auto-calibration

INTRODUCTION AND OBJECTIVES: Several studies comparing optical coherence tomography (OCT) and intravascular ultrasound (IVUS) have revealed that OCT consistently provides smaller area and diameter measurements. However, comparative assessment in clinical practice is difficult. Three-dimensional (3D) printing offers a unique opportunity to assess intravascular imaging modalities. We aim to compare intravascular imaging modalities using a 3D-printed coronary artery in a realistic simulator and to assess whether OCT underestimates intravascular dimensions, exploring potential corrections...

The purpose of this study was to develop sedentary cut-points for the activPAL and evaluate their performance against a criterion measure (i.e., activPAL processed by PALbatch). Part 1: Thirty-five adults (23.4 ± 3.6 years) completed 12 laboratory activities (6 sedentary and 6 non-sedentary activities). Receiver operator characteristic (ROC) curves proposed optimal Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) cut-points of 26.4 mg (ENMO) and 30.1 mg (MAD). Part 2: Thirty-eight adults (22...

In this paper, we present a framework for 3D gaze estimation intended to identify the user's focus of attention in a corneal imaging system. The framework uses a headset that consists of three cameras, a scene camera and two eye cameras: an IR camera and an RGB camera. The IR camera is used to continuously and reliably track the pupil and the RGB camera is used to acquire corneal images of the same eye. Deep learning algorithms are trained to detect the pupil in IR and RGB images and to compute a per user 3D model of the eye in real time...

OBJECTIVES: To investigate the image quality and capability of generalized auto-calibrating partially parallel acquisition (GRAPPA) accelerated Three-dimensional (3D) susceptibility weighted imaging (SWI) of the whole spine at 3T. METHODS: A total of 37 pregnant women (gestation age 22 to 39 weeks, average 29 ± 3 weeks) with suspected fetal vertebral anomalies by ultrasound (US) screening underwent 3.0T MR imaging with 3D SWI, conventional two-dimensional (2D) half-flourier acquisition single-shot turbo spin-echo (HASTE) and 3D true fast imaging with steady-state precession (True FISP)...

PURPOSE: To develop a free-breathing (FB) 2D radial balanced steady-state free precession cine cardiac MRI method with 100% respiratory gating efficiency using respiratory auto-calibrated motion correction (RAMCO) based on a motion-sensing camera. METHODS: The signal from a respiratory motion-sensing camera was recorded during a FB retrospectively electrocardiogram triggered 2D radial balanced steady-state free precession acquisition using pseudo-tiny-golden-angle ordering...

PURPOSE: To develop an auto-calibrated technique by joint K-space and Image-space Parallel Imaging (KIPI) for accelerated CEST acquisition. THEORY AND METHODS: The KIPI method selects a calibration frame with a low acceleration factor (AF) and auto-calibration signals (ACS) acquired, from which the coil sensitivity profiles and artifact correction maps are calculated after restoring the k-space by GRAPPA. Then the other frames with high AF and without ACS can be reconstructed by SENSE and artifact suppression...

PURPOSE: To evaluate an iterative learning approach for enhanced performance of robust artificial-neural-networks for k-space interpolation (RAKI), when only a limited amount of training data (auto-calibration signals [ACS]) are available for accelerated standard 2D imaging. METHODS: In a first step, the RAKI model was tailored for the case of limited training data amount. In the iterative learning approach (termed iterative RAKI [iRAKI]), the tailored RAKI model is initially trained using original and augmented ACS obtained from a linear parallel imaging reconstruction...

Ker NL is a general kernel-based framework for auto calibrated reconstruction method, which does not need any explicit formulas of the kernel function for characterizing nonlinear relationships between acquired and unacquired k-space data. It is non-iterative without requiring a large amount of computational costs. Since the limited autocalibration signals (ACS) are acquired to perform KerNL calibration and the calibration suffers from the overfitting problem, more training data can improve the kernel model accuracy...

Spatial light modulators (SLM), capable of dynamically and spatially manipulating electromagnetic waves, have reshaped modern life in projection display and remote sensing. The progress of SLM will expedite next-generation communication and biomedical imaging in the terahertz (THz) range. However, most current THz SLMs are adapted from optical alternatives that still need improvement in terms of uniformity, speed, and bandwidth. Here, we designed, fabricated, and characterized an 8 × 8 THz SLM based on tunable liquid crystal metamaterial absorbers for THz single-pixel compressive imaging...

PURPOSE: Deep learning (DL) based reconstruction using unrolled neural networks has shown great potential in accelerating MRI. However, one of the major drawbacks is the loss of high-frequency details and textures in the output. The purpose of the study is to propose a novel refinement method that uses null-space kernel to refine k-space and improve blurred image details and textures. METHODS: The proposed method constrains the output of the DL to comply to the linear neighborhood relationship calibrated in the auto-calibration lines...

