Automated assessment of transthoracic echocardiogram image quality using deep neural networks

Background Standard views in two-dimensional echocardiography are well established but the quality of acquired images are highly dependent on operator skills and are assessed subjectively. This study is aimed at providing an objective assessment pipeline for echocardiogram image quality by defining a new set of domain-specific quality indicators. Consequently, image quality assessment can thus be automated to enhance clinical measurements, interpretation, and real-time optimization. Methods We have developed deep neural networks for the automated assessment of echocardiographic frame which were randomly sampled from 11,262 adult patients. The private echocardiography dataset consists of 33,784 frames, previously acquired between 2010 and 2020. Unlike non-medical images where full-reference metrics can be applied for image quality, echocardiogram's data is highly heterogeneous and requires blind-reference (IQA) metrics. Therefore, deep learning approaches were used to extract the spatiotemporal features and the image's quality indicators were evaluated against the mean absolute error. Our quality indicators encapsulate both anatomical and pathological elements to provide multivariate assessment scores for anatomical visibility, clarity, depth-gain and foreshortedness, respectively. Results The model performance accuracy yielded 94.4%, 96.8%, 96.2%, 97.4% for anatomical visibility, clarity, depth-gain and foreshortedness, respectively. The mean model error of 0.375±0.0052 with computational speed of 2.52 ms per frame (real-time performance) was achieved. Conclusion The novel approach offers new insight to objective assessment of transthoracic echocardiogram image quality and clinical quantification in A4C and PLAX views. Also lays stronger foundations for operator's guidance system which can leverage the learning curve for the acquisition of optimum quality images during transthoracic exam

Bone-tendon and bone-ligament interface

Reconstruction or repair of ligaments and tendons to bone, following injury, to improve joint function is a very common surgical procedure in orthopedics. The most common surgical ligament reconstruction in humans is anterior cruciate ligament (ACL) reconstruction. Because ACL is not amenable to repair after tear, replacement of the ligament using autograft or allograft tissue is currently the treatment of choice for young and active patients. On the other hand, surgical reattachment to bone is the most reliable treatment in case of rotator cuff tendon tears. Tendon grafting or repair to bone is performed during hand, foot, and ankle surgery. Nowadays, ACL reconstruction and repair of rotator cuff tendon tears are the most commonly performed surgical procedures for soft tissue injuries in orthopedics. © 2014 Springer-Verlag London. All rights are reserved

Measurement of inelastic J / psi photoproduction at HERA

We present a measurement of the inelastic, non diffractive J/$\psi$ photoproduction cross section in the reaction $e^{+} p \to e^{+} {J}/\psi X$ with the ZEUS detector at HERA. The J/$\psi$ was identified using both the $\mu^{+}\mu^{-}$ and $e^{+}e^{-}$ decay channels and events were selected within the range $0.4<z<0.9$ ($0.5<z<0.9$) for the muon (electron) decay mode, where $z$ is the fraction of the photon energy carried by the J/$\psi$ in the proton rest frame. The cross section, the $p^2_T$ and the $z$ distributions, after having subtracted the contributions from resolved photon and diffractive proton dissociative processes, are given for the photon-proton centre of mass energy range $50<W<180$ GeV; $p^2_T$ is the square of the J/$\psi$ transverse momentum with respect to the incoming proton beam direction. In the kinematic range $0.4 1$ GeV$^2$, NLO calculations of the photon-gluon fusion process based on the colour-singlet model are in good agreement with the data. The predictions of a specific leading order colour-octet model, as formulated to describe the CDF data on J/$\psi$ hadroproduction, are not consistent with the data.Comment: 31 pages including 7 figure