Contrast-enhanced 4DCT Research Articles

Abstract 4122134: Relationships between aortic valve area planimetry on four-dimensional cardiac computed tomography and survival following transcatheter aortic valve replacement

Background: Aortic valve area planimetry (AVA CT ) is feasible on four-dimensional computed tomography (4D-CT) for grading aortic stenosis severity, with superior spatial but inferior temporal resolution to echocardiography, however its prognostic utility is not well-established. We evaluated the prognostic value of AVA CT in patients with significant aortic valve stenosis undergoing transcatheter aortic valve replacement (TAVR). Methods: We studied 1035 native aortic valve stenosis patients (age 79.0±9.0 years, 429 (41.5%) women) undergoing TAVR during 2019-2020. AVA CT and indexed to body surface area (AVAi CT ) were retrospectively measured on contrast-enhanced 4D-CT at peak systole, and standard statistical analyses performed to evaluate its associations with all-cause mortality during follow-up. Results: Mean AVA CT and AVAi CT were 1.02±0.21 cm 2 and 0.53±0.12 cm 2 /m 2 respectively, with 240 (23.2%) deaths during mean follow-up of 2.1±0.9 years. Areas under curves and optimal thresholds for identifying severe aortic stenosis (defined as AVA<1.0 cm 2 on echocardiography) were 0.78 and <1.10 cm 2 for AVA CT and 0.83 and <0.65 cm 2 /m 2 for AVAi CT . AVA CT and AVAi CT below these thresholds representing severe aortic stenosis were independently associated all-cause mortality during follow-up, with hazards ratios (95% confidence intervals) of 1.84 (1.34-2.53) and 2.38 (1.48-3.81) respectively in multivariable analyses. The Figure illustrates Kaplan-Meier survival curves for the cohort based on A) AVA CT and B) AVAi CT thresholds. Conclusion: Optimal thresholds of AVA CT (<1.10 cm 2 ) and AVAi CT (<0.65 cm 2 /m 2 ) were independently associated with worse survival in patients undergoing TAVR, indicating that AVA CT and AVAi CT as potentially useful additional imaging biomarkers in the diagnostic and prognostic evaluation of aortic stenosis patients.

Is an all-phase ITV (internal target volume) a gold standard in the target definition of hepatocellular carcinoma?

Stereotactic ablative body radiation (SABR) is a well-recognized treatment option for hepatocellular carcinoma (HCC). Due to the inherent motion of liver tumors, effective motion management is crucial for successful SABR. In the motion-encompassing motion management technique, all 10 respiratory phase image datasets are delineated and designated as the internal target volume (ITV). Some treatment centers use single or combination image sets to delineate the target volume. This study determines which specialty image set most closely matches an all-phase ITV contour on a synchronized contrast-enhanced 4DCT. Synchronized 4DCT contrast and delayed scans were acquired for 10 patients in the study. The maximum intensity projection (MiP), average intensity projection (AvgIP), and minimum intensity projection (MinIP) images were generated. The ITV delineation was done in all 10 phases (ITV_all_phase). The ITV_2phase combines the peak inhale and exhale phase, ITV_2M combines MiP and MinIP, and ITV_3M combines MiP, MinIP, and AvgIP. All ITVs were compared to ITV_all_phase with Dice similarity index (DSI) and volumes. Using ITV_all_phase as the reference, the DSI and the mean ITV volumes for the different ITVs were as follows: ITV_all_phase (1 and 116.69 cc), ITV_2phase (0.87 and 105.27 cc), MiP (0.76 and 98.24 cc), AvgIP (0.72 and 94.54 cc), ITV_MinIP (0.67 and 81.08 cc), ITV_2M (0.84 and 106.26 cc), and ITV_3M (0.86 and 112.51 cc). The study demonstrates that in the motion-encompassing technique of motion management, the target volume generated by delineating all phases of 4DCT provides the most accurate representation for patients with HCC. Specialty image sets and their combinations, while sometimes close, tend to result in less accurate targeting. Hence, the all-phase 4DCT method should be preferred to avoid geographical misses and ensure optimal treatment outcomes. However, our conclusion may be limited by the technique we employed.

