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

Robotic Tentacles with Three-Dimensional Mobility Based on Flexible Elastomers

Soft robotic tentacles that move in three dimensions upon pressurization are fabricated by composing flexible elastomers with different tensile strengths using soft lithographic molding. These actuators are able to grip complex shapes and manipulate delicate objects. Embedding functional components into these actuators (for example, a needle for delivering fluid, a video camera, and a suction cup) extends their capabilities.Engineering and Applied Science

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

CT colonography: external clinical validation of an algorithm for computer-assisted prone and supine registration

Purpose To perform external validation of a computer-assisted registration algorithm for prone and supine computed tomographic (CT) colonography and to compare the results with those of an existing centerline method. Materials and Methods All contributing centers had institutional review board approval; participants provided informed consent. A validation sample of CT colonographic examinations of 51 patients with 68 polyps (6–55 mm) was selected from a publicly available, HIPAA compliant, anonymized archive. No patients were excluded because of poor preparation or inadequate distension. Corresponding prone and supine polyp coordinates were recorded, and endoluminal surfaces were registered automatically by using a computer algorithm. Two observers independently scored three-dimensional endoluminal polyp registration success. Results were compared with those obtained by using the normalized distance along the colonic centerline (NDACC) method. Pairwise Wilcoxon signed rank tests were used to compare gross registration error and McNemar tests were used to compare polyp conspicuity. Results Registration was possible in all 51 patients, and 136 paired polyp coordinates were generated (68 polyps) to test the algorithm. Overall mean three-dimensional polyp registration error (mean ± standard deviation, 19.9 mm ± 20.4) was significantly less than that for the NDACC method (mean, 27.4 mm ± 15.1; P = .001). Accuracy was unaffected by colonic segment (P = .76) or luminal collapse (P = .066). During endoluminal review by two observers (272 matching tasks, 68 polyps, prone to supine and supine to prone coordinates), 223 (82%) polyp matches were visible (120° field of view) compared with just 129 (47%) when the NDACC method was used (P < .001). By using multiplanar visualization, 48 (70%) polyps were visible after scrolling ± 15 mm in any multiplanar axis compared with 16 (24%) for NDACC (P < .001). Conclusion Computer-assisted registration is more accurate than the NDACC method for mapping the endoluminal surface and matching the location of polyps in corresponding prone and supine CT colonographic acquisitions

Resolving Variable Respiratory Motion From Unsorted 4D Computed Tomography

4D Computed Tomography (4DCT) is widely used for many clinical applications such as radiotherapy treatment planning, PET and ventilation imaging. However, common 4DCT methods reconstruct multiple breath cycles into a single, arbitrary breath cycle which can lead to various artefacts, impacting the downstream clinical applications. Surrogate driven motion models can estimate continuous variable motion across multiple cycles based on CT segments `unsorted' from 4DCT, but it requires respiration surrogate signals with strong correlation to the internal motion, which are not always available. The method proposed in this study eliminates such dependency by adapting the hyper-gradient method to the optimization of surrogate signals as hyper-parameters, while achieving better or comparable performance, as demonstrated on digital phantom simulations and real patient data. Our method produces a high-quality motion-compensated image together with estimates of the motion, including breath-to-breath variability, throughout the image acquisition. Our method has the potential to improve downstream clinical applications, and also enables retrospective analysis of open access 4DCT dataset where no respiration signals are stored. Code is avaibale at https://github.com/Yuliang-Huang/4DCT-irregular-motion.Comment: Accepted by MICCAI 202

Surrogate-driven respiratory motion model for projection-resolved motion estimation and motion compensated Cone-Beam CT reconstruction from unsorted projection data

