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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
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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Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
07:13

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Published on: October 27, 2023

Uncertainty driven probabilistic voxel selection for image registration.

Boris N Oreshkin, Tal Arbel

    IEEE Transactions on Medical Imaging
    |May 28, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new probabilistic method for selecting voxels in medical image registration, improving accuracy and reducing failure rates even with minimal data. This approach balances exploration and informative sampling for time-sensitive applications.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Biomedical Engineering

    Background:

    • Medical image registration is crucial for diagnosis and treatment planning.
    • Time-sensitive applications require efficient registration with limited computational resources.
    • Aggressive voxel sampling presents challenges in maintaining accuracy and reliability.

    Purpose of the Study:

    • To develop a novel probabilistic voxel selection strategy for accurate medical image registration in time-sensitive scenarios.
    • To enable aggressive voxel sampling (less than 1% of total voxels) while ensuring high accuracy and low failure rates.
    • To manage the trade-off between random and fixed voxel selection methods.

    Main Methods:

    • A Bayesian framework was developed to build a voxel sampling probability field (VSPF).
    • The VSPF is based on the uncertainty of transformation parameters.
    • A multi-scale registration algorithm iteratively samples voxel subsets guided by the VSPF.

    Main Results:

    • The probabilistic sampling scheme effectively maximizes registration accuracy.
    • The method avoids commitment to a fixed subset of voxels, allowing dynamic selection.
    • It balances the exploratory benefits of random sampling with the informativeness of fixed sampling.

    Conclusions:

    • The proposed probabilistic voxel selection strategy offers a robust solution for time-sensitive medical image registration.
    • This method enhances registration accuracy and reliability under aggressive downsampling.
    • It provides a flexible and effective approach for optimizing voxel selection in medical imaging.