861: Personalized contrast-enhanced 4D-CT imaging for target definition in pancreatic and liver SBRT

861: Personalized contrast-enhanced 4D-CT imaging for target definition in pancreatic and liver SBRT

Combined evaluation of blood flow and tissue perfusion in diabetic feet by intra-arterial dynamic 4DCT imaging

Critical limb ischemia is associated with high mortality and major amputations. Intra-arterial digital subtraction angiography (IADSA) has been the reference standard but has some shortcomings including the two-dimensional projection and the lack of tissue perfusion information. The aim of this exploratory study is to examine four-dimensional computed tomography (4DCT) angiography and perfusion imaging using low-volume intra-arterial contrast injections for an improved anatomic and hemodynamic assessment in patients with foot ulcers. Three patients underwent a low-volume (2 mL) intra-arterial contrast-enhanced 4DCT examination combined with a diagnostic IADSA. An automated assessment of blood flow and tissue perfusion from the 4DCT data was performed. Vascular structures and corresponding blood flows were successfully assessed and correlated well with the IADSA results. Perfusion values of the affected tissue were significantly higher compared to the unaffected tissue. The proposed 4DCT protocol combined with the minimal usage of contrast agent (2 mL) provides superior images compared to IADSA as three phases (arterial, perfusion, and venous) are captured. The obtained parameters could allow for an improved diagnosis of critical limb ischemia as both the proximal vasculature and the extent of the perfusion deficit in the microvasculature can be assessed.Relevance statementIntra-arterial 4DCT allows for assessing three phases (arterial, perfusion and venous) using minimal contrast (2 mL). This method could lead to an improved diagnosis of critical limb ischemia as both proximal vasculature and the extent of the perfusion deficit are assessed.Trial registrationISRCTN, ISRCTN95737449. Registered 14 March 2023—retrospectively registered, https://www.isrctn.com/ISRCTN95737449Key points• Three phases (arterial, perfusion, and venous) are obtained from 2 mL intra-arterial 4DCT.• The obtained hemodynamic parameters correlated well with the IADSA findings.• 4DCT surpassed IADSA in terms of assessment of venous blood flow and inflammatory hyperperfusion.• The assessment of tissue perfusion could lead to optimizing the revascularization strategy.Graphical

Deep Learning-Driven Real-Time Liver Tumor Localization via Optical Surface Imaging and Biomechanical Modeling

<h3>Purpose/Objective(s)</h3> To track liver tumors in real time via a deep learning-based framework, by exploring patient-specific correlations between patients' body surface maps and internal anatomical motion. <h3>Materials/Methods</h3> Real-time liver tumor localization was achieved via deformable registration-based motion estimation, in a two-step framework: (a) liver boundary motion estimation via deep features learnt from optical body surface imaging; and (b) intra-liver motion propagation via deep learning-based biomechanical modeling from liver boundary motion. In step (a), a patient-specific, fully-connected convolutional neural network (SurfCNN) predicts the motion of nodes residing on the boundary of a patient-specific reference liver mesh, by extracting saliency features from curvature entropy maps derived out of real-time optical body surface images. Subsequently, step (b) uses the liver boundary motion solved in (a) to infer intra-liver tumor motion, using a U-Net-style model (UNet-Bio) inspired by biomechanical modeling via finite element analysis (FEA). The cascaded framework was evaluated using a dataset of 8 liver cancer patients from our institute. Each patient had a 10-phase, contrast-enhanced 4D-CT set with liver and liver tumor contoured. To generate sufficient motion variations to train and test the patient-specific SurfCNN model, we augmented the dataset of each patient by simulating varying real-time motion patterns and magnitudes. The augmentation was achieved via a principal component analysis-based statistical model, expanding the nine inter-phase deformation-vector-fields (DVFs) of each 4D-CT into 1,728 different motion states including both rigid and deformable motion. Optical surface imaging was simulated from each motion state via extracted body surface maps, while corresponding liver boundary motion was derived via the augmented DVFs to supervise the training of SurfCNN. UNet-Bio, on the other hand, could be trained as a population-based model using supervisions from intra-liver DVFs solved by FEA-based biomechanical modeling. The liver tumor localization accuracy was assessed through Dice similarity coefficient (DSC), Hausdorff distance (HD), and center-of-mass-error (COME), by comparing the augmentation DVF-propagated ‘ground-truth' liver tumor volumes against ones deformed by the cascaded framework. <h3>Results</h3> Tested using 576 unseen scenarios for each patient case, the cascaded SurfCNN and UNet-Bio scheme can localize liver tumors to 0.791(mean) ± 0.141 (s.d.) in DSC, 3.6 ± 1.9 mm in HD, and 2.3 ± 1.7 mm in COME. In comparison, the prior reference image without deformable registration yielded 0.534 ± 0.277 in DSC, 8.3 ± 6.1 mm in HD and 6.8 ± 5.8 mm in COME. The whole model inference time was less than 100 milliseconds, satisfying the temporal constraints of real-time imaging. <h3>Conclusion</h3> The deep learning-based framework allows accurate 3D liver tumor tracking in real-time via non-ionizing, marker-less, and high frame-rate optical surface imaging.

