104 research outputs found
Surrogate-Driven Motion Model for Motion Compensated Cone-beam CT Reconstruction using Unsorted Projection Data
Cone-beam CT (CBCT) is widely used in image guided radiotherapy, but motion due to breathing can blur the image.
Similar to 4DCT, 4D CBCT can reduce motion blur but 4D
CBCT acquisitions take 2˜4 times longer than 3D CBCT
and often suffer from phase sorting artefact. This study aims
to obtain motion models and motion-free images simultaneously from unsorted 3D CBCT projection data, using a
general motion modelling framework previously proposed by
our group, which was for the first time successfully applied to
real CBCT data equivalent to a one-minute acquisition. The
performance of our method was comprehensively evaluated
through digital phantom simulation and also validated on real
patient data. This study demonstrated the feasibility of our
proposed framework for simultaneous motion model fitting
and motion compensated reconstruction using unsorted 3D
CBCT projection data
Toward adaptive radiotherapy for head and neck patients: Uncertainties in dose warping due to the choice of deformable registration algorithm.
The aims of this work were to evaluate the performance of several deformable image registration (DIR) algorithms implemented in our in-house software (NiftyReg) and the uncertainties inherent to using different algorithms for dose warping
Toward semi-automatic biologically effective dose treatment plan optimisation for Gamma Knife radiosurgery
Objective. Dose-rate effects in Gamma Knife radiosurgery treatments can lead to varying biologically effective dose (BED) levels for the same physical dose. The non-convex BED model depends on the delivery sequence and creates a non-trivial treatment planning problem. We investigate the feasibility of employing inverse planning methods to generate treatment plans exhibiting desirable BED characteristics using the per iso-centre beam-on times and delivery sequence. Approach. We implement two dedicated optimisation algorithms. One approach relies on mixed-integer linear programming (MILP) using a purposely developed convex underestimator for the BED to mitigate local minima issues at the cost of computational complexity. The second approach (local optimisation) is faster and potentially usable in a clinical setting but more prone to local minima issues. It sequentially executes the beam-on time (quasi-Newton method) and sequence optimisation (local search algorithm). We investigate the trade-off between time to convergence and solution quality by evaluating the resulting treatment plans’ objective function values and clinical parameters. We also study the treatment time dependence of the initial and optimised plans using BED95 (BED delivered to 95% of the target volume) values. Main results. When optimising the beam-on times and delivery sequence, the local optimisation approach converges several orders of magnitude faster than the MILP approach (minutes versus hours-days) while typically reaching within 1.2% (0.02-2.08%) of the final objective function value. The quality parameters of the resulting treatment plans show no meaningful difference between the local and MILP optimisation approaches. The presented optimisation approaches remove the treatment time dependence observed in the original treatment plans, and the chosen objectives successfully promote more conformal treatments. Significance. We demonstrate the feasibility of using an inverse planning approach within a reasonable time frame to ensure BED-based objectives are achieved across varying treatment times and highlight the prospect of further improvements in treatment plan quality
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
Multi-task neural network architectures provide a mechanism that jointly
integrates information from distinct sources. It is ideal in the context of
MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT)
scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic
multi-task network that estimates: 1) intrinsic uncertainty through a
heteroscedastic noise model for spatially-adaptive task loss weighting and 2)
parameter uncertainty through approximate Bayesian inference. This allows
sampling of multiple segmentations and synCTs that share their network
representation. We test our model on prostate cancer scans and show that it
produces more accurate and consistent synCTs with a better estimation in the
variance of the errors, state of the art results in OAR segmentation and a
methodology for quality assurance in radiotherapy treatment planning.Comment: Early-accept at MICCAI 2018, 8 pages, 4 figure
Autoadaptive motion modelling for MR-based respiratory motion estimation
This repository contains four T1-weighted 2D MR slice datasets from multiple slice positions covering the entire thorax during free breathing and breath holds. The data was used to evaluate our novel autoadaptive respiratory motion model which we proposed in [1]. In particular, the datasets contain the following:
Acquisition of all sagittal slice positions covering the thorax and one coronal slice position acquired during a breath hold.
Results of registration between adjacent sagittal slice positions [control point displacements (cpp) and displacement fields (dfs)]
40 dynamic acquisitions of each slice position also present in the breath-hold acquired during free breathing.
Results of registration of the dynamic acquisitions to the respective breath-holds slices (cpp's and dfs's).
The data is divided into 4 zip files, each containing the data of one volunteer. The folder structure for each is as follows:
|-- bhs (breath hold data)
| |-- images (images)
| | |-- cor
| | `-- sag
| `-- mfs_slpos2slpos (registration results)
| `-- sag
`-- dyn (dynamic free-breathing data)
|-- images (images)
| |-- cor
| `-- sag
`-- mfs_tpos2tpos (registration results)
|-- cor
`-- sag
Please, see our publication [1] for details on the acquisition sequence and registration used.
