Dataset Information

4D liver tumor localization using cone-beam projections and a biomechanical model.

ABSTRACT: PURPOSE:To improve the accuracy of liver tumor localization, this study tests a biomechanical modeling-guided liver cone-beam CT (CBCT) estimation (Bio-CBCT-est) technique, which generates new CBCTs by deforming a prior high-quality CT or CBCT image using deformation vector fields (DVFs). The DVFs can be used to propagate tumor contours from the prior image to new CBCTs for automatic 4D tumor localization. METHODS/MATERIALS:To solve the DVFs, the Bio-CBCT-est technique employs an iterative scheme that alternates between intensity-driven 2D-3D deformation and biomechanical modeling-guided DVF regularization and optimization. The 2D-3D deformation step solves DVFs by matching digitally reconstructed radiographs of the 3D deformed prior image to 2D phase-sorted on-board projections according to imaging intensities. This step's accuracy is limited at low-contrast intra-liver regions without sufficient intensity variations. To boost the DVF accuracy in these regions, we use the intensity-driven DVFs solved at higher-contrast liver boundaries to fine-tune the intra-liver DVFs by finite element analysis-based biomechanical modeling. We evaluated Bio-CBCT-est's accuracy with seven liver cancer patient cases. For each patient, we simulated 4D cone-beam projections from 4D-CT images, and used these projections for Bio-CBCT-est based image estimations. After Bio-CBCT-est, the DVF-propagated liver tumor/cyst contours were quantitatively compared with the manual contours on the original 4D-CT 'reference' images, using the DICE similarity index, the center-of-mass-error (COME), the Hausdorff distance (HD) and the voxel-wise cross-correlation (CC) metrics. In addition to simulation, we also performed a preliminary study to qualitatively evaluate the Bio-CBCT-est technique via clinically acquired cone beam projections. A quantitative study using an in-house deformable liver phantom was also performed. RESULTS:Using 20 projections for image estimation, the average (±s.d.) DICE index increased from 0.48?±?0.13 (by 2D-3D deformation) to 0.77?±?0.08 (by Bio-CBCT-est), the average COME decreased from 7.7?±?1.5?mm to 2.2?±?1.2?mm, the average HD decreased from 10.6?±?2.2?mm to 5.9?±?2.0?mm, and the average CC increased from -0.004?±?0.216 to 0.422?±?0.206. The tumor/cyst trajectory solved by Bio-CBCT-est matched well with that manually obtained from 4D-CT reference images. CONCLUSIONS:Bio-CBCT-est substantially improves the accuracy of 4D liver tumor localization via cone-beam projections and a biomechanical model.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC6445758 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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4D liver tumor localization using cone-beam projections and a biomechanical model.

Zhang You Y Folkert Michael R MR Li Bin B Huang Xiaokun X Meyer Jeffrey J JJ Chiu Tsuicheng T Lee Pam P Tehrani Joubin Nasehi JN Cai Jing J Parsons David D Jia Xun X Wang Jing J

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 20181114

<h4>Purpose</h4>To improve the accuracy of liver tumor localization, this study tests a biomechanical modeling-guided liver cone-beam CT (CBCT) estimation (Bio-CBCT-est) technique, which generates new CBCTs by deforming a prior high-quality CT or CBCT image using deformation vector fields (DVFs). The DVFs can be used to propagate tumor contours from the prior image to new CBCTs for automatic 4D tumor localization.<h4>Methods/materials</h4>To solve the DVFs, the Bio-CBCT-est technique employs an ...[more]