Optimization of a\xa0protocol for contrast-enhanced four-dimensional computed tomography imaging of thoracic tumors using minimal contrast agent

PurposeThe accuracy of target delineation for node-positive thoracic tumors is dependent on both four-dimensional computed tomography (4D-CT) and contrast-enhanced three-dimensional (3D)-CT images; these scans enable the motion visualization of tumors and delineate the nodal areas. Combining the two techniques would be more effective; however, currently, there is no standard protocol for the contrast media injection parameters for contrast-enhanced 4D-CT (CE-4D-CT) scans because of its long scan durations and complexity. Thus, we aimed to perform quantitative and qualitative assessments of the image quality of single contrast-enhanced 4D-CT scans to simplify this process and improve the accuracy of target delineation in order to replace the standard clinical modality involved in administering radiotherapy for thoracic tumors.MethodsNinety consecutive patients with thoracic tumors were randomly and parallelly assigned to one of nine subgroups subjected to CE-4D-CT scans with the administration of contrast agent volume equal to the patient’s weight but different flow rate and scan delay time (protocol A1: flow rate of 2.0 ml/s, delay time of 15 s; A2: 2.0 ml/s, 20 s; A3: 2.0 ml/s, 25 s; B1: 2.5 ml/s, 15 s; B2: 2.5 ml/s, 20 s; B3: 2.5 ml/s, 25 s; C1: 3.0 ml/s, 15 s; C2: 3.0 ml/s, 20 s; C3: 3.0 ml/s, 25 s). The Hounsfield unit (HU) values of the thoracic aorta, pulmonary artery stem, pulmonary veins, carotid artery, and jugular vein were acquired for each protocol. Both quantitative and qualitative image analysis and delineation acceptability were assessed.ResultsThe results revealed significant differences among the nine protocols. Enhancement of the vascular structures in mediastinal and perihilar regions was more effective with protocol A1 or A2; however, when interested in the region of superior mediastinum and supraclavicular fossa, protocol C2 or C3 is recommended.ConclusionQualitatively acceptable enhancement on contrast-enhanced 4D-CT images of thoracic tumors can be obtained by varying the flow rate and delay time when minimal contrast agent is used.