--
[1]: CF Baumgartner, C Kolbitsch, JR McClelland, D Rueckert, AP King, Autoadaptive motion modelling for MR-based respiratory motion estimation, Medical Image Analysis (2016), http://dx.doi.org/10.1016/j.media.2016.06.00
Functional Lung MRI at 3.0 T using Oxygen-Enhanced MRI (OE-MRI) and Independent Component Analysis (ICA)
Analysis of dynamic lung OE-MRI is challenging due to the presence of substantial artefacts and poor SNR, particularly at 3 T. We propose a cyclical oxygen delivery scheme and ICA to separate the oxygen-enhancement signal from these confounding factors at 3 T. The proposed method extracts a well-defined oxygen-enhancement signal that removes confounds due to proton density changes, blood flow and motion. We also demonstrate the ability to resolve the opposite enhancement effects of the parenchymal and vascular OE-MRI signals to provide information on pulmonary vasculature and gas distribution. The method is shown to be sensitive to smoking status
A Novel and Automated Approach to Classify Radiation Induced Lung Tissue Damage on CT Scans
Radiation-induced lung damage (RILD) is a common side effect of radiotherapy (RT). The ability to automatically segment, classify, and quantify different types of lung parenchymal change is essential to uncover underlying patterns of RILD and their evolution over time. A RILD dedicated tissue classification system was developed to describe lung parenchymal tissue changes on a voxel-wise level. The classification system was automated for segmentation of five lung tissue classes on computed tomography (CT) scans that described incrementally increasing tissue density, ranging from normal lung (Class 1) to consolidation (Class 5). For ground truth data generation, we employed a two-stage data annotation approach, akin to active learning. Manual segmentation was used to train a stage one auto-segmentation method. These results were manually refined and used to train the stage two auto-segmentation algorithm. The stage two auto-segmentation algorithm was an ensemble of six 2D Unets using different loss functions and numbers of input channels. The development dataset used in this study consisted of 40 cases, each with a pre-radiotherapy, 3-, 6-, 12-, and 24-month follow-up CT scans (n = 200 CT scans). The method was assessed on a hold-out test dataset of 6 cases (n = 30 CT scans). The global Dice score coefficients (DSC) achieved for each tissue class were: Class (1) 99% and 98%, Class (2) 71% and 44%, Class (3) 56% and 26%, Class (4) 79% and 47%, and Class (5) 96% and 92%, for development and test subsets, respectively. The lowest values for the test subsets were caused by imaging artefacts or reflected subgroups that occurred infrequently and with smaller overall parenchymal volumes. We performed qualitative evaluation on the test dataset presenting manual and auto-segmentation to a blinded independent radiologist to rate them as 'acceptable', 'minor disagreement' or 'major disagreement'. The auto-segmentation ratings were similar to the manual segmentation, both having approximately 90% of cases rated as acceptable. The proposed framework for auto-segmentation of different lung tissue classes produces acceptable results in the majority of cases and has the potential to facilitate future large studies of RILD
Independent component analysis (ICA) applied to dynamic oxygen-enhanced MRI (OE-MRI) for robust functional lung imaging at 3 T
PURPOSE: Dynamic lung oxygen-enhanced MRI (OE-MRI) is challenging due to the presence of confounding signals and poor signal-to-noise ratio, particularly at 3 T. We have created a robust pipeline utilizing independent component analysis (ICA) to automatically extract the oxygen-induced signal change from confounding factors to improve the accuracy and sensitivity of lung OE-MRI. METHODS: Dynamic OE-MRI was performed on healthy participants using a dual-echo multi-slice spoiled gradient echo sequence at 3 T and cyclical gas delivery. ICA was applied to each echo within a thoracic mask. The ICA component relating to the oxygen-enhancement signal was automatically identified using correlation analysis. The oxygen-enhancement component was reconstructed, and the percentage signal enhancement (PSE) was calculated. The lung PSE of current smokers was compared with nonsmokers; scan-rescan repeatability, ICA pipeline repeatability, and reproducibility between two vendors were assessed. RESULTS: ICA successfully extracted a consistent oxygen-enhancement component for all participants. Lung tissue and oxygenated blood displayed the opposite oxygen-induced signal enhancements. A significant difference in PSE was observed between the lungs of current smokers and nonsmokers. The scan-rescan repeatability and the ICA pipeline repeatability were good. CONCLUSION: The developed pipeline demonstrated sensitivity to the signal enhancements of the lung tissue and oxygenated blood at 3 T. The difference in lung PSE between current smokers and nonsmokers indicates a likely sensitivity to lung function alterations that may be seen in mild pathology, supporting future use of our methods in patient studies
A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs
This paper presents a new hybrid biomechanical model-based non-rigid image registration method for lung motion estimation. In the proposed method, a patient-specific biomechanical modelling process captures major physically realistic deformations with explicit physical modelling of sliding motion, whilst a subsequent non-rigid image registration process compensates for small residuals. The proposed algorithm was evaluated with 10 4D CT datasets of lung cancer patients. The target registration error (TRE), defined as the Euclidean distance of landmark pairs, was significantly lower with the proposed method (TRE = 1.37 mm) than with biomechanical modelling (TRE = 3.81 mm) and intensity-based image registration without specific considerations for sliding motion (TRE = 4.57 mm). The proposed method achieved a comparable accuracy as several recently developed intensity-based registration algorithms with sliding handling on the same datasets. A detailed comparison on the distributions of TREs with three non-rigid intensity-based algorithms showed that the proposed method performed especially well on estimating the displacement field of lung surface regions (mean TRE = 1.33 mm, maximum TRE = 5.3 mm). The effects of biomechanical model parameters (such as Poisson’s ratio, friction and tissue heterogeneity) on displacement estimation were investigated. The potential of the algorithm in optimising biomechanical models of lungs through analysing the pattern of displacement compensation from the image registration process has also been demonstrated
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