Advancing clinical understanding of surface electromyography biofeedback: bridging research, teaching, and commercial applications.

INTRODUCTION: Expanding the use of surface electromyography-biofeedback (EMG-BF) devices in different therapeutic settings highlights the gradually evolving role of visualizing muscle activity in the rehabilitation process. This review evaluates their concepts, uses, and trends, combining evidence-based research. AREAS COVERED: This review dissects the anatomy of EMG-BF systems, emphasizing their transformative integration with machine-learning (ML) and deep-learning (DL) paradigms. Advances such as the application of sophisticated DL architectures for high-density EMG data interpretation, optimization techniques for heightened DL model performance, and the fusion of EMG with electroencephalogram (EEG) signals have been spotlighted for enhancing biomechanical analyses in rehabilitation. The literature survey also categorizes EMG-BF devices based on functionality and clinical usage, supported by insights from commercial sectors. EXPERT OPINION: The current landscape of EMG-BF is rapidly evolving, chiefly propelled by innovations in artificial intelligence (AI). The incorporation of ML and DL into EMG-BF systems augments their accuracy, reliability, and scope, marking a leap in patient care. Despite challenges in model interpretability and signal noise, ongoing research promises to address these complexities, refining biofeedback modalities. The integration of AI not only predicts patient-specific recovery timelines but also tailors therapeutic interventions, heralding a new era of personalized medicine in rehabilitation and emotional detection.

Multiple two-dimensional active shape model framework for right ventricular segmentation.

Segmentation of the right ventricle (RV) in MRI short axis images is very challenging due to its complex shape and various appearance among the different subjects and cross-sections. Active shape models (ASM) have shown potential for segmenting the complex structures, including the RV, through two formulations: two- and three-dimensional modeling with a reported trade-off between accuracy and complexity of each formulation. In this work, we propose a new framework for modeling the RV surface using multiple 2D contours, where information from multiple cross-sectional images are incorporated into the same model. The proposed method was tested using cardiac MRI images from 56 human subjects. Compared to a golden reference of manually delineated RV contours, the proposed method resulted in significantly lower error than (almost one half) that of the conventional 2D ASM especially at the apical slices. The mean absolute distance of the proposed method was 2.9 ± 2 mm while the conventional 2D ASM resulted in an error of 6.6 ± 4.5 mm. In addition, the computation time of the proposed method was 5 s compared to 4 ± 1 min previously reported for the 3D ASM formulation.

Detecting liver fibrosis using a machine learning-based approach to the quantification of the heart-induced deformation in tagged MR images.

Liver disease causes millions of deaths per year worldwide, and approximately half of these cases are due to cirrhosis, which is an advanced stage of liver fibrosis that can be accompanied by liver failure and portal hypertension. Early detection of liver fibrosis helps in improving its treatment and prevents its progression to cirrhosis. In this work, we present a novel noninvasive method to detect liver fibrosis from tagged MRI images using a machine learning-based approach. Specifically, coronal and sagittal tagged MRI imaging are analyzed separately to capture cardiac-induced deformation of the liver. The liver is manually delineated and a novel image feature, namely, the histogram of the peak strain (HPS) value, is computed from the segmented liver region and is used to classify the liver as being either normal or fibrotic. Classification is achieved using a support vector machine algorithm. The in vivo study included 15 healthy volunteers (10 males; age range 30-45 years) and 22 patients (15 males; age range 25-50 years) with liver fibrosis verified and graded by transient elastography, and 10 patients only had a liver biopsy and were diagnosed with a score of F3-F4. The proposed method demonstrates the usefulness and efficiency of extracting the HPS features from the sagittal slices for patients with moderate fibrosis. Cross-validation of the method showed an accuracy of 83.7% (specificity = 86.6%, sensitivity = 81.8%).

Influence of the analysis technique on estimating hepatic iron content using MRI.

PURPOSE: To investigate the effect of the analysis technique on estimating hepatic iron content using MRI. MATERIALS AND METHODS: We evaluated the influences of single-exponential (EXP), bi-exponential (BEXP), and exponential-plus-constant (CEXP) models; and pixel-wise (MAP), average (AVG), and median (MED) signal calculation methods on T2* measurement using numerical simulations, calibrated phantoms, and nine patients scanned on 3 Tesla MRI, based on regression, correlation, and t-test statistical analysis. RESULTS: The T2* measurement error varied from 9 to 51% in the numerical simulations (T2*: 5-20 ms), depending on signal-to-noise ratio (SNR; range: 8-233) with significant (P < 0.05) difference between actual and predicted values. The MAP method performed well (error < 10%) at high SNR (>100), but resulted in severe estimation errors at low SNR (<50). The EXP model resulted in significant measurement differences (P < 0.05) compared with all other methods, irrespective of SNR. In vivo T2* values ranged from 3.1 to 53.6 ms, depending on the amount of iron overload and implemented analysis method. The BEXP (range: 3.7-50 ms) and CEXP (range: 3.8-53.6 ms) models, and the AVG (range: 3.2-38.8 ms) and MED (range: 3.1-38.5 ms) methods provided more accurate measurements than the EXP model (range: 3.1-18.3 ms) and MAP (range: 3.8-53.6 ms) method, respectively (P < 0.05). The BEXP and CEXP models provided very similar measurements (P > 0.87). Similarly, the AVG and MED methods provided very similar results (P > 0.97), with slightly better performance of the AVG method. CONCLUSION: Different analysis techniques show different performances based on the fitting model and signal calculation method. Based on this study, the CEXP model and AVG method are recommended due to simpler implementation and less influence by the selected analysis region. J. Magn. Reson. Imaging 2016;44:1448-1455.

