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3D echocardiography (3DE) will gain increasing acceptance as a routine clinical tool as the technology evolves due to advances in technology and computer processing power. Images obtained from 3DE provide more accurate assessment of complex cardiac anatomy and sophisticated functional mechanisms compared with conventional 2D echocardiography (2DE), and are comparable to those achieved with magnetic resonance imaging. Many of the limitations associated with the early iterations of 3DE prevented their widespread clinical application. However, recent significant improvements in transducer and post-processing software technologies have addressed many of these issues. Furthermore, the most recent advances in the ability to image the entire heart in realtime and fully automated quantification have poised 3DE to become more ubiquitous in clinical routine. Realtime 3DE (RT3DE) systems offer further improvements in the diagnostic and treatment planning capabilities of cardiac ultrasound. Innovations such as the ability to acquire non-stitched, realtime, full-volume 3D images of the heart in a single heart cycle promise to overcome some of the current limitations of current RT3DE systems, which acquire images over four to seven cardiac cycles, with the need for gating and the potential for stitch artefacts.

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The biological response of rodent kidneys to low frequency, full volume diagnostic contrast-enhanced ultrasound imaging: Pilot data

Data in Brief ◽

10.1016/j.dib.2019.104170 ◽

2019 ◽

Vol 25 ◽

pp. 104170

Author(s):

A. Gloria Nyankima ◽

Sandeep Kasoji ◽

Rachel Cianciolo ◽

Paul A. Dayton ◽

Emily H. Chang

Keyword(s):

Ultrasound Imaging ◽

Low Frequency ◽

Biological Response ◽

Contrast Enhanced Ultrasound ◽

Pilot Data ◽

Contrast Enhanced ◽

Full Volume

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Feasibility of One-Beat Real-Time Full-Volume Three-Dimensional Echocardiography for Assessing Left Ventricular Volumes and Deformation Parameters

Journal of the American Society of Echocardiography ◽

10.1016/j.echo.2016.05.001 ◽

2016 ◽

Vol 29 (9) ◽

pp. 853-860.e2 ◽

Cited By ~ 16

Author(s):

Li-Tan Yang ◽

Yasufumi Nagata ◽

Kyoko Otani ◽

Yuichiro Kado ◽

Yutaka Otsuji ◽

...

Keyword(s):

Real Time ◽

Three Dimensional ◽

Left Ventricular ◽

Left Ventricular Volumes ◽

Ventricular Volumes ◽

Deformation Parameters ◽

Dimensional Echocardiography ◽

Three Dimensional Echocardiography ◽

Full Volume

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3-D prestack Kirchhoff beam migration for depth imaging

Geophysics ◽

10.1190/1.1444847 ◽

2000 ◽

Vol 65 (5) ◽

pp. 1592-1603 ◽

Cited By ~ 58

Author(s):

Yonghe Sun ◽

Fuhao Qin ◽

Steve Checkles ◽

Jacques P. Leveille

Keyword(s):

Distributed Environment ◽

Small Surface ◽

Depth Imaging ◽

Depth Migration ◽

Kirchhoff Migration ◽

Surface Patches ◽

Image Volume ◽

Kirchhoff Depth Migration ◽

Full Volume ◽

Single Set

A beam implementation is presented for efficient full‐volume 3-D prestack Kirchhoff depth migration of seismic data. Unlike conventional Kirchhoff migration in which the input seismic traces in time are migrated one trace at a time into the 3-D image volume for the earth’s subsurface, the beam migration processes a group of input traces (a supergather) together. The requirement for a supergather is that the source and receiver coordinates of the traces fall into two small surface patches. The patches are small enough that a single set of time maps pertaining to the centers of the patches can be used to migrate all the traces within the supergather by Taylor expansion or interpolation. The migration of a supergather consists of two major steps: stacking the traces into a τ-P beam volume, and mapping the beams into the image volume. Since the beam volume is much smaller than the image volume, the beam migration cost is roughly proportional to the number of input supergathers. The computational speedup of beam migration over conventional Kirchhoff migration is roughly proportional to [Formula: see text], the average number of traces per supergather, resulting a theoretical speedup up to two orders of magnitudes. The beam migration was successfully implemented and has been in production use for several years. A factor of 5–25 speedup has been achieved in our in‐house depth migrations. The implementation made 3-D prestack full‐volume depth imaging feasible in a parallel distributed environment.

