scholarly journals MR Angiography acceleration techniques with iterative reconstructions: the IT-TWIST sequence

2021 ◽  
Author(s):  
Curatolo Calogero
Keyword(s):  
Blood Vessels ◽  
Temporal Resolution ◽  
Iterative Reconstruction ◽  
Mr Angiography ◽  
Physical Characteristics ◽  
Time Resolved ◽  
Non Invasive ◽  
Acceleration Techniques ◽  
Reconstruction Time

In recent years MR Angiography allowed to assess blood vessels and flow in a non-invasive way. The aim of our work was to illunstrate a modern MR Angiography technique, the 4D Iterative reconstruction Time resolved angiography With Interleaved Stochastic Trajectories (IT-TWIST), capable to generate angiograms with high spatial and temporal resolution, providing multiple and continuous post-contrastographic sets of images. Technological and physical characteristics are reported in detail and consequent advantages are summarized.

Triple-TWIST MRA: high spatial and temporal resolution MR angiography of the entire peripheral vascular system using a time-resolved 4D MRA technique

2012 ◽  
Vol 23 (1) ◽  
pp. 298-306 ◽  
Author(s):  
Sonja Kinner ◽  
Harald H. Quick ◽  
Stefan Maderwald ◽  
Peter Hunold ◽  
Jörg Barkhausen ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Mr Angiography ◽  
Vascular System ◽  
Peripheral Vascular ◽  
Time Resolved

Time-resolved, contrast-enhanced 3D MR angiography in the subclavian steal effect

VASA ◽  
2010 ◽  
Vol 39 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Schubert
Keyword(s):  
Mr Angiography ◽  
Time Resolved ◽  
Time To Peak ◽  
Vertebral Arteries ◽  
Subclavian Steal ◽  
Cerebrovascular Events ◽  
Graphic Analysis ◽  
Contrast Enhanced ◽  
Hemodynamic Information ◽  
Supraaortic Vessels

The subclavian steal effect indicates atherosclerotic disease of the supraaortic vessels but rarely causes cerebrovascular events in itself. Noninvasive imaging providing detailed anatomic as well as hemodynamic information would therefore be desirable. From a group of 25 consecutive patients referred for MR angiography, four with absent or highly attenuated signal in one of the vertebral arteries on 3D multislab time-of-flight MR angiography were selected to undergo 3D time-resolved contrast-enhanced MR angiography. The time-resolved 3D contrast series (source images and MIPs) were evaluated visually and by graphic analysis of time-intensity curves derived from the respective V1 and V3 segments of both vertebral arteries on the source images. In two cases with high-grade proximal left subclavian stenosis, time-resolved 3D ce-MRA was able to visualise retrograde contrast filling of the left VA. There was a marked delay in time-to-peak between the left and right V1 segments in one case and a shallower slope of enhancement in another. In the other two cases, there was complete or collateralised segmental occlusion of the VAs.

Analog Circuit Failure Analysis Using Time-Resolved Emission

2005 ◽  
Author(s):  
B.J. Cain ◽  
G.L. Woods ◽  
A. Syed ◽  
R. Herlein ◽  
Toshihiro Nomura
Keyword(s):  
Analog Circuits ◽  
Integrated Circuit ◽  
Analog Circuit ◽  
Photon Emission ◽  
Nonlinear Process ◽  
Time Resolved ◽  
Non Invasive ◽  
Highly Nonlinear ◽  
Excellent Spatial Resolution ◽  
5 Ghz

Abstract Time-Resolved Emission (TRE) is a popular technique for non-invasive acquisition of time-domain waveforms from active nodes through the backside of an integrated circuit. [1] State-of-the art TRE systems offer high bandwidths (> 5 GHz), excellent spatial resolution (0.25um), and complete visibility of all nodes on the chip. TRE waveforms are typically used for detecting incorrect signal levels, race conditions, and/or timing faults with resolution of a few ps. However, extracting the exact voltage behavior from a TRE waveform is usually difficult because dynamic photon emission is a highly nonlinear process. This has limited the perceived utility of TRE in diagnosing analog circuits. In this paper, we demonstrate extraction of voltage waveforms in passing and failing conditions from a small-swing, differential logic circuit. The voltage waveforms obtained were crucial in corroborating a theory for some failures inside an 0.18um ASIC.

