Manifold learning of COPD

Analysis of CT scans for studying Chronic Obstructive Pulmonary Disease (COPD) is generally limited to mean scores of disease extent. However, the evolution of local pulmonary damage may vary between patients with discordant effects on lung physiology. This limits the explanatory power of mean values in clinical studies. We present local disease and deformation distributions to address this limitation. The disease distribution aims to quantify two aspects of parenchymal damage: locally diffuse/dense disease and global homogeneity/heterogeneity. The deformation distribution links parenchymal damage to local volume change. These distributions are exploited to quantify inter-patient differences. We used manifold learning to model variations of these distributions in 743 patients from the COPDGene study. We applied manifold fusion to combine distinct aspects of COPD into a single model. We demonstrated the utility of the distributions by comparing associations between learned embeddings and measures of severity. We also illustrated the potential to identify trajectories of disease progression in a manifold space of COPD

Disentangling the roles of aneuploidy, chromosomal instability and tumour heterogeneity in developing resistance to cancer therapies.

Aneuploidy is defined as the cellular state of having a number of chromosomes that deviates from a multiple of the normal haploid chromosome number of a given organism. Aneuploidy can be present in a static state: Down syndrome individuals stably maintain an extra copy of chromosome 21 in their cells. In cancer cells, however, aneuploidy is usually present in combination with chromosomal instability (CIN) which leads to a continual generation of new chromosomal alterations and the development of intratumour heterogeneity (ITH). The prevalence of cells with specific chromosomal alterations is further shaped by evolutionary selection, for example, during the administration of cancer therapies. Aneuploidy, CIN and ITH have each been individually associated with poor prognosis in cancer, and a wealth of evidence suggests they contribute, either alone or in combination, to cancer therapy resistance by providing a reservoir of potential resistant states, or the ability to rapidly evolve resistance. A full understanding of the contribution and interplay between aneuploidy, CIN and ITH is required to tackle therapy resistance in cancer patients. However, these characteristics often co-occur and are intrinsically linked, presenting a major challenge to defining their individual contributions. Moreover, their accurate measurement in both experimental and clinical settings is a technical hurdle. Here, we attempt to deconstruct the contribution of the individual and combined roles of aneuploidy, CIN and ITH to therapy resistance in cancer, and outline emerging approaches to measure and disentangle their roles as a step towards integrating these principles into cancer therapeutic strategy

Autoadaptive motion modelling for MR-based respiratory motion estimation

© 2016 The Authors.Respiratory motion poses significant challenges in image-guided interventions. In emerging treatments such as MR-guided HIFU or MR-guided radiotherapy, it may cause significant misalignments between interventional road maps obtained pre-procedure and the anatomy during the treatment, and may affect intra-procedural imaging such as MR-thermometry. Patient specific respiratory motion models provide a solution to this problem. They establish a correspondence between the patient motion and simpler surrogate data which can be acquired easily during the treatment. Patient motion can then be estimated during the treatment by acquiring only the simpler surrogate data.In the majority of classical motion modelling approaches once the correspondence between the surrogate data and the patient motion is established it cannot be changed unless the model is recalibrated. However, breathing patterns are known to significantly change in the time frame of MR-guided interventions. Thus, the classical motion modelling approach may yield inaccurate motion estimations when the relation between the motion and the surrogate data changes over the duration of the treatment and frequent recalibration may not be feasible.We propose a novel methodology for motion modelling which has the ability to automatically adapt to new breathing patterns. This is achieved by choosing the surrogate data in such a way that it can be used to estimate the current motion in 3D as well as to update the motion model. In particular, in this work, we use 2D MR slices from different slice positions to build as well as to apply the motion model. We implemented such an autoadaptive motion model by extending our previous work on manifold alignment.We demonstrate a proof-of-principle of the proposed technique on cardiac gated data of the thorax and evaluate its adaptive behaviour on realistic synthetic data containing two breathing types generated from 6 volunteers, and real data from 4 volunteers. On synthetic data the autoadaptive motion model yielded 21.45% more accurate motion estimations compared to a non-adaptive motion model 10 min after a change in breathing pattern. On real data we demonstrated the methods ability to maintain motion estimation accuracy despite a drift in the respiratory baseline. Due to the cardiac gating of the imaging data, the method is currently limited to one update per heart beat and the calibration requires approximately 12 min of scanning. Furthermore, the method has a prediction latency of 800 ms. These limitations may be overcome in future work by altering the acquisition protocol

