Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Practical considerations for 3-D image reconstruction using spherically symmetric volume elements.

S Matej1, R M Lewitt

  • 1Med. Image Process. Group, Pennsylvania Univ., Philadelphia, PA, USA.

IEEE Transactions on Medical Imaging
|January 1, 1996
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

System models for PET statistical iterative reconstruction: A review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2016
Same author

GPU-Accelerated Forward and Back-Projections with Spatially Varying Kernels for 3D DIRECT TOF PET Reconstruction.

IEEE transactions on nuclear science·2013
Same author

2.5-D simultaneous multislice reconstruction by series expansion methods from Fourier-rebinned PET data.

IEEE transactions on medical imaging·2000
Same author

Performance of the Fourier rebinning algorithm for PET with large acceptance angles.

Physics in medicine and biology·1998
Same author

Three-dimensional imaging characteristics of the HEAD PENN-PET scanner.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·1997
Same author

Relevance of statistically significant differences between reconstruction algorithms.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·1996
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
See all related articles

This study introduces blobs as an alternative to voxels for computer image reconstruction. Blobs improve image quality and reduce noise in iterative reconstruction methods without sacrificing resolution.

Area of Science:

  • Medical imaging
  • Computer vision
  • Image processing

Background:

  • Conventional volume imaging relies on voxels, which have limitations in controlling image characteristics.
  • Iterative reconstruction methods are crucial for generating high-quality images from projection data.

Purpose of the Study:

  • To explore spherically symmetric volume elements (blobs) as an alternative to voxels for computer volume image construction.
  • To investigate the impact of blob parameters on image characteristics and reconstruction performance.

Main Methods:

  • Developed and implemented algorithms for projection and discrete back-projection operations using blobs.
  • Investigated the relationship between blob parameters and image properties.
  • Compared blob-based reconstruction with voxel-based methods using visual and quantitative metrics.

Related Experiment Videos

Main Results:

  • Blob-based methods offer enhanced control over image shape and characteristics.
  • Efficient algorithms were designed for blob-based projection and back-projection.
  • Substantial improvements in reconstruction performance were observed compared to voxels.
  • Reconstructed images exhibited reduced noise (with and without noisy data) and maintained image resolution.

Conclusions:

  • Blobs represent a significant advancement over voxels for volume image reconstruction.
  • Appropriate selection of blob parameters leads to superior image quality and noise reduction.
  • Blob-based iterative reconstruction methods offer a promising approach for enhanced medical imaging and computer vision applications.