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Individually optimized contrast-enhanced 4D-CT for radiotherapy simulation in pancreatic ductal adenocarcinoma.

PURPOSE To develop an individually optimized contrast-enhanced (CE) 4D-computed tomography (CT) for radiotherapy simulation in pancreatic ductal adenocarcinomas (PDA). METHODS Ten PDA patients were enrolled. Each underwent three CT scans: a 4D-CT immediately following a CE 3D-CT and an individually optimized CE 4D-CT using test injection. Three physicians contoured the tumor and pancreatic tissues. Image quality scores, tumor volume, motion, tumor-to-pancreas contrast, and contrast-to-noise ratio (CNR) were compared in the three CTs. Interobserver variations were also evaluated in contouring the tumor using simultaneous truth and performance level estimation. RESULTS Average image quality scores for CE 3D-CT and CE 4D-CT were comparable (4.0 and 3.8, respectively; P = 0.082), and both were significantly better than that for 4D-CT (2.6, P < 0.001). Tumor-to-pancreas contrast results were comparable in CE 3D-CT and CE 4D-CT (15.5 and 16.7 Hounsfield units (HU), respectively; P = 0.21), and the latter was significantly higher than in 4D-CT (9.2 HU, P = 0.001). Image noise in CE 3D-CT (12.5 HU) was significantly lower than in CE 4D-CT (22.1 HU, P = 0.013) and 4D-CT (19.4 HU, P = 0.009). CNRs were comparable in CE 3D-CT and CE 4D-CT (1.4 and 0.8, respectively; P = 0.42), and both were significantly better in 4D-CT (0.6, P = 0.008 and 0.014). Mean tumor volumes were significantly smaller in CE 3D-CT (29.8 cm3, P = 0.03) and CE 4D-CT (22.8 cm3, P = 0.01) than in 4D-CT (42.0 cm3). Mean tumor motion was comparable in 4D-CT and CE 4D-CT (7.2 and 6.2 mm, P = 0.17). Interobserver variations were comparable in CE 3D-CT and CE 4D-CT (Jaccard index 66.0% and 61.9%, respectively) and were worse for 4D-CT (55.6%) than CE 3D-CT. CONCLUSIONS CE 4D-CT demonstrated characteristics comparable to CE 3D-CT, with high potential for simultaneously delineating the tumor and quantifying tumor motion with a single scan.

Individually optimized uniform contrast enhancement in CT angiography for the diagnosis of pulmonary thromboembolic disease—A simulation study

PURPOSE To improve the diagnostic quality of CT pulmonary angiography (CTPA) by individually optimizing a biphasic contrast injection function to achieve targeted uniform contrast enhancement. To compare the results against a previously reported discrete Fourier transform (DFT) approach. METHODS This simulation study used the CTPA datasets of 27 consecutive patients with pulmonary thromboembolic disease (PE). An optimization approach was developed consisting of (1) computation of the impulse enhancement function (IEF) based on a test bolus scan, and (2) optimization of a biphasic contrast injection function using the IEF in order to achieve targeted uniform enhancement. The injection rates and durations of a biphasic contrast injection function are optimized by minimizing the difference between the resulting contrast enhancement curve and the targeted uniform enhancement curve, while conforming to the clinical constraints of injection rate and total contrast volume. The total contrast volume was limited first to the clinical standard of 65 ml, and then to the same amount used in the DFT approach for comparison. The optimization approach and the DFT approach were compared in terms of the root mean square error (RMSE) and total contrast volume used. RESULTS When the total contrast volume was limited to 65 ml, the optimization approach produced significantly better contrast enhancement (closer to the targeted uniform contrast enhancement) than the DFT approach (RMSE 17 HU vs 56 HU, p < 0.00001). On average, the optimization approach used 63 ml contrast, while the DFT approach used 50 ml with four patients exceeding 65 ml. When equivalent total contrast volume was used for individual patient, the optimization approach still generated significantly better contrast enhancement (RMSE 44 HU vs 56 HU, p < 0.01). Constraints for the injection function could be easily accommodated into the optimization process when searching for the optimal biphasic injection function. CONCLUSIONS The optimization approach generated individually optimized biphasic injection functions yielding significantly better contrast enhancement compared to the DFT approach. This new approach has the potential to improve the diagnostic quality of CTPA for PE.

