A Very Large Cardiac Channel Data Database (VLCD) Used to Evaluate Global Image Coherence (GIC) as an In Vivo Image Quality Metric.

Ultrasound image quality is of utmost importance for a clinician to reach a correct diagnosis. Conventionally, image quality is evaluated using metrics to determine the contrast and resolution. These metrics require localization of specific regions and targets in the image such as a region of interest (ROI), a background region, and/or a point scatterer. Such objects can all be difficult to identify in in-vivo images, especially for automatic evaluation of image quality in large amounts of data. Using a matrix array probe, we have recorded a Very Large cardiac Channel data Database (VLCD) to evaluate coherence as an in vivo image quality metric. The VLCD consists of 33280 individual image frames from 538 recordings of 106 patients. We also introduce a global image coherence (GIC), an in vivo image quality metric that does not require any identified ROI since it is defined as an average coherence value calculated from all the data pixels used to form the image, below a preselected range. The GIC is shown to be a quantitative metric for in vivo image quality when applied to the VLCD. We demonstrate, on a subset of the dataset, that the GIC correlates well with the conventional metrics contrast ratio (CR) and the generalized contrast-to-noise ratio (gCNR) with R = 0.74 ( ) and R = 0.62 ( ), respectively. There exist multiple methods to estimate the coherence of the received signal across the ultrasound array. We further show that all coherence measures investigated in this study are highly correlated ( 0.9 and ) when applied to the VLCD. Thus, even though there are differences in the implementation of coherence measures, all quantify the similarity of the signal across the array and can be averaged into a GIC to evaluate image quality automatically and quantitatively.

Aberration correction in 2D echocardiography.

Background: An aberration correction algorithm has been implemented and demonstrated in an echocardiographic clinical trial using two-dimensional (2D) imaging. The method estimates and compensates arrival time errors between different sub-aperture processor (SAP) signals in a matrix array probe. Methods: Five standard views of channel data cineloops were recorded from 22 patients (11 male and 11 female) resulting in a total of 116 cineloops. The channel data were processed with and without the aberration correction algorithm, allowing for side-by-side comparison of images processed from the same channel data cineloops. Results: The aberration correction algorithm improved image quality, as quantified by a coherence metric, in all 7,380 processed frames. In a blinded and left-right-randomized side-by-side evaluation, four cardiologists (two experienced and two in training) preferred the aberration corrected cineloops in 97% of the cases. The clinicians reported that the corrected cineloops appeared sharper with better contrast and less noise. Many structures like valve leaflets, chordae, endocardium, and endocardial borders appeared narrower and more clearly defined in the aberration corrected images. An important finding is that aberration correction improves contrast between the endocardium and ventricle cavities for every processed image. The gain difference was confirmed by the cardiologists in their feedback and quantified with a median global gain difference estimate between the aberration-corrected and non-corrected images of 1.2 dB. Conclusions: The study shows the potential value of aberration correction in clinical echocardiography. Systematic improvement of images acquired with state-of-art equipment was observed both with quantitative metrics of image quality and clinician preference.

The Generalized Contrast-to-Noise Ratio: A Formal Definition for Lesion Detectability.

In the last 30 years, the contrast-to-noise ratio (CNR) has been used to estimate the contrast and lesion detectability in ultrasound images. Recent studies have shown that the CNR cannot be used with modern beamformers, as dynamic range alterations can produce arbitrarily high CNR values with no real effect on the probability of lesion detection. We generalize the definition of CNR based on the overlap area between two probability density functions. This generalized CNR (gCNR) is robust against dynamic range alterations; it can be applied to all kind of images, units, or scales; it provides a quantitative measure for contrast; and it has a simple statistical interpretation, i.e., the success rate that can be expected from an ideal observer at the task of separating pixels. We test gCNR on several state-of-the-art imaging algorithms and, in addition, on a trivial compression of the dynamic range. We observe that CNR varies greatly between the state-of-the-art methods, with improvements larger than 100%. We observe that trivial compression leads to a CNR improvement of over 200%. The proposed index, however, yields the same value for compressed and uncompressed images. The tested methods showed mismatched performance in terms of lesion detectability, with variations in gCNR ranging from -0.08 to +0.29. This new metric fixes a methodological flaw in the way we study contrast and allows us to assess the relevance of new imaging algorithms.

Assessment of Basic Motions and Technique Identification in Classical Cross-Country Skiing.

