TY - GEN
T1 - InSpect: INtegrated SPECTral Component Estimation and Mapping for Multi-contrast Microstructural MRI
AU - Slator, Paddy J.
AU - Hutter, Jana
AU - Marinescu, Razvan V.
AU - Palombo, Marco
AU - Young, Alexandra L.
AU - Jackson, Laurence H.
AU - Ho, Alison
AU - Chappell, Lucy C.
AU - Rutherford, Mary
AU - Hajnal, Joseph V.
AU - Alexander, Daniel C.
PY - 2019/5/22
Y1 - 2019/5/22
N2 - We introduce a novel algorithm for deriving meaningful maps from multi-contrast MRI experiments. Such experiments enable the estimation of multidimensional correlation spectra, in domains such as T1-diffusivity, T2-diffusivity, or T1-T2. These spectra combine information from complementary MR properties, and therefore have the potential for improved quantification of distinct tissue types compared to single-contrast analyses. However, spectral estimation is an ill-conditioned problem which is highly sensitive to noise and requires significant regularisation. We propose an Expectation-Maximisation based method - which we term InSpect - for unified analysis of multi-contrast MR images. The algorithm simultaneously estimates canonical spectra associated with distinct tissue types within an image, and produces maps quantifying the spatial distribution of these spectra. We test the algorithm’s capabilities on simulated data, then apply to placental diffusion-relaxometry data. On placental data we identified significant within-organ and across-subject variation in T2*-ADC spectra - showing the potential of InSpect for detailed separation and quantification of distinct microstructural environments.
AB - We introduce a novel algorithm for deriving meaningful maps from multi-contrast MRI experiments. Such experiments enable the estimation of multidimensional correlation spectra, in domains such as T1-diffusivity, T2-diffusivity, or T1-T2. These spectra combine information from complementary MR properties, and therefore have the potential for improved quantification of distinct tissue types compared to single-contrast analyses. However, spectral estimation is an ill-conditioned problem which is highly sensitive to noise and requires significant regularisation. We propose an Expectation-Maximisation based method - which we term InSpect - for unified analysis of multi-contrast MR images. The algorithm simultaneously estimates canonical spectra associated with distinct tissue types within an image, and produces maps quantifying the spatial distribution of these spectra. We test the algorithm’s capabilities on simulated data, then apply to placental diffusion-relaxometry data. On placental data we identified significant within-organ and across-subject variation in T2*-ADC spectra - showing the potential of InSpect for detailed separation and quantification of distinct microstructural environments.
UR - http://www.scopus.com/inward/record.url?scp=85066157013&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-20351-1_59
DO - 10.1007/978-3-030-20351-1_59
M3 - Conference contribution
SN - 9783030203504
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 755
EP - 766
BT - Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings
A2 - Bao, Siqi
A2 - Gee, James C.
A2 - Yushkevich, Paul A.
A2 - Chung, Albert C.S.
ER -