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

A meta-learning algorithm for respiratory flow prediction from FBG-based wearables in unrestrained conditions

The continuous monitoring of an individual's breathing can be an instrument for the assessment and enhancement of human wellness. Specific respiratory features are unique markers of the deterioration of a health condition, the onset of a disease, fatigue and stressful circumstances. The early and reliable prediction of high-risk situations can result in the implementation of appropriate intervention strategies that might be lifesaving. Hence, smart wearables for the monitoring of continuous breathing have recently been attracting the interest of many researchers and companies. However, most of the existing approaches do not provide comprehensive respiratory information. For this reason, a meta-learning algorithm based on LSTM neural networks for inferring the respiratory flow from a wearable system embedding FBG sensors and inertial units is herein proposed. Different conventional machine learning approaches were implemented as well to ultimately compare the results. The meta-learning algorithm turned out to be the most accurate in predicting respiratory flow when new subjects are considered. Furthermore, the LSTM model memory capability has been proven to be advantageous for capturing relevant aspects of the breathing pattern. The algorithms were tested under different conditions, both static and dynamic, and with more unobtrusive device configurations. The meta-learning results demonstrated that a short one-time calibration may provide subject-specific models which predict the respiratory flow with high accuracy, even when the number of sensors is reduced. Flow RMS errors on the test set ranged from 22.03 L/min, when the minimum number of sensors was considered, to 9.97 L/min for the complete setting (target flow range: 69.231 ± 21.477 L/min). The correlation coefficient r between the target and the predicted flow changed accordingly, being higher (r = 0.9) for the most comprehensive and heterogeneous wearable device configuration. Similar results were achieved even with simpler settings which included the thoracic sensors (r ranging from 0.84 to 0.88; test flow RMSE = 10.99 L/min, when exclusively using the thoracic FBGs). The further estimation of respiratory parameters, i.e., rate and volume, with low errors across different breathing behaviors and postures proved the potential of such approach. These findings lay the foundation for the implementation of reliable custom solutions and more sophisticated artificial intelligence-based algorithms for daily life health-related applications

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 183

This bibliography lists 273 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1978

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 130, July 1974

This special bibliography lists 291 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1974

Real-time intrafraction motion monitoring in external beam radiotherapy

© 2019 Institute of Physics and Engineering in Medicine. Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT

Analysis of first pass myocardial perfusion imaging with magnetic resonance

Early diagnosis and localisation of myocardial perfusion defects is an important step in the treatment of coronary artery disease. Thus far, coronary angiography is the conventional standard investigation for patients with known or suspected coronary artery disease and it provides information about the presence and location of coronary stenoses. In recent years, the development of myocardial perfusion CMR has extended the role of MR in the evaluation of ischaemic heart disease beyond the situations where there have already been gross myocardial changes such as acute infarction or scarring. The ability to non-invasively evaluate cardiac perfusion abnormalities before pathologic effects occur, or as follow-up to therapy, is important to the management of patients with coronary artery disease. Whilst limited multi-slice 2D CMR perfusion studies are gaining increased clinical usage for quantifying gross ischaemic burden, research is now directed towards complete 3D coverage of the myocardium for accurate localisation of the extent of possible defects. In 3D myocardial perfusion imaging, a complete volumetric data set has to be acquired for each cardiac cycle in order to study the first pass of the contrast bolus. This normally requires a relatively large acquisition window within each cardiac cycle to ensure a comprehensive coverage of the myocardium and reasonably high resolution of the images. With multi-slice imaging, long axis cardiac motion during this large acquisition window can cause the myocardium imaged in different cross- sections to be mis-registered, i.e., some part of the myocardium may be imaged more than twice whereas other parts may be missed out completely. This type of mis-registration is difficult to correct for by using post-processing techniques. The purpose of this thesis is to investigate techniques for tracking through plane motion during 3D myocardial perfusion imaging, and a novel technique for extracting intrinsic relationships between 3D cardiac deformation due to respiration and multiple ID real-time measurable surface intensity traces is developed. Despite the fact that these surface intensity traces can be strongly coupled with each other but poorly correlated with respiratory induced cardiac deformation, we demonstrate how they can be used to accurately predict cardiac motion through the extraction of latent variables of both the input and output of the model. The proposed method allows cross-modality reconstruction of patient specific models for dense motion field prediction, which after initial modelling can be use in real-time prospective motion tracking or correction. In CMR, new imaging sequences have significantly reduced the acquisition window whilst maintaining the desired spatial resolution. Further improvements in perfusion imaging will require the application of parallel imaging techniques or making full use of the information content of the ¿-space data. With this thesis, we have proposed RR-UNFOLD and RR-RIGR for significantly reducing the amount of data that is required to reconstruct the perfusion image series. The methods use prospective diaphragmatic navigator echoes to ensure UNFOLD and RIGR are carried out on a series of images that are spatially registered. An adaptive real-time re-binning algorithm is developed for the creation of static image sub-series related to different levels of respiratory motion. Issues concerning temporal smoothing of tracer kinetic signals and residual motion artefact are discussed, and we have provided a critical comparison of the relative merit and potential pitfalls of the two techniques. In addition to the technical and theoretical descriptions of the new methods developed, we have also provided in this thesis a detailed literature review of the current state-of-the-art in myocardial perfusion imaging and some of the key technical challenges involved. Issues concerning the basic background of myocardial ischaemia and its functional significance are discussed. Practical solutions to motion tracking during imaging, predictive motion modelling, tracer kinetic modelling, RR-UNFOLD and RR-RIGR are discussed, all with validation using patient and normal subject data to demonstrate both the strength and potential clinical value of the proposed techniques.Open acces

Technology applications

A summary of NASA Technology Utilization programs for the period of 1 December 1971 through 31 May 1972 is presented. An abbreviated description of the overall Technology Utilization Applications Program is provided as a background for the specific applications examples. Subjects discussed are in the broad headings of: (1) cancer, (2) cardiovascular disease, (2) medical instrumentation, (4) urinary system disorders, (5) rehabilitation medicine, (6) air and water pollution, (7) housing and urban construction, (8) fire safety, (9) law enforcement and criminalistics, (10) transportation, and (11) mine safety

Characterization and Compensation of Hysteretic Cardiac Respiratory Motion in Myocardial Perfusion Studies Through MRI Investigations

Respiratory motion causes artifacts and blurring of cardiac structures in reconstructed images of SPECT and PET cardiac studies. Hysteresis in respiratory motion causes the organs to move in distinct paths during inspiration and expiration. Current respiratory motion correction methods use a signal generated by tracking the motion of the abdomen during respiration to bin list- mode data as a function of the magnitude of this respiratory signal. They thereby fail to account for hysteretic motion. The goal of this research was to demonstrate the effects of hysteretic respiratory motion and the importance of its correction for different medical imaging techniques particularly SPECT and PET. This study describes a novel approach for detecting and correcting hysteresis in clinical SPECT and PET studies. From the combined use of MRI and a synchronized Visual Tracking System (VTS) in volunteers we developed hysteretic modeling using the Bouc-Wen model with inputs from measurements of both chest and abdomen respiratory motion. With the MRI determined heart motion as the truth in the volunteer studies we determined the Bouc Wen model could match the behavior over a range of hysteretic cycles. The proposed approach was validated through phantom simulations and applied to clinical SPECT studies