Evidence map›Paper›PMID 35754205›Full record

ArticleMedical physics2022

A minimum SNR criterion for computed tomography object detection in the projection domain.

Scott S Hsieh, Shuai Leng, Lifeng Yu, Nathan R Huber, Cynthia H McCollough

Abstract read
In one paragraph

Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Article
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  19. A dense search challenge phantom fabricated with pixel-based 3D printing for precise detectability assessment.Proceedings of SPIE--the International Society for Optical Engineering · 2023
    Article
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Scott S HsiehDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Shuai LengDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Lifeng YuDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Nathan R HuberDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Cynthia H McColloughDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.

Funding

Trade-offs in human observer performance, image quality metrics, and patient doseR01EB017095 · NIBIB · MAYO CLINIC ROCHESTER · PI Hao Gong, Cynthia H McCollough · 2012 to 2026
$7.0M
NIBIB NIH HHS R01 EB017095
6 · The paper itself

Abstract

backgroundA common rule of thumb for object detection is the Rose criterion, which states that a signal must be five standard deviations above background to be detectable to a human observer. The validity of the Rose criterion in CT imaging is limited due to the presence of correlated noise. Recent reconstruction and denoising methodologies are also able to restore apparent image quality in very noisy conditions, and the ultimate limits of these methodologies are not yet known. PURPOSE: To establish a lower bound on the minimum achievable signal-to-noise ratio (SNR) for object detection, below which detection performance is poor regardless of reconstruction or denoising methodology.

methodsWe consider a numerical observer that operates on projection data and has perfect knowledge of the background and the objects to be detected, and determine the minimum projection SNR that is necessary to achieve predetermined lesion-level sensitivity and case-level specificity targets. We define a set of discrete signal objects

resultsWhen

conclusionsEven with perfect knowledge of the background and target objects, the ideal observer still requires an SNR of approximately 5. This is a lower bound on the SNR that would be required in real conditions, where the background and target objects are not known perfectly. Algorithms that denoise lesions with less than 5 projection SNR, regardless of the denoising methodology, are expected to show vanishing effects or false positive lesions.

Indexed as

AlgorithmsTomography, X-Ray ComputedHumansImage Processing, Computer-AssistedMonte Carlo MethodPhantoms, ImagingRadiation DosageSignal-To-Noise RatioIM-CT image reconstructionIM-CT radiation dosimetry and riskIM-CT theory

Identifiers

PMID35754205
PMCPMC9446706

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.