Freezing-of-gait (FOG) and impaired walking are common features of Parkinson’s disease (PD). Provision of external stimuli (cueing) can improve gait, however, many cueing methods are simplistic, increase task loading or have limited utility in a real-world setting. Closed-loop (automated) somatosensory cueing systems have the potential to deliver personalised, discrete cues at the appropriate time, without requiring user input. Further development of cue delivery methods and FOG-detection are required to achieve this. In this feasibility study, we aimed to test if FOG-initiated vibration cues applied to the lower-leg via wearable devices can improve gait in PD, and to develop real-time FOG-detection algorithms. 17 participants with Parkinson’s disease and daily FOG were recruited. During 1 h study sessions, participants undertook 4 complex walking circuits, each with a different intervention: continuous rhythmic vibration cueing (CC), responsive cueing (RC; cues initiated by the research team in response to FOG), device worn with no cueing (NC), or no device (ND). Study sessions were grouped into 3 stages/blocks (A-C), separated by a gap of several weeks, enabling improvements to circuit design and the cueing device to be implemented. Video and onboard inertial measurement unit (IMU) data were analyzed for FOG events and gait metrics. RC significantly improved circuit completion times demonstrating improved overall performance across a range of walking activities. Step frequency was significantly enhanced by RC during stages B and C. During stage C, > 10 FOG events were recorded in 45% of participants without cueing (NC), which was significantly reduced by RC. A machine learning framework achieved 83% sensitivity and 80% specificity for FOG detection using IMU data. Together, these data support the feasibility of closed-loop cueing approaches coupling real-time FOG detection with responsive somatosensory lower-leg cueing to improve gait in PD.

PURPOSE:Compensation for respiratory motion is important during abdominal cancer treatments. In this work we report the results of the 2015 MICCAI Challenge on Liver Ultrasound Tracking and extend the 2D results to relate them to clinical relevance in form of reducing treatment margins and hence sparing healthy tissues, while maintaining full duty cycle. METHODS:We describe methodologies for estimating and temporally predicting respiratory liver motion from continuous ultrasound imaging, used during ultrasound-guided radiation therapy. Furthermore, we investigated the trade-off between tracking accuracy and runtime in combination with temporal prediction strategies and their impact on treatment margins. RESULTS:Based on 2D ultrasound sequences from 39 volunteers, a mean tracking accuracy of 0.9 mm was achieved when combining the results from the 4 challenge submissions (1.2 to 3.3 mm). The two submissions for the 3D sequences from 14 volunteers provided mean accuracies of 1.7 and 1.8 mm. In combination with temporal prediction, using the faster (41 vs 228 ms) but less accurate (1.4 vs 0.9 mm) tracking method resulted in substantially reduced treatment margins (70% vs 39%) in contrast to mid-ventilation margins, as it avoided non-linear temporal prediction by keeping the treatment system latency low (150 vs 400 ms). Acceleration of the best tracking method would improve the margin reduction to 75%. CONCLUSIONS:Liver motion estimation and prediction during free-breathing from 2D ultrasound images can substantially reduce the in-plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non-linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath-hold and gated approaches, and increase treatment efficiency and safety.

Understanding the interaction between shock waves and tissue is critical for advancing the use of shock waves for medical applications, such as cancer therapy. This work aims to study shock wave-cell interaction in a more realistic environment, relevant to in vitro and in vivo studies, by using 3D computational models of healthy and cancerous cells. The results indicate that for a single cell embedded in an extracellular environment, the cellular geometry does not influence significantly the membrane strain but does influence the von Mises stress. On the contrary, the presence of neighbouring cells has a strong effect on the cell response, by increasing fourfold both quantities. The membrane strain response of a cell converges with more than three neighbouring cell layers, indicating that a cluster of four layers of cells is sufficient to model the membrane strain in a large domain of tissue. However, a full 3D tissue model is needed if the stress evaluation is of main interest. A tumour mimicking multicellular spheroid model is also proposed to study mutual interaction between healthy and cancer cells and shows that cancer cells can be specifically targeted in an early stage tumour-mimicking environment. Statement of Significance This work presents 3D computational models of shock-wave/cell interaction in a biophysically realistic environment using real cell morphology in tissue-mimicking phantoms and multicellular spheroids. Results show that cell morphology does not strongly influence the membrane strain but influences the von Mises stress. While the presence of neighbouring cells significantly increases the cell response, four cell layers are enough to capture the membrane strain change in tissue. However, a full tissue model is necessary if accurate stress analysis is needed. The work also shows that cancer cells can be specifically targeted in early stage tumour mimicking environment. This work is a step towards realistic modelling of shock-wave/cell interactions in tissues and provides insight on the use of 3D models for different scenarios. (C) 2018 Acta Materialia Inc. Published by Elsevier Ltd.

