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Published on: September 25, 2016
Artifacts associated with MR neuroangiography
Abstract:
Neurovascular MR angiography (MRA) is rapidly gaining greater clinical acceptance. To provide functional information, novel techniques of acquisition, information processing, and display are used, generating a new set of artifacts. The purpose of this paper is to outline the causes, provide examples, and note clinical problems associated with MRA artifacts by grouping them into six common types: 1) poor visualization of small vessels, 2) overestimation of stenosis, 3) view-to-view variations, 4) false positives, 5) false negatives, and 6) vessel overlap. This in turn will lead to four generalized solutions: 1) optimize acquisition parameters, 2) edit volume boundaries before performing maximum intensity projection reconstructions, 3) refer to the individual source images, and 4) use alternative image processing. By organizing and simplifying both clinical problems and solutions into major categories, a greater understanding of the current clinical indications and the overall goals of MRA can be achieved.
Insights
Neurovascular MR angiography (MRA) artifacts can hinder diagnosis. This paper categorizes common MRA artifacts and offers solutions to improve diagnostic accuracy and clinical utility.
Area of Science:
- Radiology
- Medical Imaging
Background:
- Neurovascular MR angiography (MRA) is increasingly used clinically.
- Novel MRA techniques introduce new artifacts affecting image interpretation.
Purpose of the Study:
- To categorize common MRA artifacts.
- To provide examples and clinical problems associated with these artifacts.
- To propose generalized solutions for artifact mitigation.
Main Methods:
- Classification of MRA artifacts into six types: poor visualization, stenosis overestimation, view-to-view variations, false positives, false negatives, and vessel overlap.
- Identification of four generalized solutions: optimizing acquisition parameters, editing volume boundaries, reviewing source images, and employing alternative image processing.
Main Results:
- Detailed examples and clinical implications of each artifact type are presented.
- Proposed solutions aim to address the identified artifact categories.
Conclusions:
- Understanding and categorizing MRA artifacts is crucial for accurate interpretation.
- Implementing proposed solutions can enhance the clinical utility and diagnostic reliability of MRA.
