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Cardiac Motion Analysis Based on Optical Flow on Real-Time Three-Dimensional Ultrasound Data
With relatively high frame rates and the ability to acquire volume data sets with a stationary transducer, 3D ultrasound systems, based on matrix phased array transducers, provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analyses require segmentation and visual tracking of the left ventricular endocardial border. Due to the large size of the volumetric data sets, manual tracing of the endocardial border is tedious and impractical for clinical applications. Therefore the development of automatic methods for tracking three-dimensional endocardial motion is essential. In this study, we evaluate a four-dimensional optical flow motion tracking algorithm to determine its capability to follow the endocardial border in three dimensional ultrasound data through time. The four-dimensional optical flow method was implemented using three-dimensional correlation. We tested the algorithm on an experimental open-chest dog data set and a clinical data set acquired with a Philips' iE33 three-dimensional ultrasound machine. Initialized with left ventricular endocardial data points obtained from manual tracing at end-diastole, the algorithm automatically tracked these points frame by frame through the whole cardiac cycle. Finite element surfaces were fitted through the data points obtained by both optical flow tracking and manual tracing by an experienced observer for quantitative comparison of the results. Parameterization of the finite element surfaces was performed and maps displaying relative differences between the manual and semi-automatic methods were compared. The results showed good consistency with less than 10% difference between manual tracing and optical flow estimation on 73% of the entire surface. In addition, the optical flow motion tracking algorithm greatly reduced processing time (about 94% reduction compared to human involvement per cardiac cycle) for analyzing cardiac function in three-dimensional ultrasound data sets. A displacement field was computed from the optical flow output, and a framework for computation of dynamic cardiac information is introduced. The method was applied to a clinical data set from a heart transplant patient and dynamic measurements agreed with known physiology as well as experimental results
Segmentation of heart chambers in 2-D heart ultrasounds with deep learning
Echocardiography is a non-invasive image diagnosis technique where ultrasound waves are used to obtain an image or sequence of the structure and function of the heart. The segmentation of the heart chambers on ultrasound images is a task usually performed by experienced cardiologists, in which they delineate and extract the shape of both atriums and ventricles to obtain important indexes of a patient’s heart condition. However, this task is usually hard to perform accurately due to the poor image quality caused by the equipment and techniques used and due to the variability across different patients and pathologies. Therefore, medical image processing is needed in this particular case to avoid inaccuracy and obtain proper results. Over the last decade, several studies have proved that deep learning techniques are a possible solution to this problem, obtaining good results in automatic segmentation. The major problem with deep learning techniques in medical image processing is the lack of available data to train and test these architectures. In this work we have trained, validated, and tested a convolutional neural network based on the architecture of U-Net for 2D echocardiogram chamber segmentation. The data used for the training of the convolutional neural network was the B-Mode 4-chamber apical view Echogan dataset with data augmentation techniques applied. The novelty of this work is the hyperparameter and architecture optimizations to reduce the computation time while obtaining significant training and testing accuraciesObjectius de Desenvolupament Sostenible::3 - Salut i Benesta
Image based cardiac acceleration map using statistical shape and 3D+t myocardial tracking models; in-vitro study on heart phantom
International audienceIt has been demonstrated that the acceleration signal has potential to monitor heart function and adaptively optimize Cardiac Resynchronization Therapy (CRT) systems. In this paper, we propose a non-invasive method for computing myocardial acceleration from 3D echocardiographic sequences. Displacement of the myocardium was estimated using a two-step approach: (1) 3D automatic segmentation of the myocardium at end-diastole using 3D Active Shape Models (ASM); (2) propagation of this segmentation along the sequence using non-rigid 3D+t image registration (temporal diffeomorphic free-form-deformation, TDFFD). Acceleration was obtained locally at each point of the myocardium from local displacement. The framework has been tested on images from a realistic physical heart phantom (DHP-01, Shelley Medical Imaging Technologies, London, ON, CA) in which the displacement of some control regions was known. Good correlation has been demonstrated between the estimated displacement function from the algorithms and the phantom setup. Due to the limited temporal resolution, the acceleration signals are sparse and highly noisy. The study suggests a non-invasive technique to measure the cardiac acceleration that may be used to improve the monitoring of cardiac mechanics and optimization of CRT
心臓内血流動態可視化のための血球エコーの高フレームレート超音波イメージング
Tohoku University金井浩課
Echocardiography
The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography
Development of a Novel Dataset and Tools for Non-Invasive Fetal Electrocardiography Research
This PhD thesis presents the development of a novel open multi-modal dataset
for advanced studies on fetal cardiological assessment, along with a set of signal
processing tools for its exploitation. The Non-Invasive Fetal Electrocardiography
(ECG) Analysis (NInFEA) dataset features multi-channel electrophysiological
recordings characterized by high sampling frequency and digital resolution,
maternal respiration signal, synchronized fetal trans-abdominal pulsed-wave
Doppler (PWD) recordings and clinical annotations provided by expert
clinicians at the time of the signal collection. To the best of our knowledge,
there are no similar dataset available.
