Application of a novel T1 retrospective quantification using internal references (T1‐REQUIRE ) algorithm to derive quantitative T1 relaxation maps of the brain
Article Swipe
Adam Hasse
,
Julian Bertini
,
Sean Foxley
,
Yong Jeong
,
Adil Javed
,
Timothy J. Carroll
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/ima.22768
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/ima.22768
Most MRI sequences used clinically are qualitative or weighted. While such images provide useful information for clinicians to diagnose and monitor disease progression, they lack the ability to quantify tissue damage for more objective assessment. In this study, an algorithm referred to as the T1‐REQUIRE is presented as a proof‐of‐concept which uses nonlinear transformations to retrospectively estimate T1 relaxation times in the brain using T1‐weighted MRIs, the appropriate signal equation, and internal, healthy tissues as references. T1‐REQUIRE was applied to two T1‐weighted MR sequences, a spin‐echo and a MPRAGE, and validated with a reference standard T1 mapping algorithm in vivo. In addition, a multiscanner study was run using MPRAGE images to determine the effectiveness of T1‐REQUIRE in conforming the data from different scanners into a more uniform way of analyzing T1‐relaxation maps. The T1‐REQUIRE algorithm shows good agreement with the reference standard (Lin's concordance correlation coefficients of 0.884 for the spin‐echo and 0.838 for the MPRAGE) and with each other (Lin's concordance correlation coefficient of 0.887). The interscanner studies showed improved alignment of cumulative distribution functions after T1‐REQUIRE was performed. T1‐REQUIRE was validated with a reference standard and shown to be an effective estimate of T1 over a clinically relevant range of T1 values. In addition, T1‐REQUIRE showed excellent data conformity across different scanners, providing evidence that T1‐REQUIRE could be a useful addition to big data pipelines.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/ima.22768
- OA Status
- hybrid
- Cited By
- 4
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4282577842
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4282577842Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/ima.22768Digital Object Identifier
- Title
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Application of a novel
T1 retrospective quantification using internal references (T1‐REQUIRE ) algorithm to derive quantitativeT1 relaxation maps of the brainWork title - Type
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articleOpenAlex work type
- Language
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enPrimary language
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2022Year of publication
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2022-06-13Full publication date if available
- Authors
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Adam Hasse, Julian Bertini, Sean Foxley, Yong Jeong, Adil Javed, Timothy J. CarrollList of authors in order
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https://doi.org/10.1002/ima.22768Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1002/ima.22768Direct OA link when available
- Concepts
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Algorithm, Concordance, Relaxation (psychology), Concordance correlation coefficient, Computer science, Correlation, Mathematics, Medicine, Statistics, Internal medicine, GeometryTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 2, 2024: 1, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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