Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas

The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function. Cardiac motion atlases provide a space of reference in which the motion fields of a cohort of subjects can be directly compared. In this work, a cardiac motion atlas is built from cine MR data from the UK Biobank (~ 6000 subjects). Two automated quality control strategies are proposed to reject subjects with insufficient image quality. Based on the atlas, three dimensionality reduction algorithms are evaluated to learn data-driven cardiac motion descriptors, and statistical methods used to study the association between these descriptors and non-imaging data. Results show a positive correlation between the atlas motion descriptors and body fat percentage, basal metabolic rate, hypertension, smoking status and alcohol intake frequency. The proposed method outperforms the ability to identify changes in cardiac function due to these known cardiovascular risk factors compared to ejection fraction, the most commonly used descriptor of cardiac function. In conclusion, this work represents a framework for further investigation of the factors influencing cardiac health.Comment: 2018 International Workshop on Statistical Atlases and Computational Modeling of the Hear

The Effect of Endurance Training on Pulmonary V˙O2 Kinetics in Solid Organs Transplanted Recipients

BACKGROUND: We investigated the effects of single (SL-ET) and double leg (DL-ET) high-intensity interval training on O2 deficit (O2Def) and mean response time (MRT) during square-wave moderate-intensity exercise (DL-MOD), and on the amplitude of V˙O2p slow component (SCamp), during heavy intensity exercise (DL-HVY), on 33 patients (heart transplant = 13, kidney transplanted = 11 and liver transplanted = 9). METHODS: Patients performed DL incremental step exercise to exhaustion, two DL-MOD tests, and a DL-HVY trial before and after 24 sessions of SL-ET (n = 17) or DL-ET (n = 16). RESULTS: After SL-ET, O2Def, MRT and SCamp decreased by 16.4% ± 13.7 (p = 0.008), by 15.6% ± 13.7 (p = 0.004) and by 35% ± 31 (p = 0.002), respectively. After DL-ET, they dropped by 24.9% ± 16.2 (p < 0.0001), by 25.9% ± 13.6 (p < 0.0001) and by 38% ± 52 (p = 0.0003), respectively. The magnitude of improvement of O2Def, MRT, and SCamp was not significantly different between SL-ET and DL-ET after training. CONCLUSIONS: We conclude that SL-ET is as effective as DL-ET if we aim to improve V˙O2p kinetics in transplanted patients and suggest that the slower, V˙O2p kinetics is mainly caused by the impairment of peripherals exchanges likely due to the immunosuppressive medications and disuse

Association between the donor to recipient ICG-PDR variation rate and the functional recovery of the graft after orthotopic liver transplantation: A case series

Background: Despite current advances in liver transplant surgery, post-operative early allograft dysfunction still complicates the patient prognosis and graft survival. The transition from the donor has not been yet fully understood, and no study quantifies if and how the liver function changes through its transfer to the recipient. The indocyanine green dye plasma disappearance rate (ICG-PDR) is a simple validated tool of liver function assessment. The variation rate between the donor and recipient ICG-PDR still needs to be investigated. Materials and methods: Single-center retrospective study. ICG-PDR determinations were performed before graft retrieval (T1) and 24 hours after transplant (T2). The ICG-PDR relative variation rate between T1 and T2 was calculated to assess the graft function and suffering/recovering. Matched data were compared with the MEAF model of graft dysfunction. Objective: To investigate whether the variation rate between the donor ICG-PDR value and the recipient ICG-PDR measurement on first postoperative day (POD1) can be associated with the MEAF score. Results: 36 ICG-PDR measurements between 18 donors and 18 graft recipients were performed. The mean donor ICG-PDR was 22.64 (SD 6.35), and the mean receiver's ICG-PDR on 1st POD was 17.68 (SD 6.60), with a mean MEAF value of 4.51 (SD 1.23). Pearson's test stressed a good, linear inverse correlation between the ICG-PDR relative variation and the MEAF values, correlation coefficient -0.580 (p = 0.012). Conclusion: The direct correlation between the donor to recipient ICG-PDR variation rate and MEAF was found. Measurements at T1 and T2 showed an up- or downtrend of the graft performance that reflect the MEAF values

An Evaluation of Atlas Selection Methods for Atlas-Based Automatic Segmentation in Radiotherapy Treatment Planning

Atlas-based automatic segmentation is used \nin radiotherapy planning to accelerate the delineation of \norgans at risk (OARs). Atlas selection has been proposed \nas a way to improve the accuracy and execution time of \nsegmentation, assuming that, the more similar the atlas is to \nthe patient, the better the results will be. This paper presents \nan analysis of atlas selection methods in the context of \nradiotherapy treatment planning. For a range of commonly \ncontoured OARs, a thorough comparison of a large class \nof typical atlas selection methods has been performed. For \nthis evaluation, clinically contoured CT images of the head \nand neck (N = 316) and thorax (N = 280) were used. The \nstate-of-the-art intensity and deformation similarity-based \natlas selection methods were found to compare poorly to \nperfect atlas selection. Counter-intuitively, atlas selection \nmethods based on a fixed set of representative atlases \noutperformed atlas selection methods based on the patient \nimage. This study suggests that atlas-based segmentation \nwith currently available selection methods compares poorly \nto the potential best performance, hampering the clinical \nutility of atlas-based segmentation. Effective atlas selection \nremains an open challenge in atlas-based segmentation for \nradiotherapy planning

Probabilistic 3D surface reconstruction from sparse MRI information

Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research. A reliable and effective reconstruction tool should: be fast in prediction of accurate well localised and high resolution models, evaluate prediction uncertainty, work with as little input data as possible. Current deep learning state of the art (SOTA) 3D reconstruction methods, however, often only produce shapes of limited variability positioned in a canonical position or lack uncertainty evaluation. In this paper, we present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets whilst modelling the location of each mesh vertex through a Gaussian distribution. Prior shape information is encoded using a built-in linear principal component analysis (PCA) model. Extensive experiments on cardiac MR data show that our probabilistic approach successfully assesses prediction uncertainty while at the same time qualitatively and quantitatively outperforms SOTA methods in shape prediction. Compared to SOTA, we are capable of properly localising and orientating the prediction via the use of a spatially aware neural network.Comment: MICCAI 202

Signatures for Majorana neutrinos in e−γe^- \gamma collider

We study the possibilities to detect Majorana neutrinos in $e^- \gamma$ colliders for different center of mass energies. We study the $W^- W^- l_j^{+}(l_j^+\equiv e^+ ,\mu^+ ,\tau^+)$ final state which are, due to leptonic number violation, a clear signature for intermediate Majorana neutrino contribution. Such a signal (final lepton have the opposite charge of the initial lepton) is not possible if the heavy neutrinos are Dirac particles. In our calculation we use the helicity formalism to obtain analytic expressions for the amplitude and we have considered that the intermediate neutrinos can be either on shell or off shell. Finally we present our results for the total cross-section and for the angular distribution of the final lepton. We also include a discussion on the expected events number as a function of the input parameters.Comment: Latex file with 12 pages and 6 figures. Submited to Phys. Rev.

Signatures of Right-Handed Majorana neutrinos and gauge bosons in eγe \gamma Collisions

The process $e^- \gamma \to e^+ W_R^- W_R^-$ is studied in the framework of the Left-Right symmetric model. It is shown that this reaction and $e^- \gamma \to l^+ W_R^- W_R^-$ for the arbitrary final lepton are likely to be discovered for CLIC collider option. For relatively light doubly charged Higgs boson its mass does not have much influence on the discovery potential, while for heavier values the probability of the reaction increases.Comment: 18 pages, 7 figures, LaTe