MRI T2* imaging for assessment of liver iron overload: study of different data analysis approaches.

Background Recently, magnetic resonance imaging (MRI) has been established as an effective technique for evaluating iron overload by measuring T2* in the liver. Purpose To investigate the effects of various factors associated with T2* calculation on the resulting measurement and to determine the analysis criterion that provides the most accurate T2* measurements. Material and Methods Both phantom and in vivo MRI experiments were conducted to study the effects of the selected region of interest (ROI) location and size, signal-averaging method, exponential-fitting model, echo truncation, iron-overload severity, and inter-/intra-observer variabilities on T2* measurements. The results were compared to reference values from the scanner processing software. Results The pixel-by-pixel calculation method provided results in better agreement with the reference values from the MRI scanner than the average or median methods. The choice of the exponential fitting model affected the results, depending on signal-to-noise ratio, number of echoes, minimum and maximum echo times, and tissue composition inside the selected ROI. The single-exponential model resulted in smaller error than the bi-exponential or exponential-plus-constant models, where the latter two models showed similar results. The relative performance of the different models and methods was not affected by the degree of iron-overload. Conclusion Various factors associated with the adopted T2* calculation method affect the resulting measurement. In this study, the pixel-by-pixel calculation method and single-exponential model provided the most accurate results based on the conducted phantom and in vivo MRI experiments.

Cine viability magnetic resonance imaging of the heart without increased scan time.

Cardiac magnetic resonance imaging (MRI) provides information about myocardial morphology, function, and viability from cine, tagged, and late gadolinium enhancement (LGE) images, respectively. While the cine and tagged images are acquired in a time-resolved fashion, the LGE images are acquired at a single timeframe. The purpose of this work is to develop a method for generating cine LGE images without additional scan time. The motion field is extracted from the tagged images, and is then used to guide the deformation of the infarcted region from the acquired LGE image at the acquired timeframe to any other timeframe. Major techniques for motion estimation, including harmonic phase (HARP) and optical flow analysis, are tested in this work for motion estimation. The proposed method is tested on numerical phantom and images from four human subjects. The generated cine LGE images showed both viability and wall motion information in the same set of images without additional scan time or image misregistration problems. The band-pass optical flow analysis resulted in the most accurate motion estimation compared to other methods, especially HARP, which fails to track points at the myocardial boundary. Infarct transmurality from the generated images showed good agreement with myocardial strain, and wall thickening showed good agreement with that measured from conventional cine images. In conclusion, the developed technique allows for generating cine LGE images that enable simultaneous display of wall motion and viability information. The generated images could be useful for estimating myocardial contractility reserve and for treatment prognosis.

Assessment of cardiac mass from tagged magnetic resonance images.

PURPOSE: Tagged and cine magnetic resonance imaging (tMRI and cMRI) techniques are used for evaluating regional and global heart function, respectively. Measuring global function parameters directly from tMRI is challenging due to the obstruction of the anatomical structure by the tagging pattern. The purpose of this study was to develop a method for processing the tMRI images to improve the myocardium-blood contrast in order to estimate global function parameters from the processed images. MATERIALS AND METHODS: The developed method consists of two stages: (1) removing the tagging pattern based on analyzing and modeling the signal distribution in the image's k-space, and (2) enhancing the blood-myocardium contrast based on analyzing the signal intensity variability in the two tissues. The developed method is implemented on images from twelve human subjects. RESULTS: Ventricular mass measured with the developed method showed good agreement with that measured from gold-standard cMRI images. Further, preliminary results on measuring ventricular volume using the developed method are presented. CONCLUSION: The promising results in this study show the potential of the developed method for evaluating both regional and global heart function from a single set of tMRI images, with associated reduction in scan time and patient discomfort.

Tag removal in cardiac tagged MRI images using coupled dictionary learning.