A timely knowledge of concrete and ultra-high-performance concrete (UHPC) strength is possible through the so-called strength-equivalent time (Et) curves. A timely knowledge of concrete strength is useful, for instance, to precisely determine when the shores of a hardening structural element can be safely removed. At the present time, the preparation of the strength-Et curves requires time-consuming and labor-intensive testing prior to the beginning of construction operations. This paper proposes an innovative method to derive the strength-Et and total heat-Et curves for both normal strength and UHPC...

Magnetic resonance imaging (MRI) has revolutionized the radiology. As a leading medical imaging modality, MRI not only visualizes the structures inside body, but also produces functional imaging. However, due to the slow imaging speed constrained by the MR physics, MRI cost is expensive, and patient may feel not comfortable in a scanner for a long time. Parallel MRI has accelerated the imaging speed through the sub-Nyquist sampling strategy and the missing data are interpolated by the multiple coil data acquired...

PURPOSE: To develop an auto-calibrated image reconstruction for highly accelerated multi-directional phase-contrast (PC) MRI that compensates for (1) reconstruction instabilities occurring for phase differences near <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>±</mml:mo> <mml:mi>π</mml:mi></mml:mrow> </mml:math> and (2) phase errors by concomitant magnetic fields that differ for individual radial spokes. THEORY AND METHODS: A model-based image reconstruction for real-time PC MRI based on nonlinear inversion is extended to multi-directional flow by exploiting multiple flow-encodings for the estimation of velocity vectors...

PURPOSE: To improve the estimation of coil sensitivity functions from limited auto-calibration signals (ACS) in SENSE-based reconstruction for brain imaging. METHODS: We propose to use deep learning to estimate coil sensitivity functions by leveraging information from previous scans obtained using the same RF receiver system. Specifically, deep convolutional neural networks were designed to learn an end-to-end mapping from the initial sensitivity to the high-resolution counterpart...

This study aims to investigate the effectiveness of the low impact development (LID) practices on sustainable urban flood storm water management. We applied three LID techniques, i.e. green roof, permeable pavements and bioretention cells, on a highly urbanized watershed in Istanbul, Turkey. The EPA-SWMM was used as a hydrologic-hydraulic model and the model calibration was performed by the well-known Parameter ESTimation (PEST) tool. The rainfall-runoff events occurred between 2012 and 2020. A sensitivity analysis on the parameter selection was applied to reduce the computational cost...

PURPOSE: To develop a scan-specific model that estimates and corrects k-space errors made when reconstructing accelerated MRI data. METHODS: Scan-specific artifact reduction in k-space (SPARK) trains a convolutional-neural-network to estimate and correct k-space errors made by an input reconstruction technique by back-propagating from the mean-squared-error loss between an auto-calibration signal (ACS) and the input technique's reconstructed ACS. First, SPARK is applied to generalized autocalibrating partially parallel acquisitions (GRAPPA) and demonstrates improved robustness over other scan-specific models, such as robust artificial-neural-networks for k-space interpolation (RAKI) and residual-RAKI...

This article describes the use of the genetic algorithm (GA) for the optimization of the performance of a voltage measuring station. This procedure can be used for the instruments for which regular and systematic calibration is required for better performance. Here, a GA-based correction module has been used to internally store a few previous measured-value and measured-time pairs and to use these values to correct the current readings. Sanity-checking runs against zero and standard voltages from time to time and auto-calibrates itself...

PURPOSE: Segmented echo-planar imaging enables high-resolution diffusion-weighted imaging (DWI). However, phase differences between segments can lead to severe artifacts. This work investigates an algorithm to enable reconstruction of interleaved segmented acquisitions without the need of additional calibration or navigator measurements. METHODS: A parallel imaging algorithm is presented that jointly reconstructs all segments of one DWI frame maintaining their phase information...

Generalized auto-calibrating partially parallel acquisitions (GRAPPA) and other parallel Magnetic Resonance Imaging (pMRI) methods restore the unacquired data in k-space by linearly calculating the undersampled data around the missing points. In order to obtain the weight of the linear calculation, a small number of auto-calibration signal (ACS) lines need to be sampled at the center of the k-space. Therefore, the sampling pattern used in this type of method is to full sample data in the middle area and undersample in the outer k-space with nominal reduction factors...

A dual-mode auto-calibrating resistance thermometer (DART) is presented. The novel DART concept combines in one instrument the fast and accurate resistance thermometry with the primary method of Johnson noise thermometry. Unlike previous approaches, the new thermometer measures the spectral density of the thermal noise in the sensing resistor directly in a sequential measurement procedure without using correlation techniques. A sophisticated data analysis corrects the thermometer output for both the parasitic effects of the sensor wiring and the amplifier current noise...