Shock waves are used clinically for breaking kidney stones and treating musculoskeletal indications. The mechanisms by which shock waves interact with tissue are still not well understood. Here, ultra-high-speed imaging was used to visualize the deformation of individual cells embedded in a tissue-mimicking phantom when subject to shock-wave exposure from a clinical source. Three kidney epithelial cell lines were considered to represent normal healthy (human renal epithelial), cancer (CAKI-2), and virus-transformed (HK-2) cells. The experimental results showed that during the compressive phase of the shock waves, there was a small (<2%) decrease in the projected cell area, but during the tensile phase, there was a relatively large (∼10%) increase in the projected cell area. The experimental observations were captured by a numerical model with a constitutive material framework consisting of an equation of state for the volumetric response and hyper-viscoelasticity for the deviatoric response. To model the volumetric cell response, it was necessary to change from a higher bulk modulus during the compression to a lower bulk modulus during the tensile shock loading. It was discovered that cancer cells showed a smaller deformation but faster response to the shock-wave tensile phase compared to their noncancerous counterparts. Cell viability experiments, however, showed that cancer cells suffered more damage than other cell types. These data suggest that the cell response to shock waves is specific to the type of cell and waveforms that could be tailored to an application. For example, the model predicts that a shock wave with a tensile stress of 4.59 MPa would increase cell membrane permeability for cancer cells with minimal impact on normal cells.

In this work we propose to combine a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for 3D deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to 2D applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation, combined with graph cuts-based optimization can be applied to 3D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model 'sliding motion'. Applying this method to lung image registration, results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available Computed Tomography lung image dataset (www.dir-lab.com) leads to the observation that our new approach compares very favorably with state-of-the-art in continuous and discrete image registration methods achieving Target Registration Error of 1.16mm on average per landmark.

Histological images provide reliable information on tissue characteristics which can be used to validate and improve our understanding for developing radiological imaging analysis methods. However, due to the large amount of deformation in histology stemming from resected tissues, estimating spatial correspondence with other imaging modalities is a challenging image registration problem. In this work we develop a three-stage framework for nonlinear registration between ex vivo MRI and histology of rectal cancer. For this multi-modality image registration task, two similarity metrics from patch-based feature transformations were used: the dense Scale Invariant Feature Transform (dense SIFT) and the Modality Independent Neighbourhood Descriptor (MIND). The potential of our method is demonstrated on a dataset of eight rectal histology images from two patients using annotated landmarks. The mean registration error was 1.80 mm after the rigid registration steps which improved to 1.08 mm after nonlinear motion correction using dense SIFT and to 1.52 mm using MIND.

This work revisits the concept of graph cuts as an efficient optimization technique in image registration. Previously, due to the computational burden involved, the use of graph cuts in this context has been mainly limited to 2D applications. Here we show how combining graph cuts with supervoxels, resulting in a sparse, yet meaningful graph-based image representation, can overcome previous limitations. Additionally, we show that a relaxed graph representation of the image allows for 'sliding' motion modeling and provides anatomically plausible estimation of the deformations. This is achieved by using image-guided filtering of the estimated sparse deformation field. We evaluate our method on a publicly available CT lung data set and show that our new approach compares very favourably with state-of-the-art in continuous and discrete image registration.

