET and 4D-CT images of 10 lung cancer patients were acquired with AV biofeedback (AV) and free breathing (FB). The 4D-PET images in 6 respiratory bins were analyzed for motion blurring by: (1) decrease of GTVPET and (2) increase of SUVmax in 4-DPET compared to 3D-PET. The 4D-CT images were analyzed for artifacts by: (1) comparing normalized cross correlation-based scores (NCCS); and (2) quantifying a visual assessment score (VAS). A two-tailed paired t-test was used to test the hypotheses. RESULTS The impact of AV biofeedback on 4D-PET and 4D-CT images varied widely between patients, suggesting inconsistent patient comprehension and capability. Overall, the 4D-PET decrease of GTVPET was 2.0±3.0cm3 with AV and 2.3±3.9cm^3 for FB (p=0.61). The 4D-PET increase of SUVmax was 1.6±1.0 with AV and 1.1±0.8 with FB (p=0.002). The 4D-CT NCCS were 0.65±0.27 with AV and 0.60±0.32 for FB (p=0.32). The 4D-CT VAS was 0.0±2.7 (p=ns). CONCLUSION A 10-patient study demonstrated a statistically significant reduction of motion blurring of AV over FB for 1/2 functional 4D-PET imaging metrics. No difference between AV and FB was found for 2 anatomic 4D-CT imaging metrics. Future studies will focus on optimizing the human-computer interface and including patient training sessions for improved comprehension and capability. Supported by NIH/NCI R01 CA 093626, Stanford BioX Interdisciplinary Initiatives Program, NHMRC Australia Fellowship, and Kwanjeong Educational Foundation. GE Healthcare provided the Respiratory Gating Toolbox for 4D-PET image reconstruction. Stanford University owns US patent #7955270 which is unlicensed to any commercial entity.

TU-A-12A-02: Novel Lung Ventilation Imaging with Single Energy CT After Single Inhalation of Xenon: Comparison with SPECT Ventilation Images

PURPOSE We propose a novel lung functional imaging method to determine the spatial distribution of xenon (Xe) gas in a single inhalation as a measure of regional ventilation. We compare Xe-CT ventilation to single-photon emission CT (SPECT) ventilation, which is the current clinical reference. Regional lung ventilation information may be useful for the diagnosis and monitoring of pulmonary diseases such as COPD, radiotherapy planning, and assessing the progression of toxicity after radiation therapy. METHODS In an IRB-approved clinical study, Xe-CT and SPECT ventilation scans were acquired for three patients including one patient with severe emphysema and two lung cancer patients treated with radiotherapy. For Xe- CT, we acquired two breath-hold single energy CT images of the entire lung with inspiration of 100% O2 and a mixture of 70% Xe and 30% O2, respectively. A video biofeedback system was used to achieve reproducible breath-holds. We used deformable image registration to align the breathhold images with each other to accurately subtract them, producing a map of the distribution of Xe as a surrogate of lung ventilation. We divided each lung into twelve parts and correlated the Hounsfield unit (HU) enhancement at each part with the SPECT ventilation count of the corresponding part of the lung. RESULTS The mean of the Pearson linear correlation coefficient values between the Xe-CT and ventilation SPECT count for all three patients were 0.62 (p<0.01). The Xe-CT image had a higher resolution than SPECT, and did not show central airway deposition artifacts that were present in the SPECT image. CONCLUSION We developed a rapid, safe, clinically practical, and potentially widely accessible method for regional lung functional imaging. We demonstrated strong correlations between the Xe-CT ventilation image and SPECT ventilation image as the clinical reference. This ongoing study will investigate more patients to confirm this finding.