Automatic Cone Beam Projection-based Liver Tumor Localization by Deep Learning and Biomechanical Modeling

Accurate on-board liver tumor localization remains a challenge for CBCT-based image-guided radiotherapy, due to tumors’ low-visibility within normal liver tissues. Deformable registration-driven contour propagation from planning CTs may facilitate automatic, accurate tumor localization. We developed a technique combining a population-based liver surface deformation model and a patient-specific, biomechanical modeling-guided algorithm to achieve accurate deformation-driven liver tumor contour propagation and localization. The technique solves the deformation-vector-fields (DVFs) for tumor propagation by three steps: 1. Solving a coarse DVF via 2D-3D deformable registration by intensity-matching DRRs of the deformed planning CT to on-board cone-beam projections; 2. Feeding the DVF into a population-based deep learning model to improve its accuracy around liver surface region; and 3. Applying the accuracy-enhanced liver surface DVF to a patient-specific liver biomechanical model as the boundary condition, to solve intra-liver DVF through finite element analysis. The optimized intra-liver DVF is then applied to the liver tumor contour to propagate it onto the CBCT frame for automatic localization. For step 2, the deep learning model uses a U-net architecture to perform voxel-wise correction to the 2D-3D DVF solved by step 1, especially at the caudal liver surface region where limited tissue contrast diminishes the accuracy of 2D-3D registration. The population-based model was trained on a total of 21 patients through supervised learning, using patient-specific 2D-3D DVF and liver contour pairs as input, and high-accuracy liver surface DVF as ‘gold-standard’ output. The ‘gold-standard’ DVF was obtained by manually contouring, density-overriding, and deform-registering liver organs on corresponding CT-CBCT pairs. We evaluated the technique on 10 liver patients not included in the training list. Each patient had a contrast-enhanced 4D-CT set, of which the 0% phase was used as the planning CT, and phases 10-90% as 4D-CBCTs to simulate 4D cone-beam projections for tumor localization evaluation. Liver tumors were contoured by a radiation oncologist on all 4D-CT phases for propagation (phase 0%) or reference (phases 10-90%). Both DICE similarity index and center-of-mass-error (COME) were calculated between propagated liver tumor contours and physician’s reference contours for quantitative analysis. Using 20 cone-beam projections for each localization, the 2D-3D registration technique, biomechanical modeling technique (without deep learning), and our technique localized the liver tumors to average ± s.d. COMEs of 4.5 ± 1.4 mm, 2.5 ± 0.8 mm, and 1.6 ± 0.5 mm, and DICEs of 0.61 ± 0.23, 0.73 ± 0.16, and 0.80 ± 0.09, respectively. The developed technique enables automatic, accurate liver tumor localization (<2 mm) using few projections (< = 20), which facilitates 4D tumor localization after projection phase sorting.

Improving the slice interaction of 2.5D CNN for automatic pancreas segmentation.

Volumetric pancreas segmentation can be used in the diagnosis of pancreatic diseases, the research about diabetes and surgical planning. Since manual delineation is time-consuming and laborious, we develop a deep learning-based framework for automatic pancreas segmentation in three dimensional (3D) medical images. A two-stage framework is designed for automatic pancreas delineation. In the localization stage, a Square Root Dice loss is developed to handle the trade-off between sensitivity and specificity. In refinement stage, a novel 2.5D slice interaction network with slice correlation module is proposed to capture the non-local cross-slice information at multiple feature levels. Also a self-supervised learning-based pre-training method, slice shuffle, is designed to encourage the inter-slice communication. To further improve the accuracy and robustness, ensemble learning and a recurrent refinement process are adopted in the segmentation flow. The segmentation technique is validated in a public dataset (NIH Pancreas-CT) with 82 abdominal contrast-enhanced 3D CT scans. Fourfold cross-validation is performed to assess the capability and robustness of our method. The dice similarity coefficient, sensitivity, and specificity of our results are 86.21±4.37%, 87.49±6.38% and 85.11±6.49% respectively, which is the state-of-the-art performance in this dataset. We proposed an automatic pancreas segmentation framework and validate in an open dataset. It is found that 2.5D network benefits from multi-level slice interaction and suitable self-supervised learning method for pre-training can boost the performance of neural network. This technique could provide new image findings for the routine diagnosis of pancreatic disease.

A comparative study of target volumes based on 18F-FDG PET-CT and ten phases of 4DCT for primary thoracic squamous esophageal cancer.

