3D Ultrasound Evaluation of the Fetal Ear – Comparison of an xMatrix Probe with a Conventional Mechanical Probe

2015 ◽  
Vol 38 (05) ◽  
pp. 508-514
Author(s):  
Kai Bürkel ◽  
Ursula Krämer ◽  
Mareike Möllers ◽  
Maria Falkenberg ◽  
Walter Klockenbusch ◽  
...  
Keyword(s):  
Data Acquisition ◽  
3D Ultrasound ◽  
Acquisition Time ◽  
Comparison Study ◽  
Time Saving ◽  
Raw Data ◽  
Ultrasound Evaluation ◽  
Data Acquisition Time ◽  
The Mean ◽  
Mechanical Probe

Abstract Purpose New 3 D technologies like xMatrix probes promise superiority over conventional mechanical probes and may allow a more detailed and time-saving prenatal diagnosis. In a comparison study we evaluate fetal ears. The aim of our study was to compare the following aspects of both techniques: (1) ultrasound detail resolution, (2) raw data acquisition time (AT) and (3) influence of covariates. Materials and Methods 3 D raw data volumes of the fetal ear were collected with the V6 – 2 (V6) and with the xMatrix (X6) probe and were stored after offline customization to a single picture. Two observers scored these images independently. Furthermore, the 3 D raw data acquisition time (AT) was recorded. Concordance between observers, maternal age, body mass index (BMI), weeks of gestation and location of the placenta were evaluated. Results Data volumes of 103 patients were analyzed. The X6 detected anatomic structures like the scapha (p = 0.0146), fossa triangularis (p = 0.0075) and cymba conchae (p = 0.0025) more often. The mean AT of the X6 was shorter compared to the V6 (p < 0.0001). A placenta location in the scanning field increased the AT only for the V6 (p < 0.01). Concordance between observers was higher for the X6 in most cases. Detailed structures were less visible at the end of pregnancy for both devices. Conclusion The comparison study demonstrated clear advantages of the new xMatrix technology concerning an advanced and fast examination of detailed structures like the fetal ear. The importance of 3 D assessment in cases of fetal ear anomaly should be proven in further studies.

scholarly journals Actuator-Assisted Calibration of Freehand 3D Ultrasound System

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Terry K. Koo ◽  
Nathaniel Silvia
Keyword(s):  
Data Acquisition ◽  
3D Ultrasound ◽  
Radiotherapy Planning ◽  
Collinear Point ◽  
Acquisition Time ◽  
Reconstruction Accuracy ◽  
Point Target ◽  
Data Acquisition Time ◽  
Cross Wire ◽  
Freehand 3D Ultrasound

Freehand three-dimensional (3D) ultrasound has been used independently of other technologies to analyze complex geometries or registered with other imaging modalities to aid surgical and radiotherapy planning. A fundamental requirement for all freehand 3D ultrasound systems is probe calibration. The purpose of this study was to develop an actuator-assisted approach to facilitate freehand 3D ultrasound calibration using point-based phantoms. We modified the mathematical formulation of the calibration problem to eliminate the need of imaging the point targets at different viewing angles and developed an actuator-assisted approach/setup to facilitate quick and consistent collection of point targets spanning the entire image field of view. The actuator-assisted approach was applied to a commonly used cross wire phantom as well as two custom-made point-based phantoms (original and modified), each containing 7 collinear point targets, and compared the results with the traditional freehand cross wire phantom calibration in terms of calibration reproducibility, point reconstruction precision, point reconstruction accuracy, distance reconstruction accuracy, and data acquisition time. Results demonstrated that the actuator-assisted single cross wire phantom calibration significantly improved the calibration reproducibility and offered similar point reconstruction precision, point reconstruction accuracy, distance reconstruction accuracy, and data acquisition time with respect to the freehand cross wire phantom calibration. On the other hand, the actuator-assisted modified “collinear point target” phantom calibration offered similar precision and accuracy when compared to the freehand cross wire phantom calibration, but it reduced the data acquisition time by 57%. It appears that both actuator-assisted cross wire phantom and modified collinear point target phantom calibration approaches are viable options for freehand 3D ultrasound calibration.

scholarly journals TEM Random & Ultra-fast Precession ED Tomography for analysis of nm crystals

2014 ◽  
Vol 70 (a1) ◽  
pp. C371-C371 ◽  
Author(s):  
Stavros Nicolopoulos ◽  
Mauro Gemmi ◽  
Alexander Eggeman ◽  
Paul Midgley ◽  
Athanassios Galanis
Keyword(s):  
Electron Microscopy ◽  
Electron Diffraction ◽  
Data Acquisition ◽  
Automatic Measurement ◽  
Ccd Camera ◽  
Acquisition Time ◽  
Diffraction Tomography ◽  
Organic Pigments ◽  
Precession Electron Diffraction ◽  
Data Acquisition Time