Technical Note: 4D Deformable Digital Phantom for MRI Sequence Development

PURPOSE: MR-guided radiotherapy has different requirements for the images than diagnostic radiology, thus requiring development of novel imaging sequences. MRI simulation is an excellent tool for optimising these new sequences, however currently available software does not provide all the necessary features. In this paper we present a digital framework for testing MRI sequences that incorporates anatomical structure, respiratory motion and realistic presentation of MR physics. METHODS: The extended Cardiac-Torso (XCAT) software was used to create T1, T2 and proton density maps that formed the anatomical structure of the phantom. Respiratory motion model was based on the XCAT deformation vector fields, modified to create a motion model driven by a respiration signal. MRI simulation was carried out with JEMRIS, an open source Bloch simulator. We developed an extension for JEMRIS, which calculates the motion of each spin independently, allowing for deformable motion. RESULTS: The performance of the framework was demonstrated through simulating the acquisition of a 2D cine and demonstrating expected motion ghosts from T2 weighted spin echo acquisitions with different respiratory patterns. All simulations were consistent with behaviour previously described in literature. Simulations with deformable motion were not more time consuming than with rigid motion. CONCLUSIONS: We present a deformable 4D digital phantom framework for MR sequence development. The framework incorporates anatomical structure, realistic breathing patterns, deformable motion and Bloch simulation to achieve accurate simulation of MRI. This method is particularly relevant for testing novel imaging

A multichannel feature-based approach for longitudinal lung CT registration in the presence of radiation induced lung damage

Quantifying parenchymal tissue changes in the lungs is imperative in furthering the study of radiation-induced lung damage (RILD). Registering lung images from different time-points is a key step of this process. Traditional intensity-based registration approaches fail this task due to the considerable anatomical changes that occur between timepoints. This work proposes a novel method to successfully register longitudinal pre- and post-radiotherapy (RT) lung CT scans that exhibit large changes due to RILD, by extracting consistent anatomical features from CT (lung boundaries, main airways, vessels) and using these features to optimise the registrations. Pre-RT and 12-month post-RT CT pairs from fifteen lung cancer patients were used for this study, all with varying degrees of RILD, ranging from mild parenchymal change to extensive consolidation and collapse. For each CT, signed distance transforms from segmentations of the lungs and main airways were generated, and the Frangi vesselness map was calculated. These were concatenated into multi-channel images and diffeomorphic multichannel registration was performed for each image pair using NiftyReg. Traditional intensity-based registrations were also performed for comparison purposes. For the evaluation, the pre- and post-registration landmark distance was calculated for all patients, using an average of 44 manually identified landmark pairs per patient. The mean (standard deviation) distance for all datasets decreased from 15.95 (8.09) mm pre-registration to 4.56 (5.70) mm post-registration, compared to 7.90 (8.97) mm for the intensity-based registrations. Qualitative improvements in image alignment were observed for all patient datasets. For four representative subjects, registrations were performed for 3 additional follow-up timepoints up to 48-months post-RT and similar accuracy was achieved. We have demonstrated that our novel multichannel registration method can successfully align longitudinal scans from RILD patients in the presence of large anatomical changes such as consolidation and atelectasis, outperforming the traditional registration approach both quantitatively and through thorough visual inspection

Consistent and invertible deformation vector fields for a breathing anthropomorphic phantom: a post-processing framework for the XCAT phantom.

Breathing motion is challenging for radiotherapy planning and delivery. This requires advanced four-dimensional (4D) imaging and motion mitigation strategies and associated validation tools with known deformations. Numerical phantoms such as the XCAT provide reproducible and realistic data for simulation-based validation. However, the XCAT generates partially inconsistent and non-invertible deformations where tumours remain rigid and structures can move through each other. We address these limitations by post-processing the XCAT deformation vector fields (DVF) to generate a breathing phantom with realistic motion and quantifiable deformation. An open-source post-processing framework was developed that corrects and inverts the XCAT-DVFs while preserving sliding motion between organs. Those post-processed DVFs are used to warp the first XCAT-generated image to consecutive time points providing a 4D phantom with a tumour that moves consistently with the anatomy, the ability to scale lung density as well as consistent and invertible DVFs. For a regularly breathing case, the inverse consistency of the DVFs was verified and the tumour motion was compared to the original XCAT. The generated phantom and DVFs were used to validate a motion-including dose reconstruction (MIDR) method using isocenter shifts to emulate rigid motion. Differences between the reconstructed doses with and without lung density scaling were evaluated. The post-processing framework produced DVFs with a maximum [Formula: see text]-percentile inverse-consistency error of 0.02 mm. The generated phantom preserved the dominant sliding motion between the chest wall and inner organs. The tumour of the original XCAT phantom preserved its trajectory while deforming consistently with the underlying tissue. The MIDR was compared to the ground truth dose reconstruction illustrating its limitations. MIDR with and without lung density scaling resulted in small dose differences up to 1 Gy (prescription 54 Gy). The proposed open-source post-processing framework overcomes important limitations of the original XCAT phantom and makes it applicable to a wider range of validation applications within radiotherapy