PurposeTo investigate the correlations in target volumes based on 18F-FDG PET/CT and four-dimensional CT (4DCT) to detect the feasibility of implementing PET in determining gross target volumes (GTV) for tumor motion for primary thoracic esophageal cancer (EC).MethodsThirty-three patients with EC sequentially underwent contrast-enhanced 3DCT, 4DCT, and 18F-FDG PET-CT thoracic simulation. The internal gross target volume (IGTV)10 was obtained by combining the GTV from ten phases of 4DCT. The GTVs based on PET/CT images were defined by setting of different standardized uptake value thresholds and visual contouring. The difference in volume ratio, conformity index (CI), and degree of inclusion (DI) between IGTV10 and GTVPET was compared.ResultsThe images from 20 patients were suitable for further analysis. The optimal volume ratio of 0.95±0.32, 1.06±0.50, 1.07±0.49 was at standardized uptake value (SUV)2.5, SUV20%, or manual contouring. The mean CIs were from 0.33 to 0.54. The best CIs were at SUV2.0 (0.51±0.11), SUV2.5 (0.53±0.13), SUV20% (0.53±0.12), and manual contouring (0.54±0.14). The mean DIs of GTVPET in IGTV10 were from 0.60 to 0.90, and the mean DIs of IGTV10 in GTVPET ranged from 0.35 to 0.78. A negative correlation was found between the mean CI and different SUV (P=0.000).ConclusionNone of the PET-based contours had both close spatial and volumetric approximation to the 4DCT IGTV10. Further evaluation and optimization of PET as a tool for target identification are required.

Variation in target volume and position in concurrent chemoradiotherapy for pancreatic cancer

Objective To investigate the variation in target volume and position in radiotherapy for pancreatic cancer based on contrast-enhanced three-dimensional computed tomography (3DCT) images, and to investigate the influence of radiotherapy re-planning during the treatment on doses to organs at risk. Methods Thirty-one patients with pancreatic cancer received contrast-enhanced 3DCT scans before radiotherapy and at the 15th-18th fraction. gross tumor volume (GTV) was delineated on the two sets of images. The coordinates and volume of GTV were acquired after automatic registration in the treatment planning system. Radiotherapy plan-1 and plan-2 were made based on the initial target volume (CT-1) and the target volume after 15th-18th fraction (CT-2), respectively. Plan-3 was defined by copying plan-1 to the CT-2. The results were analyzed using one-way analysis of variance and Pearson correlation analysis. Results During the treatment, the center of GTV had a significant larger displacement in the superior-inferior direction than in the right-left or anterior-posterior direction ((0.7±0.2) vs. (0.4±0.1) cm, (0.7±0.2) vs. (0.4±0.1) cm, P=0.048); the volume of GTV was significantly reduced by (27.1±17.1)%(P=0.000). The degrees of inclusion (DI) of the two GTVs were 0.6±0.2 and 0.8±0.2(P=0.000). The matching index (MI) was 0.5±0.1.The three-dimensional vector of GTV was negatively correlated with DI and MI (P=0.000, 0.000, 0.000). Compared with plan-3, the Dmean values for the liver and right kidney in plan-2 were significantly reduced by 21.9% and 14.4%, respectively (P=0.025, 0.040). Conclusions The position and volume of GTV have significant changes during conventional fractionation radiotherapy combined with concurrent chemotherapy for pancreatic cancer. Re-planning during the treatment can significantly reduce doses to the liver and right kidney. Therefore, it is necessary to perform target correction and re-planning at an appropriate time during treatment. Key words: Pancreatic cancer/radiotherapy; Tomography, X ray computed, three dimentional; Concurrent radiochemotherapy; Target position; Target volume

A comparative study of three-dimensional, four-dimensional, and cone beam contrast-enhanced computed tomography in measurement of the normal thickness of the esophageal wall