PMID: 30448003

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Project description:PURPOSE:Currently, four-dimensional (4D) cone-beam computed tomography (CBCT) requires a 3-4 min full-fan scan to ensure usable image quality. Recent advancements in sparse-view 4D-CBCT reconstruction have opened the possibility to reduce scan time and dose. The aim of this study is to provide a common framework for systematically evaluating algorithms for 4D-CBCT reconstruction from a 1-min scan. Using this framework, the AAPM-sponsored SPARE Challenge was conducted in 2018 to identify and compare state-of-the-art algorithms. METHODS:A clinically realistic CBCT dataset was simulated using patient CT volumes from the 4D-Lung database. The selected patients had multiple 4D-CT sessions, where the first 4D-CT was used as the prior CT, and the rest were used as the ground truth volumes for simulating CBCT projections. A GPU-based Monte Carlo tool was used to simulate the primary, scatter, and quantum noise signals. A total of 32 CBCT scans of nine patients were generated. Additional qualitative analysis was performed on a clinical Varian and clinical Elekta dataset to validate the simulation study. Participants were blinded from the ground truth, and were given 3 months to apply their reconstruction algorithms to the projection data. The submitted reconstructions were analyzed in terms of root-mean-squared-error (RMSE) and structural similarity index (SSIM) with the ground truth within four different region-of-interests (ROI) - patient body, lungs, planning target volume (PTV), and bony anatomy. Geometric accuracy was quantified as the alignment error of the PTV. RESULTS:Twenty teams participated in the challenge, with five teams completing the challenge. Techniques involved in the five methods included iterative optimization, motion-compensation, and deformation of the prior 4D-CT. All five methods rendered significant reduction in noise and streaking artifacts when compared to the conventional Feldkamp-Davis-Kress (FDK) algorithm. The RMS of the three-dimensional (3D) target registration error of the five methods ranged from 1.79 to 3.00 mm. Qualitative observations from the Varian and Elekta datasets mostly concur with those from the simulation dataset. Each of the methods was found to have its own strengths and weaknesses. Overall, the MA-ROOSTER method, which utilizes a 4D-CT motion model for temporal regularization, had the best and most consistent image quality and accuracy. CONCLUSION:The SPARE Challenge represents the first framework for systematically evaluating state-of-the-art algorithms for 4D-CBCT reconstruction from a 1-min scan. Results suggest the potential for reducing scan time and dose for 4D-CBCT. The challenge dataset and analysis framework are publicly available for benchmarking future reconstruction algorithms.

Project description:Positron emission tomography (PET) of lung tumors suffers from breathing-motion induced blurring. Respiratory-correlated PET ameliorates motion blurring and enables visualization of lung tumor functional uptake throughout the breathing cycle but has achieved limited clinical use in radiotherapy planning. In this work, the authors propose a process for generating a gated PET maximum intensity projection (MIP), a breathing-phase projection of the 4D image set comprising gated PET images, as a technique to quantitatively and efficiently incorporate respiratory-correlated PET information into radiotherapy treatment planning.4D-CT and respiratory-gated PET using [(18)F]fluorodeoxyglucose (FDG) were acquired of three patients with a total of four small (4-18 cc), clearly defined lower-lobe lung tumors. Internal target volumes (ITVs) for the lung tumors were generated by threshold-based segmentation of PET-MIP images and ungated PET images (ITV(PET-MIP) and ITV(3D-PET), respectively), and by manual contouring of CT-MIP and end-exhale and end-inhale phases of 4D-CT (ITV(CT-MIP)) by a radiation oncologist. Because of the sensitivity of tumor segmentation to threshold value, several different thresholds were tested for ITV generation, including 40%, 30%, and 20% of maximum standardized uptake value (SUV(max)) for FDG as well as absolute SUV thresholds of 2.5 and 3.0. The normalized overlap and relative volumes of ITV(PET-MIP) and ITV(3D-PET) with respect to ITV(CT-MIP) were compared. The images were also visually compared. ITV(CT-MIP) was considered a gold standard for these tumors with CT-visible morphology.The mean and standard deviation normalized overlap and relative volumes between ITV(PET-MIP) and ITV(CT-MIP) were 0.68 ± 0.07 and 1.07 ± 0.42, respectively, averaged over all four tumors and all five threshold values. The mean and standard deviation normalized overlap and relative volumes of ITV(3D-PET) and ITV(CT-MIP) were 0.47 ± 0.12 and 0.69 ± 0.56, respectively.PET-MIP images better match CT-MIP images for this sample of four small CT-visible tumors as compared to ungated PET images, based on the metrics of volumetric overlap and relative volumes as well as visual interpretation. The PET-MIP is a way to incorporate 4D-PET imaging into the process of lung tumor contouring that is time-efficient for the radiation oncologist and involves minimal effort to implement in treatment planning software, because it requires only a single PET image beyond contouring on CT alone.

