TY - GEN
T1 - Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction
AU - Dzyubachyk, Oleh
AU - Koolstra, Kirsten
AU - Pezzotti, Nicola
AU - Lelieveldt, Boudewijn
AU - Webb, Andrew
AU - Börnert, Peter
PY - 2019
Y1 - 2019
N2 - Quality assessment of different Magnetic Resonance Fingerprinting (MRF) sequences and their corresponding dictionaries remains an unsolved problem. In this work we present a method in which we approach analysis of MRF dictionaries by performing dimensionality reduction and representing them as low-dimensional point sets (embeddings). Dimensionality reduction was performed using a modification of the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. First, we demonstrated stability of calculated embeddings that allows neglecting the stochastic nature of t-SNE. Next, we proposed and analyzed two algorithms for comparing the embeddings. Finally, we performed two simulations in which we reduced the MRF sequence/dictionary in length or size and analyzed the influence of this reduction on the resulting embedding. We believe that this research can pave the way to development of a software tool for analysis, including better understanding, optimization and comparison, of different MRF sequences.
AB - Quality assessment of different Magnetic Resonance Fingerprinting (MRF) sequences and their corresponding dictionaries remains an unsolved problem. In this work we present a method in which we approach analysis of MRF dictionaries by performing dimensionality reduction and representing them as low-dimensional point sets (embeddings). Dimensionality reduction was performed using a modification of the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. First, we demonstrated stability of calculated embeddings that allows neglecting the stochastic nature of t-SNE. Next, we proposed and analyzed two algorithms for comparing the embeddings. Finally, we performed two simulations in which we reduced the MRF sequence/dictionary in length or size and analyzed the influence of this reduction on the resulting embedding. We believe that this research can pave the way to development of a software tool for analysis, including better understanding, optimization and comparison, of different MRF sequences.
KW - Dimensionality reduction
KW - Magnetic Resonance Fingerprinting (MRF)
KW - Point cloud registration
KW - t-SNE
UR - http://www.scopus.com/inward/record.url?scp=85076318885&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-35817-4_6
DO - 10.1007/978-3-030-35817-4_6
M3 - Conference contribution
AN - SCOPUS:85076318885
SN - 978-3-030-35816-7
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 44
EP - 52
BT - Graph Learning in Medical Imaging
A2 - Zhang, Daoqiang
A2 - Zhou, Luping
A2 - Jie, Biao
A2 - Liu, Mingxia
PB - Springer
CY - Cham
T2 - 1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 17 October 2019 through 17 October 2019
ER -