TH‐A‐WAB‐03: Radiation Dose Changes Pulmonary Function Measured by 4D‐CT Ventilation Imaging

PURPOSE The purpose of this study was to quantify mid-and post-treatment changes in 4D-CT ventilation and test the hypothesis: temporal changes of 4D-CT ventilation in lung regions receiving high doses are greater than those in regions receiving low or no doses. METHODS In an IRB-approved clinical trial, repeat 4D-CT scans were acquired for eight thoracic cancer patients treated with conventionally-fractionated or hypo-fractionated radiotherapy at the following time points: pre-treatment (8 patients), mid-treatment (6) and post-treatment (7). Ventilation images were created for each time point using deformable registration for spatial mapping of the peak-exhale 4D-CT image to the peak-inhale image and computation of Jacobian-based ventilation metric. To quantify the ventilation change between pre-treatment (baseline) and mid-or post-treatment, rigid registration was performed and the ventilation values were normalized by the mean value in high-functional (>50th percentile ventilation value) lung regions receiving low doses (biologically effective dose <30 Gy). Statistical significance was tested using a Wilcoxon test. RESULTS Absolute ventilation changes in the high dose ROI were found to be greater than those in the low dose ROI consistently for all patients at all time points, except for one case. Overall, the mean absolute ventilation change in the high dose ROI was 22.6±17.7%, which was significantly greater than that in the low dose ROI (5.0±3.7%) (p<0.01). Several patients showed clear correlations between 4D-CT ventilation changes and anatomic changes observed in CT images. For example, patient 3 demonstrated increased ventilation, which was correlated with central airway re-opening and tumor regression. CONCLUSION An 8-patient study demonstrated significantly greater changes of 4D-CT ventilation in lung regions receiving high doses than those in regions receiving low or no doses, providing a validation for 4D-CT ventilation imaging. Future studies will focus on detailed analysis of correlations between 4D-CT ventilation changes and anatomic changes. National Lung Cancer Partnership Young Investigator Research Grant; NIH/NCI2 R01 CA 093626.

WE-AB-202-08: Feasibility of Single-Inhalation/Single-Energy Xenon CT for High-Resolution Imaging of Regional Lung Ventilation in Humans

PURPOSE To demonstrate the efficacy of a novel functional lung imaging method that utilizes single-inhalation, single-energy xenon CT (Xe-CT) lung ventilation scans, and to compare it against the current clinical standard, ventilation single-photon emission CT (V-SPECT). METHODS In an IRB-approved clinical study, 14 patients undergoing thoracic radiotherapy received two successive single inhalation, single energy (80keV) CT images of the entire lung using 100% oxygen and a 70%/30% xenon-oxygen mixture. A subset of ten patients also received concurrent SPECT ventilation scans. Anatomic reproducibility between the two scans was achieved using a custom video biofeedback apparatus. The CT images were registered to each other by deformable registration, and a calculated difference image served as surrogate xenon ventilation map. Both lungs were partitioned into twelve sectors, and a sector-wise correlation was performed between the xenon and V-SPECT scans. A linear regression model was developed with forced expiratory volume (FEV) as a predictor and the coefficient of variation (CoV) as the outcome. RESULTS The ventilation comparison for five of the patients had either moderate to strong Pearson correlation coefficients (0.47 to 0.69, p<0.05). Of these, four also had moderate to strong Spearman correlation coefficients (0.46 to 0.80, p<0.03). The patients with the strongest correlation had clear regional ventilation deficits. The patient comparisons with the weakest correlations had more homogeneous ventilation distributions, and those patients also had diminished lung function as assessed by spirometry. Analysis of the relationship between CoV and FEV yielded a non-significant trend toward negative correlation (Pearson coefficient -0.60, p<0.15). CONCLUSION Significant correlations were found between the Xe-CT and V-SPECT ventilation imagery. The results from this small cohort of patients indicate that single inhalation, single energy Xe-CT has the potential to quantify regional lung ventilation volumetrically with high resolution using widely accessible radiologic equipment. Bill Loo and Peter Maxim are founders of TibaRay, Inc. Bill Loo is also a board member. Bill Loo and Peter Maxim have received research grants from Varian Medical Systems, Inc. and RaySearch Laboratory.