TH-EF-BRA-04: Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in Pancreatic Ductal Adenocarcinoma

PURPOSE To develop an individually optimized contrast-enhanced (CE) 4D-CT for radiotherapy simulation in pancreatic ductal adenocarcinomas (PDA). METHODS Ten PDA patients were enrolled. Each underwent 3 CT scans: a 4D-CT immediately following a CE 3D-CT and an individually optimized CE 4D-CT using test injection. Three physicians contoured the tumor and pancreatic tissues. We compared image quality scores, tumor volume, motion, tumor-to-pancreas contrast, and contrast-to-noise ratio (CNR) in the 3 CTs. We also evaluated interobserver variations in contouring the tumor using simultaneous truth and performance level estimation (STAPLE). RESULTS Average image quality scores for CE 3DCT and CE 4D-CT were comparable (4.0 and 3.8, respectively; P=0.47), and both were significantly better than that for 4D-CT (2.6, P<0.001). Tumor-to-pancreas contrast results were comparable in CE 3D-CT and CE 4D-CT (15.5 and 16.7 HU, respectively; P=0.71), and the latter was significantly higher than in 4D-CT (9.2 HU, P=0.03). Image noise in CE 3D-CT (12.5 HU) was significantly lower than in CE 4D-CT (22.1 HU, P<0.001) and 4D-CT (19.4 HU, P=0.005). CNRs were comparable in CE 3D-CT and CE 4DCT (1.4 and 0.8, respectively; P=0.23), and the former was significantly better than in 4D-CT (0.6, P = 0.04). Mean tumor volumes were smaller in CE 3D-CT (29.8 cm3 ) and CE 4D-CT (22.8 cm3 ) than in 4D-CT (42.0 cm3 ), although these differences were not statistically significant. Mean tumor motion was comparable in 4D-CT and CE 4D-CT (7.2 and 6.2 mm, P=0.23). Interobserver variations were comparable in CE 3D-CT and CE 4D-CT (Jaccard index 66.0% and 61.9%, respectively) and were worse for 4D-CT (55.6%) than CE 3D-CT. CONCLUSION CE 4D-CT demonstrated characteristics comparable to CE 3D-CT, with high potential for simultaneously delineating the tumor and quantifying tumor motion with a single scan. Supported in part by Philips Healthcare.

SU-E-J-187: Individually Optimized Contrast-Enhancement 4D-CT for Pancreatic Adenocarcinoma in Radiotherapy Simulation

PURPOSE To study the feasibility of individually optimized contrastenhancement (CE) 4D-CT for pancreatic adenocarcinoma (PDA) in radiotherapy simulation. To evaluate the image quality and contrast enhancement of tumor in the CE 4D-CT, compared to the clinical standard of CE 3D-CT and 4D-CT. METHODS In this IRB-approved study, each of the 7 PDA patients enrolled underwent 3 CT scans: a free-breathing 3D-CT with contrast (CE 3D-CT) followed by a 4D-CT without contrast (4D-CT) in the first study session, and a 4D-CT with individually synchronized contrast injection (CE 4D-CT) in the second study session. In CE 4D-CT, the time of full contrast injection was determined based on the time of peak enhancement for the test injection, injection rate, table speed, and longitudinal location and span of the pancreatic region. Physicians contoured both the tumor (T) and the normal pancreatic parenchyma (P) on the three CTs (end-of-exhalation for 4D-CT). The contrast between the tumor and normal pancreatic tissue was computed as the difference of the mean enhancement level of three 1 cm3 regions of interests in T and P, respectively. Wilcoxon rank sum test was used to statistically compare the scores and contrasts. RESULTS In qualitative evaluations, both CE 3D-CT and CE 4D-CT scored significantly better than 4D-CT (4.0 and 3.6 vs. 2.6). There was no significant difference between CE 3D-CT and CE 4D-CT. In quantitative evaluations, the contrasts between the tumor and the normal pancreatic parenchyma were 0.6±23.4, -2.1±8.0, and -19.6±28.8 HU, in CE 3D-CT, 4D-CT, and CE 4D-CT, respectively. Although not statistically significant, CE 4D-CT achieved better contrast enhancement between the tumor and the normal pancreatic parenchyma than both CE 3D-CT and 4DCT. CONCLUSION CE 4D-CT achieved equivalent image quality and better contrast enhancement between tumor and normal pancreatic parenchyma than the clinical standard of CE 3D-CT and 4D-CT. This study was supported in part by Philips Healthcare.