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Computed tomography-based radiomics analysis to predict lymphovascular invasion in esophageal squamous cell carcinoma

British Journal of Radiology ◽

10.1259/bjr.20210918 ◽

2021 ◽

Author(s):

Hui Peng ◽

Qiuxing Yang ◽

Ting Xue ◽

Qiaoling Chen ◽

Manman Li ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Esophageal Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Lymphovascular Invasion ◽

Validation Cohort ◽

Regression Method ◽

Training Cohort ◽

Volume Model ◽

Single Slice ◽

Full Volume

Objective The present study explored the value of preoperative CT radiomics in predicting lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC). Methods A retrospective analysis of 294 pathologically confirmed ESCC patients undergoing surgical resection and their preoperative chest-enhanced CT arterial images were used to delineate the target area of the lesion. All patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Radiomics features were extracted from single-slice, three-slice, and full-volume regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) regression method was applied to select valuable radiomics features. Radiomics models were constructed using logistic regression method and were validated using leave group out cross-validation (LGOCV) method. The performance of the three models was evaluated using the receiver characteristic curve (ROC) and decision curve analysis (DCA). Results A total of 1218 radiomics features were separately extracted from single-slice ROIs, three-slice ROIs, and full-volume ROIs, and 16, 13 and 18 features, respectively, were retained after optimization and screening to construct a radiomics prediction model. The results showed that the AUC of the full-volume model was higher than that of the single-slice and three-slice models. According to LGOCV, the full-volume model showed the highest mean AUC for the training cohort and the validation cohort. Conclusion The full-volume radiomics model has the best predictive performance and thus can be used as an auxiliary method for clinical treatment decision making. Advances in knowledge: LVI is considered to be an important initial step for tumor dissemination. CT radiomics features correlate with LVI in ESCC and can be used as potential biomarkers for predicting LVI in ESCC.

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OP12.04: Primary assessment of fetal ventricular volume and systolic function using real-time and full volume three-dimensional echocardiography

Ultrasound in Obstetrics and Gynecology ◽

10.1002/uog.8032 ◽

2010 ◽

Vol 36 (S1) ◽

pp. 85-86

Author(s):

Y. Yang

Keyword(s):

Real Time ◽

Systolic Function ◽

Three Dimensional ◽

Ventricular Volume ◽

Dimensional Echocardiography ◽

Three Dimensional Echocardiography ◽

Primary Assessment ◽

Full Volume

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Full Volume

Scottish Studies ◽

10.2218/ss.v37i0.1783 ◽

2017 ◽

Vol 37 (0) ◽

Author(s):

The Editor

Keyword(s):

Full Volume

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Deep learning based MRI contrast synthesis using full volume prediction

Biomedical Physics & Engineering Express ◽

10.1088/2057-1976/ac3c64 ◽

2021 ◽

Author(s):

Jose V Manjon ◽

Jose E Romero ◽

Pierrick Coupé

Keyword(s):

Three Dimensional ◽

Patient Comfort ◽

Segmentation Method ◽

Resonance Imaging ◽

Complementary Information ◽

Mri Contrast ◽

Ambiguity Reduction ◽

Magnetic Resonance Imaging Mri ◽

Time Limitations ◽

Full Volume

Abstract In Magnetic Resonance Imaging (MRI), depending on the image acquisition settings, a large number of image types or contrasts can be generated showing complementary information of the same imaged subject. This multi-spectral information is highly beneficial since can improve MRI analysis tasks such as segmentation and registration, thanks to pattern ambiguity reduction. However, the acquisition of several contrasts is not always possible due to time limitations and patient comfort constraints. Contrast synthesis has emerged recently as an approximate solution to generate other image types different from those acquired originally. Most of the previously proposed methods for contrast synthesis are slice-based which result in intensity inconsistencies between neighbor slices when applied in 3D. We propose the use of a 3D convolutional neural network (CNN) capable of generating T2 and FLAIR images from a single anatomical T1 source volume. The proposed network is a 3D variant of the UNet that processes the whole volume at once breaking with the inconsistency in the resulting output volumes related to 2D slice or patch-based methods. Since working with a full volume at once has a huge memory demand we have introduced a spatial-to-depth and a reconstruction layer that allows working with the full volume but maintain the required network complexity to solve the problem. Our approach enhances the coherence in the synthesized volume while improving the accuracy thanks to the integrated three-dimensional context-awareness. Finally, the proposed method has been validated with a segmentation method, thus demonstrating its usefulness in a direct and relevant application.

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