WE‐E‐17A‐02: Predictive Modeling of Outcome Following SABR for NSCLC Based On Radiomics of FDG‐PET Images

PURPOSE This study aims to develop predictive models of patient outcome by extracting advanced imaging features (i.e., Radiomics) from FDG-PET images. METHODS We acquired pre-treatment PET scans for 51 stage I NSCLC patients treated with SABR. We calculated 139 quantitative features from each patient PET image, including 5 morphological features, 8 statistical features, 27 texture features, and 100 features from the intensity-volume histogram. Based on the imaging features, we aim to distinguish between 2 risk groups of patients: those with regional failure or distant metastasis versus those without. We investigated 3 pattern classification algorithms: linear discriminant analysis (LDA), naive Bayes (NB), and logistic regression (LR). To avoid the curse of dimensionality, we performed feature selection by first removing redundant features and then applying sequential forward selection using the wrapper approach. To evaluate the predictive performance, we performed 10-fold cross validation with 1000 random splits of the data and calculated the area under the ROC curve (AUC). RESULTS Feature selection identified 2 texture features (homogeneity and/or wavelet decompositions) for NB and LR, while for LDA SUVmax and one texture feature (correlation) were identified. All 3 classifiers achieved statistically significant improvements over conventional PET imaging metrics such as tumor volume (AUC = 0.668) and SUVmax (AUC = 0.737). Overall, NB achieved the best predictive performance (AUC = 0.806). This also compares favorably with MTV using the best threshold at an SUV of 11.6 (AUC = 0.746). At a sensitivity of 80%, NB achieved 69% specificity, while SUVmax and tumor volume only had 36% and 47% specificity. CONCLUSION Through a systematic analysis of advanced PET imaging features, we are able to build models with improved predictive value over conventional imaging metrics. If validated in a large independent cohort, the proposed techniques could potentially aid in identifying patients who might benefit from adjuvant therapy.

TU‐G‐141‐08: Impact of Audiovisual Biofeedback Respiratory Training On 4D‐PET Image Quality

Purpose: Current 4D‐CT methods show artifacts of >4 mm in 90% of cases. The purpose of this study was to quantify the impact of audiovisual (AV) biofeedback respiratory training on 4D‐CT image quality. Methods: In an IRB‐approved clinical trial, two 4D‐CT scans, with free breathing and AV biofeedback, were acquired with a cine method for ten thoracic cancer patients. AV biofeedback was based on: (1) a patient‐specific representative waveform and a dot moving according to the abdominal displacement, displayed on the video screen of the goggles; and (2) musical guides provided with a beat period corresponding to the waveform period. The phase‐sorted 4D‐CT images with free breathing and AV biofeedback at the 0% (peak‐inhale), 30%, 50% (peak‐exhale) and 80% phases were analyzed for artifacts by: (1) comparing normalized cross correlation (NCC)‐based scores (artifacts give high scores) of the two images; and (2) quantifying a score for each pair based on visual assessment of the two image sets (positive scores mean that AV biofeedback 4D‐CT is of higher quality). We tested the hypothesis that AV biofeedback significantly reduces 4D‐CT artifacts using the two‐tailed paired t‐test. Results: The impact of AV biofeedback varied widely with patients and phases, suggesting inconsistent patient compliance. For example, patient 5 demonstrated a visual assessment score of 4 at the 0% phase indicating that AV biofeedback 4D‐CT was of higher quality than free breathing, while a score of −3 at the 50% phase indicating the opposite. Overall, no statistically significant differences were found in both the NCC‐based score (free 0.60±0.32 vs. AV 0.65±0.27, p=0.32) and visual assessment score (0.0±2.7, p=0.95). Conclusion: A 10‐patient study demonstrated no statistically significant impact of AV biofeedback respiratory training on 4D‐CT image quality. Future studies will investigate strategies to improve patient compliance by optimizing the biofeedback interface or increasing patient comfort. NIH/NCI 2 R01 CA 093626; Stanford BioX Interdisciplinary Initiatives Program