SU‐E‐J‐77: Inter‐Fractional Variations in Internal Tumor Volumes Using Multiple 4DCTs and CBCTs

PURPOSE To quantitatively evaluate the inter-fractional variations in ITVs over the course of treatment using multiple 4DCTs and CBCTs for patients treated with SBRT for lung targets. To compare using ITV generated from all phases of a 4D or average intensity projection (ITV-AIP) as reference contours for image guidance. METHODS An IRB approved study included five patients who received SBRT for a lung target using daily image guidance with a CBCT. Three 4DCTs were acquired before (simulation), during and at the end of treatment. ITV-4Ds (on all 10 phases) and ITV-CBCTs were contoured manually. All CT images and contours were registered to the reference image - AIP1 from 4D1. The registration consists of intensity-based image registration followed by manual soft-tissue alignment, where physicians manually make shift to ensure that the tumor seen on CBCT is encompassed in ITV-4D1 or ITV-AIP1. The relationship between two ITVs was quantified by centroid distance, volume inclusion, and volume overlap (Dice Coefficient). RESULTS The average volume for ITV-4D, ITV-AIP, and ITV-CBCT were 23.1, 17.2, and 14.1 cm3, respectively. When using ITV-4D1 as a reference contour in the manual soft-tissue alignment, an average of 93% of ITV-CBCTs was included in ITV-4D1, with a centroid distance of 2.0 mm. Only 81% of ITV-4Ds were included in ITV-4D1. The Dice Coefficient between ITV-4D1 and ITV-4Ds was only 0.68, suggesting that the two ITVs were not highly overlapping. For all comparisons, the results were similar using either ITV-AIP1 or ITV-4D1 as a reference contour in the manual soft-tissue alignment. CONCLUSIONS There were noteworthy inter-fractional variations in ITV-4Ds. Compared to ITV-4D, ITV-AIP was much similar to ITV-CBCT. However, there was no significant difference between using ITV-4D1 and using ITV-AIP1 as a reference contour for image guidance. Further dosimetric studies are required to evaluate actual tumor coverage. This study was supported in part by Philips Healthcare.

SU-C-9A-01: Parameter Optimization in Adaptive Region-Growing for Tumor Segmentation in PET

PURPOSE To design a reliable method to determine the optimal parameter in the adaptive region-growing (ARG) algorithm for tumor segmentation in PET. METHODS The ARG uses an adaptive similarity criterion m - fσ ≤ I_PET ≤ m + fσ, so that a neighboring voxel is appended to the region based on its similarity to the current region. When increasing the relaxing factor f (f ≥ 0), the resulting volumes monotonically increased with a sharp increase when the region just grew into the background. The optimal f that separates the tumor from the background is defined as the first point with the local maximum curvature on an Error function fitted to the f-volume curve. The ARG was tested on a tumor segmentation Benchmark that includes ten lung cancer patients with 3D pathologic tumor volume as ground truth. For comparison, the widely used 42% and 50% SUVmax thresholding, Otsu optimal thresholding, Active Contours (AC), Geodesic Active Contours (GAC), and Graph Cuts (GC) methods were tested. The dice similarity index (DSI), volume error (VE), and maximum axis length error (MALE) were calculated to evaluate the segmentation accuracy. RESULTS The ARG provided the highest accuracy among all tested methods. Specifically, the ARG has an average DSI, VE, and MALE of 0.71, 0.29, and 0.16, respectively, better than the absolute 42% thresholding (DSI=0.67, VE= 0.57, and MALE=0.23), the relative 42% thresholding (DSI=0.62, VE= 0.41, and MALE=0.23), the absolute 50% thresholding (DSI=0.62, VE=0.48, and MALE=0.21), the relative 50% thresholding (DSI=0.48, VE=0.54, and MALE=0.26), OTSU (DSI=0.44, VE=0.63, and MALE=0.30), AC (DSI=0.46, VE= 0.85, and MALE=0.47), GAC (DSI=0.40, VE= 0.85, and MALE=0.46) and GC (DSI=0.66, VE= 0.54, and MALE=0.21) methods. CONCLUSIONS The results suggest that the proposed method reliably identified the optimal relaxing factor in ARG for tumor segmentation in PET. This work was supported in part by National Cancer Institute Grant R01 CA172638; The dataset is provided by AAPM TG211.