Tagged Magnetic Resonance Imaging (tMRI) is considered to be the gold standard for quantitative assessment of the cardiac local functions. However, the tagging patterns and low myocardium-to-blood-pool contrast of tagged images bring great challenges to cardiac image processing and analysis tasks such as myocardium segmentation and tracking. Hence, there has been growing interest in techniques for removing tagging lines. In this work, a method for removing tagging patterns in tagged MR images using a coupled dictionary learning (CDL) model is proposed. In this model, identical sparse representations are assumed for image patches in the tagged MRI and corresponding cine MRI image spaces. First, we learn a dictionary for the tagged MRI image space. Then, we compute a dictionary for the cine MRI image space so that corresponding tagged and cine patches have the same sparse codes in terms of their respective dictionaries. Finally, in order to produce the de-tagged (cine version) of a test tagged image, the sparse codes of the tagged patches and the trained cine dictionary are used together to construct the de-tagged patches. We have tested this tag removal method on a dataset of tagged cardiac MR images. Our experimental results compared favorably with a recently proposed tag removal method that removes tags in the frequency domain using an optimal band-stop filter of harmonic peaks.

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images.

A collaborative framework was initiated to establish a community resource of ground truth segmentations from cardiac MRI. Multi-site, multi-vendor cardiac MRI datasets comprising 95 patients (73 men, 22 women; mean age 62.73±11.24years) with coronary artery disease and prior myocardial infarction, were randomly selected from data made available by the Cardiac Atlas Project (Fonseca et al., 2011). Three semi- and two fully-automated raters segmented the left ventricular myocardium from short-axis cardiac MR images as part of a challenge introduced at the STACOM 2011 MICCAI workshop (Suinesiaputra et al., 2012). Consensus myocardium images were generated based on the Expectation-Maximization principle implemented by the STAPLE algorithm (Warfield et al., 2004). The mean sensitivity, specificity, positive predictive and negative predictive values ranged between 0.63 and 0.85, 0.60 and 0.98, 0.56 and 0.94, and 0.83 and 0.92, respectively, against the STAPLE consensus. Spatial and temporal agreement varied in different amounts for each rater. STAPLE produced high quality consensus images if the region of interest was limited to the area of discrepancy between raters. To maintain the quality of the consensus, an objective measure based on the candidate automated rater performance distribution is proposed. The consensus segmentation based on a combination of manual and automated raters were more consistent than any particular rater, even those with manual input. The consensus is expected to improve with the addition of new automated contributions. This resource is open for future contributions, and is available as a test bed for the evaluation of new segmentation algorithms, through the Cardiac Atlas Project (www.cardiacatlas.org).

Hybrid intensity- and phase-based optical flow tracking of tagged MRI.

Accurate tracking of the myocardium tissues in tagged Magnetic Resonance Images (MRI) is essential for evaluating the cardiac function. Current tracking methods utilize either the image intensity or the image phase as landmarks that can be tracked. In either case, the performance is vulnerable to the image quality and the fading of the tag lines. In this work, we propose a hybrid optical flow tracking method that combines both the intensity and the phase features of the image. The method is validated using numerical cardiac phantom as well as real MRI data experiments. Both experiments showed that the proposed method outperforms current intensity-based optical flow tracking and the phase-based HARP method with maximum error of 1 pixel at extreme conditions of tag fading.

The influence of the analysis technique on estimating liver iron overload using magnetic resonance imaging T2* quantification.

Iron toxicity is the major cause of tissue damage in patients with iron overload. Iron deposits mainly in the liver, where its concentration closely correlates with whole body iron overload. Different techniques have been proposed for estimating iron content, with liver biopsy being the gold standard despite its invasiveness and influence by sampling error. Recently, magnetic resonance imaging (MRI) has been established as an effective technique for evaluating iron overload by measuring T2(*) in the liver. However, various factors associated with the adopted analysis technique, mainly the exponential fitting model and signal averaging method, affect the resulting measurements. In this study, we evaluate the influences of these factors on T2(*) measurement in numerical phantom, calibrated phantoms, and nine patients with different degrees of iron overload. The results show different performances among the fitting models and signal averaging methods, which are affected by SNR, image quality and signal homogeneity inside the selected ROI for analysis.

Automatic synthesis of cine viability MRI images for evaluation of coronary heart disease.

Coronary heart disease (CHD) is the leading cause of death worldwide. Cardiac magnetic resonance imaging (MRI) is a valuable imaging modality, as it can noninvasively provide information about myocardial function, viability, and morphology. Viability delayed-enhancement (DE) images are acquired at a single timeframe while myocardial functional (tagged) images are acquired as a cine loop of timeframes throughout the cardiac cycle. In this work, we propose a method for estimating DE images at all timeframes in the cardiac cycle without additional scan time to show both viability and functional information in the same image. The method is based on generating a dense motion field of the heart from the acquired tagged images, and then applying the extracted field to the acquired DE image. The developed technique is accurate in generating cine DE images and providing simultaneous information about myocardial viability and wall motion for comprehensive patient evaluation and optimal treatment selection.