Project description:Simulation of x-ray projection images plays an important role in cone beam CT (CBCT) related research projects, such as the design of reconstruction algorithms or scanners. A projection image contains primary signal, scatter signal, and noise. It is computationally demanding to perform accurate and realistic computations for all of these components. In this work, the authors develop a package on graphics processing unit (GPU), called gDRR, for the accurate and efficient computations of x-ray projection images in CBCT under clinically realistic conditions.The primary signal is computed by a trilinear ray-tracing algorithm. A Monte Carlo (MC) simulation is then performed, yielding the primary signal and the scatter signal, both with noise. A denoising process specifically designed for Poisson noise removal is applied to obtain a smooth scatter signal. The noise component is then obtained by combining the difference between the MC primary and the ray-tracing primary signals, and the difference between the MC simulated scatter and the denoised scatter signals. Finally, a calibration step converts the calculated noise signal into a realistic one by scaling its amplitude according to a specified mAs level. The computations of gDRR include a number of realistic features, e.g., a bowtie filter, a polyenergetic spectrum, and detector response. The implementation is fine-tuned for a GPU platform to yield high computational efficiency.For a typical CBCT projection with a polyenergetic spectrum, the calculation time for the primary signal using the ray-tracing algorithms is 1.2-2.3 s, while the MC simulations take 28.1-95.3 s, depending on the voxel size. Computation time for all other steps is negligible. The ray-tracing primary signal matches well with the primary part of the MC simulation result. The MC simulated scatter signal using gDRR is in agreement with EGSnrc results with a relative difference of 3.8%. A noise calibration process is conducted to calibrate gDRR against a real CBCT scanner. The calculated projections are accurate and realistic, such that beam-hardening artifacts and scatter artifacts can be reproduced using the simulated projections. The noise amplitudes in the CBCT images reconstructed from the simulated projections also agree with those in the measured images at corresponding mAs levels.A GPU computational tool, gDRR, has been developed for the accurate and efficient simulations of x-ray projections of CBCT with realistic configurations.

Project description:A novel method called respiratory triggered 4D cone-beam computed tomography (RT 4D CBCT) is described whereby imaging dose can be reduced without degrading image quality. RT 4D CBCT utilizes a respiratory signal to trigger projections such that only a single projection is assigned to a given respiratory bin for each breathing cycle. In contrast, commercial 4D CBCT does not actively use the respiratory signal to minimize image dose.To compare RT 4D CBCT with conventional 4D CBCT, 3600 CBCT projections of a thorax phantom were gathered and reconstructed to generate a ground truth CBCT dataset. Simulation pairs of conventional 4D CBCT acquisitions and RT 4D CBCT acquisitions were developed assuming a sinusoidal respiratory signal which governs the selection of projections from the pool of 3600 original projections. The RT 4D CBCT acquisition triggers a single projection when the respiratory signal enters a desired acquisition bin; the conventional acquisition does not use a respiratory trigger and projections are acquired at a constant frequency. Acquisition parameters studied were breathing period, acquisition time, and imager frequency. The performance of RT 4D CBCT using phase based and displacement based sorting was also studied. Image quality was quantified by calculating difference images of the test dataset from the ground truth dataset. Imaging dose was calculated by counting projections.Using phase based sorting RT 4D CBCT results in 47% less imaging dose on average compared to conventional 4D CBCT. Image quality differences were less than 4% at worst. Using displacement based sorting RT 4D CBCT results in 57% less imaging dose on average, than conventional 4D CBCT methods; however, image quality was 26% worse with RT 4D CBCT.Simulation studies have shown that RT 4D CBCT reduces imaging dose while maintaining comparable image quality for phase based 4D CBCT; image quality is degraded for displacement based RT 4D CBCT in its current implementation.

Project description:PurposePrevious literature has reported contradicting results regarding the relationship between tumor volume changes during radiotherapy treatment for non-small cell lung cancer (NSCLC) patients and locoregional recurrence-free rate or overall survival. The aim of this study is to validate the results from a previous study by using a different volume extraction procedure and evaluating an external validation dataset.MethodsFor two datasets of 94 and 141 NSCLC patients, gross tumor volumes were determined manually to investigate the relationship between tumor volume regression and locoregional control using Kaplan-Meier curves. For both datasets, different subgroups of patients based on histology and chemotherapy regimens were also investigated. For the first dataset (n?=?94), automatically determined tumor volumes were available from a previously published study to further compare their correlation with updated clinical data.ResultsA total of 70 out of 94 patients were classified into the same group as in the previous publication, splitting the dataset based on median tumor regression calculated by the two volume extraction methods. Non-adenocarcinoma patients receiving concurrent chemotherapy with large tumor regression show reduced locoregional recurrence-free rates in both datasets (p?<?0.05 in dataset 2). For dataset 2, the opposite behavior is observed for patients not receiving chemotherapy, which was significant for overall survival (p?=?0.01) but non-significant for locoregional recurrence-free rate (p?=?0.13).ConclusionThe tumor regression pattern observed during radiotherapy is not only influenced by irradiation but depends largely on the delivered chemotherapy schedule, so it follows that the relationship between patient outcome and the degree of tumor regression is also largely determined by the chemotherapy schedule. This analysis shows that the relationship between tumor regression and outcome is complex, and indicates factors that could explain previously reported contradicting findings. This, in turn, will help guide future studies to fully understand the relationship between tumor regression and outcome.

Dataset Information

4D liver tumor localization using cone-beam projections and a biomechanical model.

Publications

4D liver tumor localization using cone-beam projections and a biomechanical model.

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