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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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NIRViz: 3D visualization software for multimodality optical imaging using visualization toolkit (VTK) and insight segmentation toolkit (ITK).

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Image guided near-infrared spectroscopy of breast tissue in vivo using boundary element method.

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3-D Image-guided diffuse optical tomography using boundary element method and MPI implementation.

Subhadra Srinivasan1, Hamid Ghadyani

  • 1Thayer School of Engineering, Dartmouth College, 8000 Cummings Hall, Hanover, NH-03755, USA. subha@dartmouth.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

Boundary element methods enable 3D multi-modality near-infrared spectroscopy using surface data. This approach simplifies meshing for faster, more accurate imaging in applications like breast cancer detection.

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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Computational Modeling

Background:

  • Near-infrared spectroscopy (NIRS) is a non-invasive optical imaging technique.
  • Multi-modality imaging combines NIRS with other modalities like MRI or microCT for enhanced diagnostics.
  • Accurate modeling of light propagation in tissue is crucial for NIRS.

Purpose of the Study:

  • To present boundary element methods (BEM) for 3D image-guided multi-modality NIRS.
  • To address challenges in BEM for NIRS, including dense matrix solutions and complex domains.
  • To demonstrate the application of BEM-NIRS in specific biomedical imaging scenarios.

Main Methods:

  • Utilizing surface discretization for 3D NIRS with BEM.
  • Assuming piece-wise constant tissue domains with boundaries known from MRI or microCT.
  • Developing solutions for dense matrix inversion and handling heterogeneous tissue models.

Main Results:

  • BEM significantly simplifies the meshing process for NIRS.
  • The method offers speed-up and improved accuracy in forward solutions.
  • Demonstrated feasibility for breast cancer imaging and small-animal molecular imaging.

Conclusions:

  • Boundary element methods are a viable and attractive approach for 3D image-guided multi-modality NIRS.
  • The presented solutions overcome key computational challenges.
  • This technique holds promise for advancing diagnostic capabilities in oncology and preclinical research.