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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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FAST: framework for heterogeneous medical image computing and visualization.

Erik Smistad1,2, Mohammadmehdi Bozorgi3, Frank Lindseth3,4

  • 1Department of Computer and Information Science, Norwegian University of Science and Technology, Sem Saelandsvei 7-9, 7491, Trondheim, Norway. smistad@idi.ntnu.no.

International Journal of Computer Assisted Radiology and Surgery
|February 17, 2015
PubMed
Summary
This summary is machine-generated.

The novel FrAmework for heterogeneouS medical image compuTing and visualization (FAST) simplifies processing and visualization of medical images on diverse computer systems. It offers significant speedups compared to existing toolkits, enhancing efficiency for medical imaging tasks.

Keywords:
ComputingGPUHeterogeneousImageMedicalOpenCLParallelVisualization

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Area of Science:

  • Computer Science
  • Medical Imaging
  • High-Performance Computing

Background:

  • Modern computer systems are increasingly heterogeneous, featuring diverse processors like multi-core CPUs and GPUs.
  • Growing volumes of medical image data necessitate efficient utilization of these heterogeneous computational resources.
  • Existing methods for medical image processing on heterogeneous systems are often hindered by driver complexities, processor variations, and low-level memory management challenges.

Purpose of the Study:

  • To introduce a novel framework, FrAmework for heterogeneouS medical image compuTing and visualization (FAST), designed for efficient medical image computing and visualization on heterogeneous systems.
  • To simplify the process of simultaneous image processing and visualization on systems with multiple processor types.
  • To overcome the difficulties associated with low-level memory handling and processor differences in medical imaging.

Main Methods:

  • FAST employs common programming paradigms for image processing, abstracting away complex memory handling details.
  • The framework is designed to leverage all available processors and cores within a heterogeneous system.
  • FAST is developed as an open-source, cross-platform solution, accessible online.

Main Results:

  • Performance evaluations demonstrate the simplicity and efficiency of the FAST framework.
  • Comparisons with established toolkits like the Insight Toolkit (ITK) and Visualization Toolkit (VTK) show significant speedups.
  • FAST achieved up to a 20-fold increase in speed for several common medical imaging algorithms.

Conclusions:

  • The FAST framework effectively enables efficient medical image computing and visualization on heterogeneous computing environments.
  • Demonstrated ease of use and superior performance compared to existing frameworks like ITK and VTK.
  • FAST represents a significant advancement for processing and visualizing medical images in complex computational settings.