Cross-country skiing is a popular Olympic winter sport, which is also used extensively as a recreational activity. While cross-country skiing primarily is regarded as a demanding endurance activity it is also technically challenging, as it contains two main styles (classical and skating) and many sub-techniques within these styles. To further understand the physiological demands and technical challenges of cross-country skiing it is imperative to identify sub-techniques and basic motion features during training and competitions. Therefore, this paper presents features for identification and assessment of the basic motion patterns used during classical-style cross-country skiing. The main motivation for this work is to contribute to the development of a more detailed platform for comparing and communicating results from technique analysis methods, to prevent unambiguous definitions and to allow more precise discussions and quality assessments of an athlete's technical ability. To achieve this, our paper proposes formal motion components and classical style technique definitions as well as sub-technique classifiers. This structure is general and can be used directly for other cyclic activities with clearly defined and distinguishable sub-techniques, such as the skating style in cross country skiing. The motion component features suggested in our approach are arm synchronization, leg kick, leg kick direction, leg kick rotation, foot/ski orientation and energy like measures of the arm, and leg motion. By direct measurement, estimation, and the combination of these components, the traditional sub-techniques of diagonal stride, double poling, double poling kick, herringbone, as well as turning techniques can be identified. By assuming that the proposed definitions of the classical XC skiing sub-techniques are accepted, the presented classifier is proven to map measures from the motion component definitions to a unique representation of the sub-techniques. This formalization and structure may be used on new motion components, measurement principles, and classifiers, and therefore provides a framework for comparing different methodologies. Pilot data from a group of high-level cross-country skiers employing inertial measurement sensors placed on the athlete's arms and skis are used to demonstrate the approach. The results show how detailed sub-technique information can be coupled with physical, track, and environmental data to analyze the effects of specific motion patterns, to develop useful debriefing tools for coaches and athletes in training and competition settings, and to explore new research hypotheses.

Wireless, Real-Time Plane-Wave Coherent Compounding on an iPhone: A Feasibility Study.

The processing power in commercially available hand-held devices has improved dramatically in recent years. In parallel, techniques used in high-frame-rate medical ultrasound imaging, especially plane-wave (PW) imaging, have reduced the number of ultrasound transmissions and amount of data necessary to reconstruct an ultrasound image. In combination, the processing power and data reduction allow all of the processing steps in ultrasound image formation, from raw ultrasound channel data to final rendering, to be performed on a hand-held device. In this study, we send the raw ultrasound channel data from a research scanner wirelessly to an off-the-shelf hand-held device. The hand-held unit's graphical processing unit is processing the raw ultrasound data into the final image, achieving real-time frame rates on the order of 60-90 frames per second (FPS) for a single-angle PW transmission. Higher quality images are achieved by trading off frame rate by coherently compounding multiple PW images, resulting in frame rates on the order of, e.g., 13 FPS when coherently compounding 7 PW transmissions. The presented setup has the potential of providing image quality which could be valuable for simple medical ultrasound diagnostic scans of, e.g., the carotid artery or thyroid. Also, since the computationally expensive beamforming can be done in off-the-shelf devices, this could reduce the price of hand-held ultrasound systems in the future.

The Effect of Dynamic Range Alterations in the Estimation of Contrast.

Many adaptive beamformers claim to produce images with increased contrast, a feature that could enable a better detection of lesions and anatomical structures. Contrast is often quantified using the contrast ratio (CR) and the contrast-to-noise ratio (CNR). The estimation of CR and CNR can be affected by dynamic range alterations (DRAs), such as those produced by a trivial gray-level transformation. Thus, we can form the hypothesis that contrast improvements from adaptive beamformers can, partly, be due to DRA. In this paper, we confirm this hypothesis. We show evidence on the influence of DRA on the estimation of CR and CNR and on the fact that several methods in the state of the art do alter the DR. To study this phenomenon, we propose a DR test (DRT) to estimate the degree of DRA and we apply it to seven beamforming methods. We show that CR improvements correlate with DRT with [Formula: see text] in simulated data and [Formula: see text] in experiments. We also show that DRA may lead to increased CNR values, under some circumstances. These results suggest that claims on lesion detectability, based on CR and CNR values, should be revised.

Robust Short-Lag Spatial Coherence Imaging of Breast Ultrasound Data: Initial Clinical Results.

Ultrasound is frequently used in conjunction with mammography in order to detect breast cancer as early as possible. However, due largely to the heterogeneity of breast tissue, ultrasound images are plagued with clutter that obstructs important diagnostic features. Short-lag spatial coherence (SLSC) imaging has proven to be effective at clutter reduction in noisy ultrasound images. M -Weighted SLSC and Robust-SLSC (R-SLSC) imaging were recently introduced to further improve image quality at higher lag values, while R-SLSC imaging has the added benefit of enabling the adjustment of tissue texture to produce a tissue signal-to-noise ratio (SNR) that is quantitatively similar to B-mode speckle SNR. This paper investigates the initial application of SLSC, M -Weighted SLSC, and R-SLSC imaging to nine targets in the female breast [two simple cysts, one complicated cyst, two fibroadenomas, one hematoma, one complex cystic and solid mass, one invasive ductal carcinoma (IDC), and one ductal carcinoma in situ (DCIS)]. As expected, R-SLSC beamforming improves cyst and hematoma contrast by up to 6.35 and 1.55 dB, respectively, when compared to the original B-mode image, and similar improvements are achieved with SLSC and M -Weighted SLSC imaging. However, an interesting finding from this initial investigation is that the solid masses (i.e., fibroadenoma, complex cystic and solid mass, IDC, and DCIS), which appear as hypoechoic in the B-mode image, have similarly high coherence to that of surrounding tissue in coherence-based images. This work holds promise for using SLSC, M -Weighted SLSC, and/or R-SLSC imaging to distinguish between fluid-filled and solid hypoechoic breast masses.

