Varatharajan Nainamalai
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Explore the profile of Varatharajan Nainamalai including associated specialties, affiliations and a list of published articles.
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Recent Articles
1.
Rezaeitaleshmahalleh M, Lyu Z, Mu N, Nainamalai V, Tang J, Gemmete J, et al.
Ann Biomed Eng
. 2025 Feb;
PMID: 39904865
This study uses a spatial pattern analysis of time-resolved aneurysmal velocity fields to enhance the characterization of intracranial aneurysms' (IA) rupture status. We name this technique temporal velocity-informatics (TVI). In...
2.
Lippert M, Dumont K, Birkeland S, Nainamalai V, Solvin H, Suther K, et al.
Eur Heart J Digit Health
. 2024 Nov;
5(6):725-734.
PMID: 39563912
Aims: New three-dimensional cardiac visualization technologies are increasingly employed for anatomic digital twins in pre-operative planning. However, the role and influence of extended reality (virtual, augmented, or mixed) within heart...
3.
Nainamalai V, Qair H, Pelanis E, Jenssen H, Fretland A, Edwin B, et al.
Eur J Radiol Open
. 2024 Jul;
13:100582.
PMID: 39041057
Objective: Routinely collected electronic health records using artificial intelligence (AI)-based systems bring out enormous benefits for patients, healthcare centers, and its industries. Artificial intelligence models can be used to structure...
4.
Zhang X, Gosnell J, Nainamalai V, Page S, Huang S, Haw M, et al.
Diagnostics (Basel)
. 2023 Sep;
13(18).
PMID: 37761348
Percutaneous interventions are gaining rapid acceptance in cardiology and revolutionizing the treatment of structural heart disease (SHD). As new percutaneous procedures of SHD are being developed, their associated complexity and...
5.
Kulseng C, Nainamalai V, Grovik E, Geitung J, Aroen A, Gjesdal K
BMC Musculoskelet Disord
. 2023 Jan;
24(1):41.
PMID: 36650496
Background: To study deep learning segmentation of knee anatomy with 13 anatomical classes by using a magnetic resonance (MR) protocol of four three-dimensional (3D) pulse sequences, and evaluate possible clinical...
6.
Nainamalai V, Prasad P, Pelanis E, Edwin B, Albregtsen F, Elle O, et al.
Eur J Radiol Open
. 2022 Nov;
9:100448.
PMID: 36386761
Purpose: Automated algorithms for liver parenchyma segmentation can be used to create patient-specific models (PSM) that assist clinicians in surgery planning. In this work, we analyze the clinical applicability of...