Radiation-Pressure-Mediated Control of an Optomechanical Cavity

We describe and demonstrate a method to control a detuned movable-mirror Fabry-Perot cavity using radiation pressure in the presence of a strong optical spring. At frequencies below the optical spring resonance, self-locking of the cavity is achieved intrinsically by the optomechanical (OM) interaction between the cavity field and the movable end mirror. The OM interaction results in a high rigidity and reduced susceptibility of the mirror to external forces. However, due to a finite delay time in the cavity, this enhanced rigidity is accompanied by an anti-damping force, which destabilizes the cavity. The cavity is stabilized by applying external feedback in a frequency band around the optical spring resonance. The error signal is sensed in the amplitude quadrature of the transmitted beam with a photodetector. An amplitude modulator in the input path to the cavity modulates the light intensity to provide the stabilizing radiation pressure force

Tumour auto-contouring on 2d cine MRI for locally advanced lung cancer: A comparative study

BACKGROUND AND PURPOSE: Radiotherapy guidance based on magnetic resonance imaging (MRI) is currently becoming a clinical reality. Fast 2d cine MRI sequences are expected to increase the precision of radiation delivery by facilitating tumour delineation during treatment. This study compares four auto-contouring algorithms for the task of delineating the primary tumour in six locally advanced (LA) lung cancer patients. MATERIAL AND METHODS: Twenty-two cine MRI sequences were acquired using either a balanced steady-state free precession or a spoiled gradient echo imaging technique. Contours derived by the auto-contouring algorithms were compared against manual reference contours. A selection of eight image data sets was also used to assess the inter-observer delineation uncertainty. RESULTS: Algorithmically derived contours agreed well with the manual reference contours (median Dice similarity index: ⩾0.91). Multi-template matching and deformable image registration performed significantly better than feature-driven registration and the pulse-coupled neural network (PCNN). Neither MRI sequence nor image orientation was a conclusive predictor for algorithmic performance. Motion significantly degraded the performance of the PCNN. The inter-observer variability was of the same order of magnitude as the algorithmic performance. CONCLUSION: Auto-contouring of tumours on cine MRI is feasible in LA lung cancer patients. Despite large variations in implementation complexity, the different algorithms all have relatively similar performance

Cone-Beam Computed Tomography and Deformable Registration-Based “Dose of the Day” Calculations for Adaptive Proton Therapy

Purpose: The aim of this work was to evaluate the feasibility of cone-beam computed tomography (CBCT) and deformable image registration (DIR)–based ‘‘dose of the day’’ calculations for adaptive proton therapy. Methods: Intensity-modulated radiation therapy (IMRT) and proton therapy plans were designed for 3 head and neck patients that required replanning, and hence had a replan computed tomography (CT). Proton plans were generated for different beam arrangements and optimizations: intensity modulated proton therapy and single-field uniform dose. We used an in-house DIR software implemented at our institution to generate a deformed CT, by warping the planning CT onto the daily CBCT. This CBCT had a similar patient geometry to the replanned CT. Dose distributions on the replanned CT were considered the gold standard for ‘‘dose of the day’’ calculations, and were compared with doses on deformed CT (our method) and directly on the calibrated CBCT and rigidly aligned planning CT (alternative methods) in terms of dose difference (DD), by calculating the percentage of voxels whose DD was smaller than 2% of the prescribed dose (DD2%-pp) and the root mean square of the DD distribution (DDRMS). Results: Using a deformed CT, the DD2%-pp within the CBCT imaging volume was 93.2% 6 0.7% for IMRT, and 87% 6 3% for proton plans. In a region of higher dose gradient, we found that although DD2%-pp was 94.3% 6 0.2% for IMRT, in proton plans, it dropped to 74% 6 4%. A larger number of treatment beams and single-field uniform dose optimization appear to make the proton plans less sensitive to DIR errors. For example, within the treated volume, the DDRMS was reduced from 2.6% 6 0.6% of the prescribed doseto 1.0% 6 1.3% ofthe prescribed dose when using single-field uniform dose optimization. Conclusions: Promising results were found for DIR- and CBCT-based proton dose calculations. Proton dose calculations were, however, more sensitive to registration errors than IMRT doses were, particularly in high dose gradient regions