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Generating lung tumor internal target volumes from 4D-PET maximum intensity projections.

ABSTRACT: 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.

SUBMITTER: Lamb JM

PROVIDER: S-EPMC3203127 | biostudies-other | 2011 Oct

REPOSITORIES: biostudies-other

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Project description:The purpose of this study was to investigate internal tumor volume density overwrite strategies to minimize intensity modulated proton therapy (IMPT) plan degradation of mobile lung tumors. Four planning paradigms were compared for nine lung cancer patients. Internal gross tumor volume (IGTV) and internal clinical target volume (ICTV) structures were defined encompassing their respective volumes in every 4DCT phase. The paradigms use different planning CT (pCT) created from the average intensity projection (AIP) of the 4DCT, overwriting the density within the IGTV to account for movement. The density overwrites were: (a) constant filling with 100 HU (C100) or (b) 50 HU (C50), (c) maximum intensity projection (MIP) across phases, and (d) water equivalent path length (WEPL) consideration from beam's-eye-view. Plans were created optimizing dose-influence matrices calculated with fast GPU Monte Carlo (MC) simulations in each pCT. Plans were evaluated with MC on the 4DCTs using a model of the beam delivery time structure. Dose accumulation was performed using deformable image registration. Interplay effect was addressed applying 10 times rescanning. Significantly less DVH metrics degradation occurred when using MIP and WEPL approaches. Target coverage ([Formula: see text] Gy(RBE)) was fulfilled in most cases with MIP and WEPL ([Formula: see text] Gy (RBE)), keeping dose heterogeneity low ([Formula: see text] Gy(RBE)). The mean lung dose was kept lowest by the WEPL strategy, as well as the maximum dose to organs at risk (OARs). The impact on dose levels in the heart, spinal cord and esophagus were patient specific. Overall, the WEPL strategy gives the best performance and should be preferred when using a 3D static geometry for lung cancer IMPT treatment planning. Newly available fast MC methods make it possible to handle long simulations based on 4D data sets to perform studies with high accuracy and efficiency, even prior to individual treatment planning.

Project description:PurposeTo 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/materialsTo 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.ResultsUsing 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.ConclusionsBio-CBCT-est substantially improves the accuracy of 4D liver tumor localization via cone-beam projections and a biomechanical model.

Project description:PurposeTo investigate whether a novel signal derived from tumor motion allows more precise sorting of 4D-magnetic resonance (4D-MR) image data than do signals based on normal anatomy, reducing levels of stitching artifacts within sorted lung tumor volumes.Methods(4D-MRI) scans were collected for 10 lung cancer patients using a 2D T2-weighted single-shot turbo spin echo sequence, obtaining 25 repeat frames per image slice. For each slice, a tumor-motion signal was generated using the first principal component of movement in the tumor neighborhood (TumorPC1). Signals were also generated from displacements of the diaphragm (DIA) and upper and lower chest wall (UCW/LCW) and from slice body area changes (BA). Pearson r coefficients of correlations between observed tumor movement and respiratory signals were determined. TumorPC1, DIA, and UCW signals were used to compile image stacks showing each patient's tumor volume in a respiratory phase. Unsorted image stacks were also built for comparison. For each image stack, the presence of stitching artifacts was assessed by measuring the roughness of the compiled tumor surface according to a roughness metric (Rg). Statistical differences in weighted means of Rg between any two signals were determined using an exact permutation test.ResultsThe TumorPC1 signal was most strongly correlated with superior-inferior tumor motion, and had significantly higher Pearson r values (median 0.86) than those determined for correlations of UCW, LCW, and BA with superior-inferior tumor motion (p < 0.05). Weighted means of ratios of Rg values in TumorPC1 image stacks to those in unsorted, UCW, and DIA stacks were 0.67, 0.69, and 0.71, all significantly favoring TumorPC1 (p = 0.02-0.05). For other pairs of signals, weighted mean ratios did not differ significantly from one.ConclusionTumor volumes were smoother in 3D image stacks compiled using the first principal component of tumor motion than in stacks compiled with signals based on normal anatomy.

Project description:ObjectivesTo automatically detect MRI artifacts on dynamic contrast-enhanced (DCE) maximum intensity projections (MIPs) of the breast using deep learning.MethodsWomen who underwent clinically indicated breast MRI between October 2015 and December 2019 were included in this IRB-approved retrospective study. We employed two convolutional neural network architectures (ResNet and DenseNet) to detect the presence of artifacts on DCE MIPs of the left and right breasts. Networks were trained on images acquired up to and including the year 2018 using a 5-fold cross-validation (CV). Ensemble classifiers were built with the resulting CV models and applied to an independent holdout test dataset, which was formed by images acquired in 2019.ResultsOur study sample contained 2265 examinations from 1794 patients (median age at first acquisition: 50 years [IQR: 17 years]), corresponding to 1827 examinations of 1378 individuals in the training dataset and 438 examinations of 416 individuals in the holdout test dataset with a prevalence of image-level artifacts of 53% (1951/3654 images) and 43% (381/876 images), respectively. On the holdout test dataset, the ResNet and DenseNet ensembles demonstrated an area under the ROC curve of 0.92 and 0.94, respectively.ConclusionNeural networks are able to reliably detect artifacts that may impede the diagnostic assessment of MIPs derived from DCE subtraction series in breast MRI. Future studies need to further explore the potential of such neural networks to complement quality assurance and improve the application of DCE MIPs in a clinical setting, such as abbreviated protocols.Key points• Deep learning classifiers are able to reliably detect MRI artifacts in dynamic contrast-enhanced protocol-derived maximum intensity projections of the breast. • Automated quality assurance of maximum intensity projections of the breast may be of special relevance for abbreviated breast MRI, e.g., in high-throughput settings, such as cancer screening programs.

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Generating lung tumor internal target volumes from 4D-PET maximum intensity projections.

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