Since the invention of Precession Electron Diffraction (PED) in Transmission Electron Microscopy (TEM) by Vincent & Midgley [1] in 1994 and mainly after the introduction of dedicated PED devices to different TEM, the structure of various nano-sized crystals have been solved by Electron Crystalography. The most popular technique that was recently developed based on beam precession is the 3D Precession Diffraction Tomography (PEDT) [2]. A series of ED patterns are collected every 10while the sample is tilted around the goniometer axis. By the automatic measurement of ED intensities (ADT 3D software), the unit cell, crystal symmetry and the detailed crystal structure can be determined. A large number of crystal structures, such as complex metals, alloys, organic pigments, MOF, catalysts etc., have been solved by the 3D PEDT technique. A drawback of 3D PEDT (especially for beam sensitive materials) is the long acquisition times (45–120 min), due to the time consuming step of tracking the crystal under the beam during tilting. To deal with this problem, we have developed two novel approaches: the Random Electron Diffraction Tomography (rPEDT) technique and the Ultra-Fast 3D diffraction tomography (UF PEDT) [3]. By rPEDT technique, a sample area (few microns), where several crystals in different (random) orientations are present, is scanned rapidly using an ASTAR precession device (NanoMEGAS SPRL). PED patterns of all scanned crystals are collected by a fast speed CCD camera (up to 120 frames/sec; 8/12 bit). Concerning UF PEDT, the data acquisition time can be 10-20 times faster compared to hitherto 3D PEDT procedure. UF PEDT can be applied when the crystal shift is stable and reproducible during tilting the sample for a specific tilt range. Thus, such crystals can be tracked by shifting the beam following the crystal displacement during tilting (using ASTAR beam scanning). Obtained PED patterns can be recorded with a fast CCD camera, while crystal is tilted. As a conclusion, rPEDT and UF-PEDT can be considered as breakthrough techniques in electron crystallography as they can be performed in any commercial TEM. Both techniques reduce considerable 3D intensity data acquisition time, and allow the analysis of unknown compounds, including beam sensitive organic crystals, as fast techniques prevents crystal beam damage. The authors acknowledge financial support from EU ESTEEM-2 project (European Network for Electron Microscopy www.esteem2.eu).

scholarly journals Data acquisition time minimization in FANET-based IoT networks

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
Alaa Taima Albu-Salih ◽  
◽  
Osama Majeed Hilal ◽  
Hayder Ayad Khudhair ◽  
◽  
...  
Keyword(s):  
Data Acquisition ◽  
Optimization Model ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Data Gathering ◽  
Acquisition Time ◽  
Iot Applications ◽  
Time Minimization ◽  
Efficient Data ◽  
Data Acquisition Time

Unmanned aerial vehicles (UAVs) is widely used in many military, and civilian applications. UAVs communicate in a Flying Ad hoc Network (FANET) environment where UAVs communicate with each other through an ad hoc network without infrastructure. FANET provide a flexible platform for internet of things (IoT) applications by playing different roles in IoT such as mobile data collector. In fact, in deadline based IoT applications, the deadline is restricted to the critical application level. And as a result, this deadline for data acquisition is not adequate, and FANET cannot collect data from the sensed area with the predetermined deadline. In this paper, a novel efficient data gathering approach for IoT using FANET is proposed. The main objective of this approach is to solve the problem of insufficient deadlines given by FANET in IoT-based deadline applications. Authors will first provide a multi-objective optimization model as a MILP optimization model to solve this problem, and then normalize and add two weighing coefficients to solve the MILP model. The results obtained in the simulation show that the proposed approach can provide efficient data acquisition while guaranteeing the deadline time.

scholarly journals MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning

2020 ◽  
Vol 34 (01) ◽  
pp. 792-799 ◽  
Author(s):  
Wentian Li ◽  
Xidong Feng ◽  
Haotian An ◽  
Xiang Yao Ng ◽  
Yu-Jin Zhang
Keyword(s):  
Reinforcement Learning ◽  
Data Acquisition ◽  
Acquisition Time ◽  
Reconstruction Process ◽  
Anatomical Structures ◽  
Markov Decision ◽  
Data Acquisition Time ◽  
Mri Reconstruction ◽  
Space Data ◽  
Medical Analysis

Compressed sensing magnetic resonance imaging (CS-MRI) is a technique aimed at accelerating the data acquisition of MRI. While down-sampling in k-space proportionally reduces the data acquisition time, it results in images corrupted by aliasing artifacts and blur. To reconstruct images from the down-sampled k-space, recent deep-learning based methods have shown better performance compared with classical optimization-based CS-MRI methods. However, they usually use deep neural networks as a black-box, which directly maps the corrupted images to the target images from fully-sampled k-space data. This lack of transparency may impede practical usage of such methods. In this work, we propose a deep reinforcement learning based method to reconstruct the corrupted images with meaningful pixel-wise operations (e.g. edge enhancing filters), so that the reconstruction process is transparent to users. Specifically, MRI reconstruction is formulated as Markov Decision Process with discrete actions and continuous action parameters. We conduct experiments on MICCAI dataset of brain tissues and fastMRI dataset of knee images. Our proposed method performs favorably against previous approaches. Our trained model learns to select pixel-wise operations that correspond to the anatomical structures in the MR images. This makes the reconstruction process more interpretable, which would be helpful for further medical analysis.

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