A new framework for tracking of anatomically relevant landmarks on 2D liver ultrasound sequences is presented in this work. It combines logDemons nonlinear registration, which estimates the motion within ultrasound sequences, with a moving window tracking method, that propagates the estimated motion around the region of interest to subsequent frames. Robust and accurate nonlinear registration is obtained by employing the dense Scale Invariant Feature Transform as a similarity measure. The proposed method was evaluated on 24 sequences from the CLUST 2015 challenge. On a total of 62 landmarks within these sequences, a mean target error of 0.91mm was achieved, surpassing the previous challenge best performance, 1.44mm on CLUST 2014.

• Pharmacokinetic analysis of dceMRI has shown potential as a tool to predict and assess tumour treatment response• Quantitative PK analysis can reveal more information from tumour micro-vasculature and increase the reproducibility of dceMRI studies• It requires the estimation of the pre-contrast relaxation time (𝑇10) to convert signal intensity to contrast agent (CA) concentration curves• 𝑇10 maps may be computed from a sequence of Spoiled Gradient Echo (SPGR) volumes with variable flip angles• This method for 𝑇10 estimation assumes that no motion is present during the SPGR acquisitions

Relaxation time (T 10) estimation using variable flip angle sequences is a key step for pharmacokinetic (PK) analysis of tumours in DCE-MRI exams. In this study, the effects of motion within flip angle sequences on the T 10 and subsequent K trans and k ep estimation were examined. It was found that errors in T 10 estimation caused by motion had a significant impact on subsequent PK analysis. A new similarity metric, based on the T 10 regression error, for groupwise motion correction of variable flip angle sequences is proposed and compared against Groupwise Normalized Mutual Information (GNMI). In rigid registration experiments on simulated data, the new metric outperformed GNMI, showing an improvement alignment of over 14% in terms of average target registration error, which is also reflected by a lower T 10 estimation error. Finally, registration was applied to 46 clinical sequences to identify the average amount of motion found in this type of acquisition; this showed an estimated displacement of 0.98mm, which could lead to over 25% K trans estimation error if motion were not corrected.

INTRODUCTION: Dynamic Contrast Enhanced MRI (DCE-MRI) is a functional imaging method whose use in oncology shows a high potential in assessing and predicting treatment response. Before it can be used in standard clinical oncology, however, it first must be validated with respect to accuracy and reliability. One of the steps required to obtain localized quantitative PK information of tumors is the estimation of the pre-contrast relaxation time, T10. To estimate this parameter, a contrast free sequence of spoiled gradient recalled echo (SPGR) with variable flip angles is used. Although the effects of incorrect T10 on the computation of PK parameters have already been reported, motion between the variable flip angle volumes is still an unexplored problem which may be a relevant source of T10 estimation errors. In rectal tumor scans, for example, deformations are unavoidable, as, in addition to the patient's movement during the acquisitions, it is also subject to physiological motion and deformation of the organs. This work aims to evaluate the effects of motion within the variable flip angle sequence on accuracy of the estimation of T10 and in subsequent Tofts model PK parameter (Ktrans and kep) estimation. Moreover, we also estimate the amount of motion present in rectal SPGR sequences. Evaluating and correcting potential sources of error in PK analysis by DCE-MRI may improve reproducibility of this procedure and increase confidence in the results from the clinical research being currently performed. METHODS: Signal intensity (S) in T1 MRI images is a function of the relaxation time (T1), while relaxation time is affected by the contrast agent (CA) concentration. These relations are expressed by the following equations:

Obstructive Sleep Apnoea (OSA) is a sleep disorder with long term consequences. It is often diagnosed with an overnight sleep study or polysomnogram. Monitoring can be costly with long wait times for diagnosis. In this paper we describe a novel OSA screening framework and prototype phone application (app). A database of 856 patients that underwent at-home polysomnography was collected. Features were derived from audio, actigraphy, photoplethysmography (PPG) and demographics, and used as the inputs of a support vector machine (SVM) classifier. The SVM was trained on 735 patients (368 non-OSA and 567 OSA) and tested on 121 patients (44-77 split). Classification on the test set had an accuracy of up to 92.3%. The signal processing and machine learning algorithms were ported to Java and integrated into the phone app. The app records the audio, actigraphy and PPG signals, implements the clinically validated STOP-BANG questionnaire, derives features from the signals, and finally classifies the patient as needing treatment or not using the trained SVM. The resulting software could provide a new, easy-to-use, low-cost and widely available modality for OSA screening.