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

2020 ◽  
Vol 16 (4) ◽  
pp. 397-426
Author(s):  
Muhammad Nadeem Ashraf ◽  
Muhammad Hussain ◽  
Zulfiqar Habib
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Early Stage ◽  
Spherical Shape ◽  
Cost Effective ◽  
Lesion Detection ◽  
Diabetic Patients ◽  
Cad System ◽  
Non Invasive ◽  
Optic Discs

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

scholarly journals Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms

2021 ◽  
Author(s):  
Luuk J. Oostveen ◽  
Frederick J. A. Meijer ◽  
Frank de Lange ◽  
Ewoud J. Smit ◽  
Sjoert A. Pegge ◽  
...  
Keyword(s):  
Deep Learning ◽  
White Matter ◽  
Image Quality ◽  
Iterative Reconstruction ◽  
Quality Characteristics ◽  
Model Based ◽  
Hybrid Iterative Reconstruction ◽  
Contrast Ct ◽  
Lower Noise ◽  
Reconstruction Time

Abstract Objectives To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT). Methods Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests. Results For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively. Conclusions With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times. Key Points • Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.

scholarly journals A data reduction and compression description for high throughput time-resolved electron microscopy

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Abhik Datta ◽  
Kian Fong Ng ◽  
Deepan Balakrishnan ◽  
Melissa Ding ◽  
See Wee Chee ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Temporal Resolution ◽  
Data Reduction ◽  
Accurate Estimation ◽  
Data Streaming ◽  
Compression Scheme ◽  
Raw Data ◽  
Time Resolved ◽  
Detection And Counting ◽  
Spatio Temporal

AbstractFast, direct electron detectors have significantly improved the spatio-temporal resolution of electron microscopy movies. Preserving both spatial and temporal resolution in extended observations, however, requires storing prohibitively large amounts of data. Here, we describe an efficient and flexible data reduction and compression scheme (ReCoDe) that retains both spatial and temporal resolution by preserving individual electron events. Running ReCoDe on a workstation we demonstrate on-the-fly reduction and compression of raw data streaming off a detector at 3 GB/s, for hours of uninterrupted data collection. The output was 100-fold smaller than the raw data and saved directly onto network-attached storage drives over a 10 GbE connection. We discuss calibration techniques that support electron detection and counting (e.g., estimate electron backscattering rates, false positive rates, and data compressibility), and novel data analysis methods enabled by ReCoDe (e.g., recalibration of data post acquisition, and accurate estimation of coincidence loss).

scholarly journals Super-resolution time-resolved imaging using computational sensor fusion

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
C. Callenberg ◽  
A. Lyons ◽  
D. den Brok ◽  
A. Fatima ◽  
A. Turpin ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Single Photon ◽  
Super Resolution ◽  
Numerical Data ◽  
Data Cube ◽  
Fluorescence Lifetime Imaging ◽  
Time Resolved ◽  
Temporal Precision

AbstractImaging across both the full transverse spatial and temporal dimensions of a scene with high precision in all three coordinates is key to applications ranging from LIDAR to fluorescence lifetime imaging. However, compromises that sacrifice, for example, spatial resolution at the expense of temporal resolution are often required, in particular when the full 3-dimensional data cube is required in short acquisition times. We introduce a sensor fusion approach that combines data having low-spatial resolution but high temporal precision gathered with a single-photon-avalanche-diode (SPAD) array with data that has high spatial but no temporal resolution, such as that acquired with a standard CMOS camera. Our method, based on blurring the image on the SPAD array and computational sensor fusion, reconstructs time-resolved images at significantly higher spatial resolution than the SPAD input, upsampling numerical data by a factor $$12 \times 12$$ 12 × 12 , and demonstrating up to $$4 \times 4$$ 4 × 4 upsampling of experimental data. We demonstrate the technique for both LIDAR applications and FLIM of fluorescent cancer cells. This technique paves the way to high spatial resolution SPAD imaging or, equivalently, FLIM imaging with conventional microscopes at frame rates accelerated by more than an order of magnitude.

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