The signal processing tools targeted both the PWD and the non-invasive
fetal ECG, exploiting the recorded dataset. About the former, the study focuses
on the processing aimed at the preparation of the signal for the automatic
measurement of relevant morphological features, already adopted in the
clinical practice for cardiac assessment. To this aim, a relevant step is the automatic
identification of the complete and measurable cardiac cycles in the PWD
videos: a rigorous methodology was deployed for the analysis of the different
processing steps involved in the automatic delineation of the PWD envelope,
then implementing different approaches for the supervised classification of the
cardiac cycles, discriminating between complete and measurable vs. malformed
or incomplete ones. Finally, preliminary measurement algorithms were also developed
in order to extract clinically relevant parameters from the PWD.
About the fetal ECG, this thesis concentrated on the systematic analysis of
the adaptive filters performance for non-invasive fetal ECG extraction processing,
identified as the reference tool throughout the thesis. Then, two studies
are reported: one on the wavelet-based denoising of the extracted fetal ECG
and another one on the fetal ECG quality assessment from the analysis of the
raw abdominal recordings.
Overall, the thesis represents an important milestone in the field, by promoting
the open-data approach and introducing automated analysis tools that
could be easily integrated in future medical devices
Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels
Motion mode (M-mode) recording is an essential part of echocardiography to
measure cardiac dimension and function. However, the current diagnosis cannot
build an automatic scheme, as there are three fundamental obstructs: Firstly,
there is no open dataset available to build the automation for ensuring
constant results and bridging M-mode echocardiography with real-time instance
segmentation (RIS); Secondly, the examination is involving the time-consuming
manual labelling upon M-mode echocardiograms; Thirdly, as objects in
echocardiograms occupy a significant portion of pixels, the limited receptive
field in existing backbones (e.g., ResNet) composed from multiple convolution
layers are inefficient to cover the period of a valve movement. Existing
non-local attentions (NL) compromise being unable real-time with a high
computation overhead or losing information from a simplified version of the
non-local block. Therefore, we proposed RAMEM, a real-time automatic M-mode
echocardiography measurement scheme, contributes three aspects to answer the
problems: 1) provide MEIS, a dataset of M-mode echocardiograms for instance
segmentation, to enable consistent results and support the development of an
automatic scheme; 2) propose panel attention, local-to-global efficient
attention by pixel-unshuffling, embedding with updated UPANets V2 in a RIS
scheme toward big object detection with global receptive field; 3) develop and
implement AMEM, an efficient algorithm of automatic M-mode echocardiography
measurement enabling fast and accurate automatic labelling among diagnosis. The
experimental results show that RAMEM surpasses existing RIS backbones (with
non-local attention) in PASCAL 2012 SBD and human performances in real-time
MEIS tested. The code of MEIS and dataset are available at
https://github.com/hanktseng131415go/RAME
Improved Left Ventricular Mass Quantification with Partial Voxel Interpolation – In-Vivo and Necropsy Validation of a Novel Cardiac MRI Segmentation Algorithm
Background—CMR typically quantifies LV mass (LVM) via manual planimetry (MP), but this approach is time consuming and does not account for partial voxel components - myocardium admixed with blood in a single voxel. Automated segmentation (AS) can account for partial voxels, but this has not been used for LVM quantification. This study used automated CMR segmentation to test the influence of partial voxels on quantification of LVM. Methods and Results—LVM was quantified by AS and MP in 126 consecutive patients and 10 laboratory animals undergoing CMR. AS yielded both partial voxel (ASPV) and full voxel (ASFV) measurements. Methods were independently compared to LVM quantified on echocardiography (echo) and an ex-vivo standard of LVM at necropsy. AS quantified LVM in all patients, yielding a 12-fold decrease in processing time vs. MP (0:21±0:04 vs. 4:18±1:02 min; pFV mass (136±35gm) was slightly lower than MP (139±35; Δ=3±9gm, pPV yielded higher LVM (159±38gm) than MP (Δ=20±10gm) and ASFV (Δ=23±6gm, both pPV and ASFV correlated with larger voxel size (partial r=0.37, pPV yielded better agreement with echo (Δ=20±25gm) than did ASFV (Δ=43±24gm) or MP (Δ=40±22gm, both pPV and ex-vivo results were similar (Δ=1±3gm, p=0.3), whereas ASFV (6±3g, P\u3c0.001) and MP (4±5 g, P=0.02) yielded small but significant differences with LVM at necropsy
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