Motion Correction Resolved for MRI via Multi-Tasking: A Simultaneous Reconstruction and Registration Approach

The prolonged time required to form an MR image continues to impose different challenges at both theoretical and clinical levels. With this motivation in mind, this work addresses a central topic in MRI, which is how to correct the motion problem, through a new multitask optimisation framework. The significance is that by tackling the reconstruction and registration tasks simultaneously and jointly one can exploit their strong correlation reducing error propagations and resulting in a significant motion correction. The clinical potentials of our approach are reflected in having higher image quality with fewer artefacts whilst keeping fine details. We evaluate our approach through a set of quantitative and qualitative experimental results

A variational model dedicated to joint segmentation, registration, and atlas generation for shape analysis

In medical image analysis, constructing an atlas, i.e., a mean representative of an ensemble of images, is a critical task for practitioners to estimate variability of shapes inside a population, and to characterize and understand how structural shape changes have an impact on health. This involves identifying significant shape constituents of a set of images, a process called segmentation, and mapping this group of images to an unknown mean image, a task called registration, making a statistical analysis of the image population possible. To achieve this goal, we propose treating these operations jointly to leverage their positive mutual influence, in a hyperelasticity setting, by viewing the shapes to be matched as Ogden materials. The approach is complemented by novel hard constraints on the L\infty norm of both the Jacobian and its inverse, ensuring that the deformation is a bi-Lipschitz homeomorphism. Segmentation is based on the Potts model, which allows for a partition into more than two regions, i.e., more than one shape. The connection to the registration problem is ensured by the dissimilarity measure that aims to align the segmented shapes. A representation of the deformation field in a linear space equipped with a scalar product is then computed in order to perform a geometry-driven Principal Component Analysis (PCA) and to extract the main modes of variations inside the image population. Theoretical results emphasizing the mathematical soundness of the model are provided, among which are existence of minimizers, analysis of a numerical method, asymptotic results, and a PCA analysis, as well as numerical simulations demonstrating the ability of the model to produce an atlas exhibiting sharp edges, high contrast, and a consistent shape

Gray whale detection in satellite imagery using deep learning

Funder: British Antarctic Survey; doi: http://dx.doi.org/10.13039/501100007849AbstractThe combination of very high resolution (VHR) satellite remote sensing imagery and deep learning via convolutional neural networks provides opportunities to improve global whale population surveys through increasing efficiency and spatial coverage. Many whale species are recovering from commercial whaling and face multiple anthropogenic threats. Regular, accurate population surveys are therefore of high importance for conservation efforts. In this study, a state‐of‐the‐art object detection model (YOLOv5) was trained to detect gray whales (Eschrichtius robustus) in VHR satellite images, using training data derived from satellite images spanning different sea states in a key breeding habitat, as well as aerial imagery collected by unoccupied aircraft systems. Varying combinations of aerial and satellite imagery were incorporated into the training set. Mean average precision, whale precision, and recall ranged from 0.823 to 0.922, 0.800 to 0.939, and 0.843 to 0.889, respectively, across eight experiments. The results imply that including aerial imagery in the training data did not substantially impact model performance, and therefore, expansion of representative satellite datasets should be prioritized. The accuracy of the results on real‐world data, along with short training times, indicates the potential of using this method to automate whale detection for population surveys.</jats:p

Accuracy of manual and automated rectal contours using helical tomotherapy image guidance scans during prostate radiotherapy.

Background: Prostate radiotherapy can be delivered using daily image-guided helical tomotherapy. Previous work has shown that contouring the rectum on the kV planning CT scan has a Jaccard conformity index (JCI) of 0.78 for different oncologists (inter-observer variability) and 0.82 for a single oncologist (intra-observer variability) (Lutgendorf-Caucig C et al. Feasibility of CBCT-based target and normal structure delineation in prostate cancer radiotherapy: multi-observer and image multi-modality study. Radiother Oncol. 2011;98(2):154-61.). Using the daily image guidance MV CT scan we have developed automated methods to contour the rectum in order to investigate the dose delivered over a course of treatment. We sought to quantify the accuracy of MV manual and automated contours. Methods: A single oncologist (JES) contoured the rectum on 370 MV scans for 10 participants treated with helical tomotherapy to prostate and pelvic lymph nodes. Accuracy of MV manual contours was tested using a scalar algorithm to enlarge and reduce the contours and intra-observer re-contouring at a 3-month interval. Automated contouring, incorporating the Chan-Vese algorithm, was developed and outputs were compared with manual contours. Results: JES could identify differences in MV manual contour size at the level of ±2.2 mm, equivalent to 1.7 pixels. The median JCI for MV re-contouring was 0.87 with inter-quartile range (IQR) 0.78 to 0.90. When compared with manual contours, automated outputs had a median JCI of 0.79 (IQR 0.74 to 0.79). These results were obtained after 3 iterations, each taking less than 10 seconds. Conclusions: Manual contouring using MV scans was accurate, at a level of approximately 2 mm, and reproducible, with JCI of 0.87. The time taken to contour was approximately 20 minutes per scan. Automated contouring was also reproducible with JCI of 0.79 and, in contrast, took less than a minute per scan. Both manual and automated methods produced results comparable to those for contouring using kV scans. We plan to use auto-contouring to calculate accumulated dose to the rectum in an initial cohort of 100 participants. These doses will be correlated with toxicity as part of the VoxTox Study