Pretreatment SBRT Imaging Correlates Equally Well With Multiphase 4DCT and Averaging of 4DCT Simulation

locations. Simulations of combined setup errors showed an increased cord dose sensitivity to rotational errors when combined with translational error. Conclusions: This study is the most detailed analysis of the effect of rotational setup errors in isolation, and when combined with translational setup errors, on the spinal cord dose distribution and mapped cord surface dose. The effects were more significant with greater distance between the isocenter and the 90% isodose on the PRV surface. A linear model was designed to predict dosimetric impact of rotational setup error which can be used to prospectively identify treatment plans that are more likely to be sensitive to even minor rotational setup errors. Author Disclosure: A. Fotouhi Ghiam: None. H. Keller: None. M. Sharpe: None. B. Millar: None. P. Chung: None. D. Jaffray: None. A. Sahgal: None. D. Letourneau: None.

SU‐E‐J‐10: Inter‐Fractional Tumor Motion Analysis Using 4D‐CT and CBCT

PURPOSE To quantitatively evaluate the inter-fractional variation in tumor volumes with repeated 4D-CTs and repeated CBCTs for lung patients. To evaluate the uncertainties in patient set-up that uses internal target volume (ITV) of 4D-CT to match the soft tissue on CBCTs. METHODS We retrospectively selected 5 lung cancer patients: each with three 4D-CTs (4D1, 4D2, and 4D3) and three CBCTs (C1, C2, and C3), and each CBCT was scanned within one week of a corresponding 4D-CT. All CT images are registered to 4D1, together with contours on each image: ITV for 4D-CT and gross tumor volumes (GTV) for CBCT. Then, these volumes are compared to ITV in 4D1 in terms of tumor volume, centroid distance, and volume overlap coefficient. RESULTS In each CBCT/4D-CT pair, GTV in CBCT underestimate the ITV in 4D-CT by 41.22±1.39 %. When normalized to the ITV volume in the 4D1, other ITVs of 4D-CTs have an average volume of 1.07±0.13, and GTV in CBCTs have an average volume of 0.58±0.01. The centroid distance between ITV of 4D1 and a GTV of CBCT (5.6±11.9 mm) is larger than that between ITVs of 4D1and 4D2/4D3 (4.6±8.1 mm), while the CBCT GTV volumes are more included in the ITV of 4D1 (BinA: 0.863±0.018) than those of 4D2/4D3 (BinA: 0.735±0.033). From visual observation, the tumors presented in CBCTs are more similar to those in average projections compared to the ITVs of 4D-CTs. CONCLUSIONS The soft tissue alignment using 4D ITV on CBCT image has room for improvement. Although CBCT tumor seems more included, the centroid distance between CBCT-GTV and 4D1-ITV is larger than that between 4D2/4D3-ITV and 4D1-ITV. This, together with the underestimation of tumor volume from CBCT, makes current soft tissue alignment not as reliable as it seems. This work is supported in part by Philips Healthcare, Inc.

TU‐C‐213CD‐05: 4D‐CT Simulation Using Individually Optimized Contrast Enhancement (CE): A Phantom Study

Purpose: Contrast‐enhanced (CE) 4D‐CT has the advantage of both visualizing an abdominal tumor and estimating its motion in a single scan. Considering the large variation in the peak CE time from one patient to another, we propose to optimize the contrast injection delay time for individual patients so that the CE is maximized when scanning the tumor region in CE 4D‐CT. Methods: We designed a flow phantom simulating the contrast enhancement and washing out in an organ. Peak CE time of a test injection (Tpeak.test) with 10 ml contrast was used to estimate the peak CE time of a full injection (Tpeak) with 140 ml contrast in the CE 4D‐CT: Tpeak = Tpeak.test + (TID‐TID.test), where TID.test and TID are the corresponding injection durations. We then computed an optimal contrast injection delay time, TIV = TO ‐ Tpeak, where TO is the 4D‐CT scan time from the starting position to the center of phantom. To verify that maximal CE is achieved with TIV, we performed five CE 4D‐CT scans using an injection delay time of TIV + AT, where ΔT = +6, +3, 0, −3, and −6 s. The CE in a volume of interest at the center of the phantom was measured in each 4D‐CT. Results: The phantom showed good reproducibility about ±1 s in the peak CE time of both test injection and full injection. The 4D‐CT using the optimal TIV (ΔT = 0) showed the maximum contrast enhancement. The larger deviation from TIV, the lower was contrast enhancement. These results verified that the estimated optimal injection time delay achieved maximum contrast enhancement. Conclusions: It is feasible to estimate individually optimized injection delay time to maximally enhance a region or organ of interest in CE 4D‐CT. This study is supported in part by Philips Healthcare. This study is supported in part by Philips Healthcare.