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.

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 (DRA), 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 dynamic range alterations. 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 dynamic range. To study this phenomenon, we propose a dynamic range test (DRT) to estimate the degree of DRA and we apply it to 7 beamforming methods. We show that CR improvements correlate with DRT with R2-adj=0.88 in simulated data and R2-adj=0.98 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.

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.

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.

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.