TY - GEN
T1 - Estimating Neural Orientation Distribution Fields on High Resolution Diffusion MRI Scans
AU - Dwedari, Mohammed Munzer
AU - Consagra, William
AU - Müller, Philip
AU - Turgut, Özgün
AU - Rueckert, Daniel
AU - Rathi, Yogesh
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - The Orientation Distribution Function (ODF) characterizes key brain microstructural properties and plays an important role in understanding brain structural connectivity. Recent works introduced Implicit Neural Representation (INR) based approaches to form a spatially aware continuous estimate of the ODF field and demonstrated promising results in key tasks of interest when compared to conventional discrete approaches. However, traditional INR methods face difficulties when scaling to large-scale images, such as modern ultra-high-resolution MRI scans, posing challenges in learning fine structures as well as inefficiencies in training and inference speed. In this work, we propose HashEnc, a grid-hash-encoding-based estimation of the ODF field and demonstrate its effectiveness in retaining structural and textural features. We show that HashEnc achieves a 10 % enhancement in image quality while requiring 3× less computational resources than current methods. Our code can be found at https://github.com/MunzerDw/NODF-HashEnc.
AB - The Orientation Distribution Function (ODF) characterizes key brain microstructural properties and plays an important role in understanding brain structural connectivity. Recent works introduced Implicit Neural Representation (INR) based approaches to form a spatially aware continuous estimate of the ODF field and demonstrated promising results in key tasks of interest when compared to conventional discrete approaches. However, traditional INR methods face difficulties when scaling to large-scale images, such as modern ultra-high-resolution MRI scans, posing challenges in learning fine structures as well as inefficiencies in training and inference speed. In this work, we propose HashEnc, a grid-hash-encoding-based estimation of the ODF field and demonstrate its effectiveness in retaining structural and textural features. We show that HashEnc achieves a 10 % enhancement in image quality while requiring 3× less computational resources than current methods. Our code can be found at https://github.com/MunzerDw/NODF-HashEnc.
KW - Diffusion MRI
KW - Implicit Neural Representation
KW - Orientation Distribution Function
UR - http://www.scopus.com/inward/record.url?scp=85212490883&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-72104-5_30
DO - 10.1007/978-3-031-72104-5_30
M3 - Conference contribution
AN - SCOPUS:85212490883
SN - 9783031721038
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 307
EP - 317
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings
A2 - Linguraru, Marius George
A2 - Dou, Qi
A2 - Feragen, Aasa
A2 - Giannarou, Stamatia
A2 - Glocker, Ben
A2 - Lekadir, Karim
A2 - Schnabel, Julia A.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
Y2 - 6 October 2024 through 10 October 2024
ER -