Objective To compare the normal thickness of the esophageal wall measured by contrast-enhanced three-dimensional (3DCT), four-dimensional (4DCT), and cone beam computed tomography (CBCT), and to provide a basis for target volume delineation in esophageal cancer. Methods From 2009 to 2016, thoracic contrast-enhanced 3DCT and 4DCT simulations were performed in 50 patients with lung cancer or metastatic lung cancer. Contrast-enhanced CBCT scans were acquired during the first three-dimensional conformal radiotherapy. The normal esophageal wall was contoured on 3DCT images, the end-exhalation phase of 4DCT images (4DCT50), the maximum intensity projection of 4DCT images (4DCTMIP), and CBCT images. The wall thickness was measured on each segment and the average thickness of esophageal wall was obtained. Comparison of the thickness of a fixed segment of esophageal wall between different CT images was made by paired t test. Comparison of thickness on the same type of CT images between different segments of esophageal wall was made by one-way analysis of variance. Results For the thoracic and intra-abdominal segments, there was no significant difference in the thickness of esophageal wall between 3DCT and 4DCT50 images (P=0.056-0.550); however, the thickness of esophageal wall was significantly smaller on 3DCT images than on 4DCTMIP or CBCT images (P=0.000-0.004). For the upper and middle thoracic segments, the thickness of esophageal wall was significantly larger on CBCT images than on 4DCTMIP images (P=0.008, P=0.001). On 3DCT, 4DCT50, and 4DCTMIP images, the thickness of esophageal wall was significantly larger in the lower thoracic segment than in the upper or middle thoracic segments (P=0.008~0.041); the intra-abdominal segment had a significantly larger thickness of esophageal wall than the thoracic segments (all P=0.000). There was no significant difference in wall thickness on CBCT images between three thoracic segments (P=0.088~0.945). Conclusions A uniform criterion can be adopted to judge the normal thickness of esophageal wall in gross tumor volume (GTV) delineation on 3DCT and 4DCT50 images for thoracic esophageal cancer. However, caution should be taken when 5 mm is used as a criterion for normal thickness of esophageal wall in GTV delineation on 4DCTMIP and CBCT images. Key words: Tomography, X-ray computed, three-dimensional; Tomography, X-ray computed, four-dimensional; Tomography, X-ray computed, cone beam; Normal oesophagus; Wall thickness

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

To develop an individually optimized contrast-enhanced (CE) 4D-computed tomography (CT) for radiotherapy simulation in pancreatic ductal adenocarcinomas (PDA). 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. 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. 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.

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&lt;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&lt;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.

Effect of contrast enhancement in delineating GTV and constructing IGTV of thoracic oesophageal cancer based on 4D-CT scans

ObjectiveTo investigate the effect of contrast enhancement on delineating the gross tumour volumes (GTVs) of different respiratory phases and constructing the corresponding internal GTVs (IGTVs) of primary thoracic oesophageal cancer based on four-dimensional computed tomography (4D-CT) scans. MethodsForty-five patients with upper (14 cases), middle (16 cases), or lower (15 cases) thoracic oesophageal cancer sequentially underwent conventional plain and contrast-enhanced 4D-CT scans during free breathing. First, the GTVs were delineated on plain 4D-CT, and the corresponding IGTVs were constructed by a physician. Then the GTVs were delineated on contrast-enhanced 4D-CT images, and the corresponding IGTVs were constructed by the same physician using the same standards. ResultsThe coefficient of variation for the target volume delineated on contrast-enhanced 4D-CT images was constantly smaller than that for plain 4D-CT images. The length of the GTVs along the z axis, as well as the volumes of the GTVs that were delineated and the IGTVs that were constructed, did not change between contrast-enhanced and plain 4D-CT images in patients with upper or lower thoracic oesophageal cancer (P>0.05), but showed significant differences in patients with middle thoracic oesophageal cancer (P<0.05). ConclusionsContrast-enhanced 4D-CT scans can reduce the error of target volume delineation and be used to construct a more accurate internal target volume in patients with middle thoracic oesophageal cancer, however, whether GTV delineation or IGTV construction for patients with upper or lower thoracic oesophageal cancer, no significant benefit was found from contrast-enhanced 4D-CT scan.