OBJECTIVE: As the most common solution to motion artefact for Cone-Beam CT (CBCT) in radiotherapy, 4DCBCT suffers from long acquisition time and phase sorting error. This issue could be addressed if the motion at each projection could be known, which is a severely ill-posed problem. This study aims to obtain the motion at each time point and motion-free image simultaneously from unsorted projection data of a standard 3DCBCT scan. &#xD;Approach: Respiration surrogate signals were extracted by the Intensity Analysis method. A general framework was then deployed to fit a surrogate-driven motion model that characterized the relation between the motion and surrogate signals at each time point. Motion model fitting and motion compensated reconstruction were alternatively and iteratively performed. Stochastic subset gradient based method was used to significantly reduce the computation time. The performance of our method was comprehensively evaluated through digital phantom simulation and also validated on clinical scans from six patients.&#xD;Results: For digital phantom experiments, motion models fitted with ground-truth or extracted surrogate signals both achieved a much lower motion estimation error and higher image quality, compared with non motion-compensated results.For the public SPARE Challenge datasets, more clear lung tissues and less blurry diaphragm could be seen in the motion compensated reconstruction, comparable to the benchmark 4DCBCT images but with a higher temporal resolution. Similar results were observed for two real clinical 3DCBCT scans. SIGNIFICANCE: This study demonstrated the feasibility of our proposed framework for simultaneous motion model fitting and motion compensated reconstruction using unsorted 3D CBCT projection data

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment

Head and neck cancer patients experience systematic anatomical changes as well as random day to day anatomical changes during fractionated radiotherapy treatment. Modelling the expected systematic anatomical changes could aid in creating treatment plans which are more robust against such changes. A patient specific (SM) and population average (AM) model are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. &#xD;Inter- patient correspondence aligned all patients to a model space. Intra- patient correspondence between each planning CT scan and on treatment cone beam CT scans was obtained using diffeomorphic deformable image registration. The stationary velocity fields were then used to develop B-Spline based SMs and AMs. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models. &#xD;Both SMs and AMs were able to capture systematic changes. The average surface distance between the registration propagated contours and the contours generated by the SM was less than 2mm, showing that the SM are able to capture the anatomical changes which a patient experiences during the course of radiotherapy. The testing accuracy was lower than the training accuracy of the SM, suggesting that the model overfits to the limited data available and therefore also captures some of the random day to day changes. For most patients the AMs were a better estimate of the anatomical changes than assuming there were no changes, but the AMs could not capture the variability in the anatomical changes seen in all patients. No difference was seen in the training and testing accuracy of the AMs. These observations were highlighted in both the geometric and dosimetric evaluations and comparisons. The large patient variability highlights the need for more complex, capable population models

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

PET/CT Motion Correction Exploiting Motion Models Fit on Coarsely Gated Data Applied to Finely Gated Data

Motion correction is imperative to the reduction of blurring and artefacts inherent in PET; due to the relatively long acquisition time, and the temporal difference in the attenuation map acquisition. Registration literature contains many examples of spatial regularisers, however there are few temporal regularisers. Motion models can act as such a temporal regulariser, as well as allowing for the interpolation of unseen motion correction results. In our previous work, we applied a motion modelling approach to high TOF resolution non-attenuation corrected data; where the data was corrected to the space of the attenuation map. However, this approach was challenging, especially when low contrast lung tumours are present. This work seeks to extend previous work, by incorporating an approach suggested by Y. Lu et al. (JNM 2018), to perform an initial MLACF reconstruction for the motion estimation. In this work, we combine these two approaches, with several improvements, including; µ-map alignment, as well as, fitting the motion model on low noise low temporal/gate resolution data, and applying it to high noise high temporal/gate resolution data. To test this, XCAT volumes are constructed, and TOF data simulated. Evaluation compares the results of the proposed method against, where the motion model was fit on data gated more finely, where the motion model was fit on noiseless data, and finally non-motion corrected examples. Results indicate that the incorporation of MLACF, and fitting of the motion model on low noise low temporal/gate resolution data, improves contrast and quantification, while allowing for a relatively fast execution time

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