WE‐C‐103‐08: Automated Tool for Determining Pulmonary Nodule Elasticity to Distinguish Malignant Nodules

PURPOSE To develop and validate an automated method of determining pulmonary nodule (PN) elasticity against a manual contouring method, and preliminarily assess its ability to distinguish malignant tissue by comparing the elasticities of malignant PNs treated with stereotactic ablative radiotherapy (SABR) with those of the lung. METHODS We analyzed breath-hold images of 30 patients with malignant PNs who underwent SABR in our department. A parametric nonrigid transformation model based on multilevel B-spline guided by Sum of Squared Differences similarity metric was applied on breath-hold images to determine the deformation map. The Jacobian of the calculated deformation map, which is directly related to the volume changes between the two respiratory phases, was calculated. Next, elasticity parameter will be derived by calculating the ratio of the Jacobian of the PN to the Jacobian of a 1cm region of lung tissue surrounding the tumor (E-ROI) as well as the Jacobian of the whole lung (E-Lung). RESULTS For the first group of 15 patients we evaluated the volumetric changes of PNs and the lung from the maximum exhale phase to the maximum inhale phase, whereas the reverse was done for the second group of 15 patients. For the first group, mean and standard deviation for E-ROI and E-Lung were 0.91±0.09 and 0.86±0.18, respectively, which was verified by the manual method. For the second group, E-ROI and E-Lung were 1.34±0.27 and 1.57±0.51, respectively. These results demonstrate that the elasticity of the PNs was less than that of the surrounding lung (p<0.0037). CONCLUSION We developed an automated tool to determine the elasticity of PNs based on deformable image registration of breath-hold images. The tool was validated against manual contouring. Preliminarily, PN elastometry distinguishes proven malignant PNs from normal tissue of lung, suggesting its potential utility as a non-invasive diagnostic tool to differentiate malignant from benign PN. This Study is suuported by DoD LCRP 2011, Grant# W81XWH-12-1-0286.

SU‐C‐110‐02: Reducing 4D CT Artifacts Using Optimized Sorting Based on Anatomic Similarity

Purpose: Four‐dimensional (4D) computed tomography(CT) has been widely used as a tool to characterize respiratory motion in radiotherapy. The two most commonly used 4DCT algorithms sort images by respiratory phase or displacement into a predefined number of bins, and are prone to image artifacts at transitions between bed positions. The purpose of this work is to demonstrate a method of reducing motion artifacts in 4DCT by incorporating anatomic similarity into phase or displacement based sorting protocols. Methods: Ten patient datasets were retrospectively sorted using both displacement and phase based sorting algorithms. Conventional sorting methods allow selection of only the nearest neighbor image in time or displacement within each bin. In our method, for each bed position either the displacement or the phase defines the center of a bin range about which several candidate images are selected. The two dimensional correlation coefficients between slices bordering the interface between adjacent couch positions are then calculated for all candidate pairings. Two slices have a high correlation if they are anatomically similar. Candidates from each bin are then selected to maximize the slice correlation over the entire data set using Dijkstras shortest path algorithm. To assess the reduction of artifacts, two thoracic radiationoncologists independently compared the re‐sorted 4D datasets pair‐wise with conventionally sorted datasets, blinded to the sorting method, to choose which had the least motion artifacts. Agreement between reviewers was evaluated using the weighted kappa score. Results: Anatomically based image selection resulted in 4DCT datasets with significantly reduced motion artifacts with both displacement (P=0.0063) and phase sorting (P=0.00022). The reviewers agreed 34 times and disagreed 6 times. Conclusions: Sorting using anatomic similarity significantly reduces 4DCT motion artifacts compared to conventional phase or displacement based sorting. This improved sorting algorithm is a straightforward extension of the two most common 4DCT sorting algorithms.