Effects of high-intensity low-volume vs. low-intensity high-volume plyometric jump training on athletic performance in taekwondo athletes: randomized trial.

Background: This study aims to determine the effects of high-intensity low-volume (HILV) versus low-intensity high-volume (LIHV) plyometric jump training on athletic performance in Taekwondo athletes.

Methods: Seventeen Taekwondo athletes (age: 19.35±1.

Effect of Reformer Spring Resistance Modifications on Core Muscle Activity During Basic Core Muscle Exercises.

Background: Core muscles serve as a central group within the functional kinetic chain, stabilizing the spine during movement. The Reformer is one of the most popular and primary pieces used in Pilates core exercises, requiring effective control of core muscles. The purpose of this study was to investigate the effect of Reformer spring resistance on core muscle activity.

Super-resolution deep learning image reconstruction: image quality and myocardial homogeneity in coronary computed tomography angiography.

Background: The recently introduced super-resolution (SR) deep learning image reconstruction (DLR) is potentially effective in reducing noise level and enhancing the spatial resolution. We aimed to investigate whether SR-DLR has advantages in the overall image quality and intensity homogeneity on coronary computed tomography (CT) angiography with four different approaches: filtered-back projection (FBP), hybrid iterative reconstruction (IR), DLR, and SR-DLR.

Methods: Sixty-three patients (mean age, 61 ± 11 years; range, 18-81 years; 40 men) who had undergone coronary CT angiography between June and October 2022 were retrospectively included.

A preliminary study of super-resolution deep learning reconstruction with cardiac option for evaluation of endovascular-treated intracranial aneurysms.

Objectives: To investigate the usefulness of super-resolution deep learning reconstruction (SR-DLR) with cardiac option in the assessment of image quality in patients with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement compared with other image reconstructions.

Methods: This single-centre retrospective study included 50 patients (mean age, 59 years; range, 44-81 years; 13 men) who were treated with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement between January and July 2023. The images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (IR), and SR-DLR.

Improved stent sharpness evaluation with super-resolution deep learning reconstruction in coronary CT angiography.

Objectives: This study aimed to assess the impact of super-resolution deep learning reconstruction (SR-DLR) on coronary CT angiography (CCTA) image quality and blooming artifacts from coronary artery stents in comparison to conventional methods, including hybrid iterative reconstruction (HIR) and deep learning-based reconstruction (DLR).

Methods: A retrospective analysis included 66 CCTA patients from July to November 2022. Major coronary arteries were evaluated for image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR).

Coronary artery calcium quantification: comparison between filtered-back projection, hybrid iterative reconstruction, and deep learning reconstruction techniques.

Background: The reference protocol for the quantification of coronary artery calcium (CAC) should be updated to meet the standards of modern imaging techniques.

Purpose: To assess the influence of filtered-back projection (FBP), hybrid iterative reconstruction (IR), and three levels of deep learning reconstruction (DLR) on CAC quantification on both in vitro and in vivo studies.

Material And Methods: In vitro study was performed with a multipurpose anthropomorphic chest phantom and small pieces of bones.

The effectiveness of post-processing head and neck CT angiography using contrast enhancement boost technique.

Background And Purpose: This study aimed to investigate the potential of contrast enhancement (CE)-boost technique in the head and neck computed tomography (CT) angiography in terms of the objective and subjective image quality.

Materials And Methods: Consecutive patients who underwent head and neck CT angiography between May 2022 and July 2022 were included. The CE-boost images were generated by combining the subtracted iodinated image and contrast-enhanced image.

Effects of Ground Slopes on Erector Spinae Muscle Activities and Characteristics of Golf Swing.

(1) Background: 'Slope' refers to the position faced by golfers on the course. Research on the recruitment strategies of thoracolumbar erector spinae during golf swings on different slopes may help us to understand some underlying mechanisms of lower back pain. (2) Purpose: The purpose of the present study is to assess electromyography (EMG) patterns of the erector spinae muscles (ES) and the kinematics of the trunk and swing parameters while performing golf swings on three different ground slopes: (1) no slope where the ball is level with the feet (BLF), (2) a slope where the ball is above the feet (BAF), and (3) a slope where the ball is below the feet (BBF).

Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction.

