Comparison of deep learning reconstruction and adaptive statistical iterative reconstruction for head CT in acute stroke

  • 0Department of Medical Imaging, St. Michael's Hospital, Toronto, Canada.

|

Summary

No abstract available on PubMed

Related Concept Videos

Porcine As a Training Module for Head and Neck Microvascular Reconstruction 07:43

8.1K

Here we present a protocol for the use of the pig superior epigastric artery perforator flap as a learning module for head and neck microvascular...

Structure of HIV-1 Capsid Assemblies by Cryo-electron Microscopy and Iterative Helical Real-space Reconstruction 12:38

17.8K

This article describes a method to obtain a three-dimensional (3D) structure of helically assembled molecules using cryo-electron microscopy. In this protocol, we use HIV-1 capsid assemblies to illustrate the detailed 3D reconstruction procedure for achieving a density map by the iterative helical real-space reconstruction...

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach 08:01

9.4K

Methods for designing a computer-aided design/computer-aided manufacturing (CAD/CAM) surgical guide are shown. Cutting planes are separated, united, and thickened to easily visualize the necessary bone transfer. These designs can be three-dimensional printed and checked for...

Three-Dimensional Reconstruction of Orbital Fractures 08:18

636

Personalized medicine for orbital reconstruction is developing rapidly. Due to the delicate nature of the orbit, small discrepancies following fracture reconstruction may cause impairment in visual perception. Here, we describe three methods for virtual 3D reconstruction of orbital defects and their indications and potential pitfalls for correct reconstruction.

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible 10:42

6.9K

Here, we propose a practical, feasible and reproducible evaluation guideline for computer-assisted reconstruction of the mandible in order to create uniformity between studies regarding postoperative accuracy evaluation. This protocol continues and specifies an earlier publication of this evaluation...

Visual Statistical Learning 06:02

7.8K

Source: Laboratory of Jonathan Flombaum—Johns Hopkins University

The visual environment contains massive amounts of information involving the relations between objects in space and time; certain objects are more likely to appear in the vicinity of other objects. Learning these regularities can support a wide array of visual processing, including object recognition. Unsurprisingly, then, humans appear to learn these regularities automatically, quickly, and without conscious awareness. The...