Sex-based differences in speed, sub-technique selection, and kinematic patterns during low- and high-intensity training for classical cross-country skiing.

OBJECTIVES: We investigated sex-based differences in speed, sub-technique selection, and kinematic patterns during low- (LIT) and high-intensity training (HIT) for classical cross-country (XC) skiing across varying terrain. METHODS: Six male and six female elite XC skiers with an approximately 15% differences in VO2max (men: 68.9±2.9 mL·min-1·kg-1, women: 60.1±3.3 mL·min-1·kg-1) were monitored using a multi-sensor system to collect time-synchronised data of heart rate, speed, and multiple tri-axial inertial measurements units while XC skiing on a 5-km competition track. RESULTS: Men skied 21% faster than women during HIT (5.9±0.3 m·s-1 vs. 4.9±0.2 m·s-1, P < .001), with the greatest difference (26%) while skiing on flat terrain, whereas skiing speed did not significantly differ between men and women during LIT. At similar instructed intensity and rating of perceived effort, women exhibited significantly higher relative heart rate (85±2% vs. 71±3% of maximum) and blood lactate levels (4.0±1.3 vs. 1.2±0.2 mmol/L) during LIT (all P < .001) than men, whereas physiological responses did generally not differ between the sexes during HIT. During both intensities and among both sexes, double poling (DP) was the sub-technique most used relative to distance, followed by miscellaneous sub-techniques (MISC), diagonal stride (DIA), kick double poling (DK) and herringbone (HRB). In relation to distance women used DIA more than men during LIT (22% vs. 17%, P = .009) and HIT (23% vs. 12%, P = .001), whereas men used MISC, including tucking and turning, more than women during LIT (39% vs. 25%, P = .017) and HIT (41% vs. 30%, P = .064). In particular, men used DP more than women while skiing the uphill sections during both LIT (24% vs. 11%, P = .015) and HIT (39% vs. 13%, P = .002). CONCLUSIONS: Our findings provide novel insights into sex-based differences in speed, sub-technique selection, and kinematic patterns during LIT and HIT for classical skiing.

Signal Coherence and Image Amplitude With the Filtered Delay Multiply and Sum Beamformer.

The filtered delay multiply and sum (F-DMAS) beamformer has recently been presented in the context of medical ultrasound image formation. This nonlinear beamformer produces images with improved contrast resolution and noise rejection when compared with the delay and sum (DAS) beamformer. In an attempt to better understand the origin of the improved image quality, this paper shows a theoretical study of the image amplitude statistics backed up by numerical simulations. The results show that the difference in image amplitude using the DAS or F-DMAS beamformers can be partly explained by the way signal coherence influences both beamformers. When using the F-DMAS compared with the DAS beamformer, the image amplitude is shown to be more dependent on the signal coherence. Experimental ultrasound images of a phantom confirm our findings.

Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data.

The automatic classification of sub-techniques in classical cross-country skiing provides unique possibilities for analyzing the biomechanical aspects of outdoor skiing. This is currently possible due to the miniaturization and flexibility of wearable inertial measurement units (IMUs) that allow researchers to bring the laboratory to the field. In this study, we aimed to optimize the accuracy of the automatic classification of classical cross-country skiing sub-techniques by using two IMUs attached to the skier's arm and chest together with a machine learning algorithm. The novelty of our approach is the reliable detection of individual cycles using a gyroscope on the skier's arm, while a neural network machine learning algorithm robustly classifies each cycle to a sub-technique using sensor data from an accelerometer on the chest. In this study, 24 datasets from 10 different participants were separated into the categories training-, validation- and test-data. Overall, we achieved a classification accuracy of 93.9% on the test-data. Furthermore, we illustrate how an accurate classification of sub-techniques can be combined with data from standard sports equipment including position, altitude, speed and heart rate measuring systems. Combining this information has the potential to provide novel insight into physiological and biomechanical aspects valuable to coaches, athletes and researchers.

Hypothesis of Improved Visualization of Microstructures in the Interventricular Septum with Ultrasound and Adaptive Beamforming.

In this work, in vivo ultrasound cardiac images created with Capon's minimum variance adaptive beamformer are compared with images acquired with the conventional delay-and-sum beamformer. Specifically, we provide three views of a human heart imaged through the parasternal short-axis, the parasternal long-axis and the apical four-chamber views. The minimum variance beamformer produced images with improved lateral resolution, resulting in better resolved speckle structure and improved edges, especially on close investigation of the interventricular septum. These improvements in image quality might possibly improve the visualization of microstructures in the human heart.