Aravind R Krishnan
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Explore the profile of Aravind R Krishnan including associated specialties, affiliations and a list of published articles.
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10
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2
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Recent Articles
1.
Saunders A, Kim M, Gao C, Remedios L, Krishnan A, Schilling K, et al.
Magn Reson Imaging
. 2025 Jan;
117:110322.
PMID: 39756665
While typical qualitative T1-weighted magnetic resonance images reflect scanner and protocol differences, quantitative T1 mapping aims to measure T1 independent of these effects. Changes in T1 in the brain reflect...
2.
Xu H, Newlin N, Kim M, Gao C, Kanakaraj P, Krishnan A, et al.
Proc SPIE Int Soc Opt Eng
. 2024 Sep;
12926.
PMID: 39310215
Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis...
3.
Krishnan A, Xu K, Li T, Gao C, Remedios L, Kanakaraj P, et al.
Proc SPIE Int Soc Opt Eng
. 2024 Sep;
12926.
PMID: 39268356
The reconstruction kernel in computed tomography (CT) generation determines the texture of the image. Consistency in reconstruction kernels is important as the underlying CT texture can impact measurements during quantitative...
4.
Gao C, Yang Q, Kim M, Khairi N, Cai L, Newlin N, et al.
J Med Imaging (Bellingham)
. 2024 Aug;
11(4):044007.
PMID: 39185477
Purpose: As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI)...
5.
McMaster E, Newlin N, Rudravaram G, Saunders A, Krishnan A, Remedios L, et al.
ArXiv
. 2024 Aug;
PMID: 39130197
To date, there has been no comprehensive study characterizing the effect of diffusion-weighted magnetic resonance imaging voxel resolution on the resulting connectome for high resolution subject data. Similarity in results...
6.
Li T, Still J, Xu K, Lee H, Cai L, Krishnan A, et al.
Med Image Comput Comput Assist Interv
. 2024 May;
14221:649-659.
PMID: 38779102
The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by incorporating repeat imaging and medical context, such as electronic health records (EHRs). However, clinically...
7.
Krishnan A, Xu K, Li T, Remedios L, Sandler K, Maldonado F, et al.
Med Phys
. 2024 Mar;
51(8):5510-5523.
PMID: 38530135
Background: The kernel used in CT image reconstruction is an important factor that determines the texture of the CT image. Consistency of reconstruction kernel choice is important for quantitative CT-based...
8.
Xu H, Newlin N, Kim M, Gao C, Kanakaraj P, Krishnan A, et al.
ArXiv
. 2024 Feb;
PMID: 38344221
Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis...
9.
Xu K, Li T, Terry J, Krishnan A, Deppen S, Huo Y, et al.
medRxiv
. 2023 Dec;
PMID: 38106099
Rationale: Skeletal muscle fat infiltration progresses with aging and is worsened among individuals with a history of cigarette smoking. Many negative impacts of smoking on muscles are likely reversible with...
10.
Gao C, Yang Q, Kim M, Khairi N, Cai L, Newlin N, et al.
medRxiv
. 2023 Sep;
PMID: 37662348
Purpose: As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI)...