Feasibility and potential benefits of defining the internal gross tumor volume of hepatocellular carcinoma using contrast-enhanced 4D CT images obtained by deformable registration.

ObjectiveTo study the feasibility and the potential benefits of defining the internal gross tumor volume (IGTV) of hepatocellular carcinoma (HCC) using contrast-enhanced 4D CT images obtained by combining arterial-phase (AP) contrast-enhanced (CE) 3D CT and non-contrast-enhanced (NCE) 4D CT images using deformable registration (DR).MethodsTen HCC patients who had received radiotherapy beforehand were selected for this study. The following CT simulation images were acquired sequentially: NCE 4D CT in free breathing, NCE 3D CT and APCE 3D CT in end-expiration breath holding. All 4D CT images were sorted into ten phases according to breath cycle (CT00 ~ CT90). Gross tumor volumes (GTVs) were contoured on all CT images and the IGTV-1 was obtained by merging the GTVs in each phase of 4D CT images. The GTV on the APCE 3D CT image was deformably registered to each 4D CT phase image according to liver shape using RayStationTM 3.99.0.7 version treatment planning system. The IGTV-DR was obtained by merging the GTVs after DR on the 4D CT images. Volume differences among the GTVs and between the IGTV-1 and the IGTV-DR were compared.ResultsThe edge of most lesions could be definitively identified using APCE 3D CT images compared to NCE 4D and 3D CT images. The GTV volume on APCE 3D CT images increased by an average of 34.79% (P < 0.05). There was no significant difference among the GTV volumes obtained using NCE 4D and 3D CT images (P > 0.05). The GTV volumes after DR on 4D CT different phase images increased by an average of 36.29% (P < 0.05), as was observed using the APCE 3D CT image (P > 0.05). Lastly, the volume of IGTV-DR increased by an average of 19.91% compared to that of IGTV-1 (P < 0.05).ConclusionNCE 4D CT imaging alone has the potential risk of missing a partial volume of the HCC. The combination of APCE 3D CT and NCE 4D CT images using the DR technique improved the accuracy of the definition of the IGTV in HCC.

To Study the Feasibility and Potential Benefit of Defining the Internal Gross Tumor Volume (ITV) for Hepatocellular Carcinoma (HCC) Applying the Contrast-Enhanced 4D-CT Obtained by Deformable Registration Technology

To Study the Feasibility and Potential Benefit of Defining the Internal Gross Tumor Volume (ITV) for Hepatocellular Carcinoma (HCC) Applying the Contrast-Enhanced 4D-CT Obtained by Deformable Registration Technology

4DCT MIP图像与FDG PET-CT不同SUV阈值勾画NSCLC IGTV的比较研究

Objective To compare the positional and volumetric differences between internal gross target volumes (IGTV) based on the maximum intensity projection (MIP) images of four-dimensional computed tomography (4DCT) and different standardized uptake value (SUV) thresholds of fluorodeoxyglucose (FDG) positron emission tomography/CT (PET/CT) for the primary tumor of non-small cell lung cancer (NSCLC).Methods Ten patients with NSCLC sequentially underwent contrast-enhanced 3DCT and 4DCT,as well as FDG PET-CT (with the same body position and positional parameters).IGTVMIP of the primary tumor was delineated on the MIP images of 4DCT.IGTVPET2.0,IGTVPET25,IGTVPET3.0,IGTVPET3.5,IGTVPET20％,IGTVPET25％,IGTVPET30％,IGTVPET35％,IGTVPET40％ and IGTVPETman were automatically and manually delineated based on different SUV thresholds of FDG PET/CT and different percentages of SUVmax.The differences in position,volume,matching index (MI),and degree of inclusion (DI) between IGTVPET and IGTVMIP were evaluated by paired t-test.Results There were significant differences in center coordinate between IGTVPET and IGTVMIP only in z axes (P =0.014-0.044).In terms of volume,IGTVPET2.0 and IGTVPET20％ were most similar to IGTNMIP,with volume ratios of 1.02 and 1.06(P =0.806).The highest MI was seen between IGTVMIP and IGTVPET2.0 and between IGTVMIP and IGTVPET20％ (0.46 and 0.45,P =0.603).The DI of IGTVPET20％ or IGTVPET2.0 in IGTVMIP was the highest (0.61 or 0.61,P =0.963).Conclusions IGTVPET20 and IGTVPET20％ are most similar to IGTVMIP in terms of volume and matching index,but neither of them could replace IGTVMIP in spatial position.When the IGTV of the primary tumor of NSCLC is delineated based on PET-CT,target position correction should be done with reference to 4DCT images while selecting the suitable SUV threshold. Key words: Tomography, positron-emission, fluorodeoxyglucose ; Tomography, X-ray computed,four-dimensional; Maximum intensity projection; Internal gross target volume; Lung neoplasms/radiotherapy