Objective: This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with filtered back projection (FBP) and hybrid iterative reconstruction (IR) methods.

Materials And Methods: CCTA images of 51 patients (mean age ± standard deviation [SD], 63.9 ± 9.

A Novel Computed Tomography Image Reconstruction for Improving Visualization of Pulmonary Vasculature: Comparison Between Preprocessing and Postprocessing Images Using a Contrast Enhancement Boost Technique.

Objective: This study aimed to evaluate chest computed tomography (CT) angiography image quality using the contrast enhancement (CE)-boost technique compared with conventional images.

Methods: Forty patients who underwent contrast-enhanced chest CT were included. Combined CT angiography images of the iodinated image obtained from the subtraction of nonenhanced CT images and CT angiography images were used to generate CE-boost images.

Deep learning reconstruction allows for usage of contrast agent of lower concentration for coronary CTA than filtered back projection and hybrid iterative reconstruction.

Background: The demand for homogeneous and higher vascular contrast enhancement is critical to provide an appropriate interpretation of abnormal vascular findings in coronary computed tomography angiography (CTA).

Purpose: To evaluate the effect of various contrast media concentrations (Iohexol-370, Iohexol-300, Iohexol-240) and image reconstructions (filtered back projection [FBP], hybrid iterative reconstruction [IR], and deep learning reconstruction [DLR]) on coronary CTA.

Material And Methods: A total of 63 patients referred for coronary CTA between July and October 2021 were enrolled in this prospective study, and they randomly received one of three contrast media.

Improvement of depiction of the intracranial arteries on brain CT angiography using deep learning reconstruction.

To evaluate the ability of a commercialized deep learning reconstruction technique to depict intracranial vessels on the brain computed tomography angiography and compare the image quality with filtered-back-projection and hybrid iterative reconstruction in terms of objective and subjective measures. Forty-three patients underwent brain computed tomography angiography, and images were reconstructed using three algorithms: filtered-back-projection, hybrid iterative reconstruction, and deep learning reconstruction. The image noise, computed tomography attenuation value, signal-to-noise ratio, and contrast-to-noise ratio were measured in the bilateral cavernous segment of the internal carotid artery, vertebral artery, basilar apex, horizontal segment of the middle cerebral artery and used for the objective assessment of the image quality among the three different reconstructions.

An equal-TE ultrafast 3D gradient-echo imaging method with high tolerance to magnetic susceptibility artifacts: Application to BOLD functional MRI.

Purpose: To develop an ultrafast 3D gradient echo-based MRI method with constant TE and high tolerance to B inhomogeneity, dubbed ERASE (equal-TE rapid acquisition with sequential excitation), and to introduce its use in BOLD functional MRI (fMRI).

Theory And Methods: Essential features of ERASE, including spin behavior, were characterized, and a comparison study was conducted with conventional EPI. To demonstrate high tolerance to B inhomogeneity, in vivo imaging of the mouse brain with a fiber-optic implant was performed at 9.

k-Space deep learning for reference-free EPI ghost correction.

Purpose: Nyquist ghost artifacts in echo planar imaging (EPI) are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field MRI due to the nonlinear and time-varying local magnetic field changes. Recently, it was shown that the problem of ghost correction can be reformulated as k-space interpolation problem that can be solved using structured low-rank Hankel matrix approaches.

A new ultrafast 3D gradient echo-based imaging method using quadratic-phase encoding.

Purpose: To propose a novel 3D ultrafast gradient echo-based MRI method, dubbed RASE, using quadratic-phase encoding.

Theory And Methods: Several characteristics of RASE, including spin behaviors, spatial resolution, SNR, and reduction of susceptibility-induced signal loss, were analytically described. A way of compensating for TE variation was suggested in the quadratic phase-encoding direction.

Inter-vender and test-retest reliabilities of resting-state functional magnetic resonance imaging: Implications for multi-center imaging studies.

This prospective multi-center study aimed to evaluate the inter-vendor and test-retest reliabilities of resting-state functional magnetic resonance imaging (RS-fMRI) by assessing the temporal signal-to-noise ratio (tSNR) and functional connectivity. Study included 10 healthy subjects and each subject was scanned using three 3T MR scanners (GE Signa HDxt, Siemens Skyra, and Philips Achieva) in two sessions. The tSNR was calculated from the time course data.