EP-1700: Sequential contrast-enhanced 4DCT and 3DCT for radiotherapy planning in lower-third oesophageal cancer

EP-1700: Sequential contrast-enhanced 4DCT and 3DCT for radiotherapy planning in lower-third oesophageal cancer

Intensity based image registration by minimizing exponential function weighted residual complexity

In this paper, we propose a novel intensity-based similarity measure for medical image registration. Traditional intensity-based methods are sensitive to intensity distortions, contrast agent and noise. Although residual complexity can solve this problem in certain situations, relative modification of the parameter can generate dramatically different results. By introducing a specifically designed exponential weighting function to the residual term in residual complexity, the proposed similarity measure performed well due to automatically weighting the residual image between the reference image and the warped floating image. We utilized local variance of the reference image to model the exponential weighting function. The proposed technique was applied to brain magnetic resonance images, dynamic contrast enhanced magnetic resonance images (DCE-MRI) of breasts and contrast enhanced 3D CT liver images. The experimental results clearly indicated that the proposed approach has achieved more accurate and robust performance than mutual information, residual complexity and Jensen–Tsallis.

Value of 3-Dimensional CT Virtual Anatomy Imaging in Complex Foreign Body Retrieval from Soft Tissues

ObjectiveTo investigate the value of 3-dimensional (3D) CT virtual anatomy imaging (VAI) in the complex foreign body (FB) retrieval of the soft tissues.Materials and MethodsFour hundred and seventy-five patients with radiopaque FB(s) diagnosed by radiograph underwent contrast-enhanced 3D CT examination. VAI was reconstructed by volume-rendering opacity software, by sliding down the lowest threshold from -600 to 100 HU. The imaging was grouped into three groups: A (axial and multi-planar reformation [MPR] images), B (standard 3D imaging with axial and MPR images), and C (VAI with axial and MPR images). They were analyzed to reveal the type, size, number, location, complications, and the interventional removability of the object, with the comparisons in the management and clinical outcomes on the patient follow-up studies. The data were subjected to chi-square tests, with p value < 0.05 indicating significant statistical difference.ResultsThe FB shape, size, number, site distribution and vessels around FB, as well as the FB-associated vascular complications and the FB interventional removability were assessed more accurately in Group C than in Group B or Group A (p < 0.005). There was no significant difference in disclosing the type and depth of the FB among the three groups (p > 0.75). On the basis of the 3D CT, especially the enhanced 3D CT VAI, the followings were processed: the recommendation of interventional removal in 286 (60.47%) and non-intervention in 187 (39.53%) of the 473 patients with soft-tissue FB(s); in 352 (56.50%) of the 623 radiopaque FBs patients, 258 (54.55%) patients accurately detected on 3D CT and the successful removal by intervention (343 FBs) or surgery (9 FBs) without any sequela; and 215 (45.45%) patients with 271 FBs lost in the follow-up, with their departure from the hospital.ConclusionThe 3D CT, especially 3D enhanced CT VAI, has great incremental value in further diagnosis and management of complex FB extraction from soft tissues.