Hyochul Lee1, Hyeong Hun Lee1, Hyeonjin Kim1,2. 1. Department of Biomedical Sciences, Seoul National University, Seoul, Korea. 2. Department of Radiology, Seoul National University Hospital, Seoul, Korea.
Abstract
PURPOSE: To explore the applicability of convolutional neural networks (CNNs) in the reconstruction of spectra from truncated FIDs (tFIDs) in 1 H-MRS, which can be valuable in situations in which data sampling is highly limited, such as spectroscopic magnetic resonance fingerprinting. METHODS: Rat brain FIDs were simulated at 9.4 T based on in vivo data (N = 11) and randomly truncated by retaining 8, 16, 32, 64, 128, 256, 512, and 1024 (null truncation) points (denoted as tFID8 , tFID16 , … tFID1024 ). Using a U-net, 3 CNNs were individually trained (N = 40 000) in time domain only (FID to FID [FID CNNFID ]), in frequency domain only (spectrum to spectrum [spec CNNspec ]), and across the domains (FID to spectrum [FID CNNspec ]) to map the truncated data to their fully sampled versions. The CNNs were tested on the simulated data (N = 5000), and the CNN with the best performance was further tested on the in vivo data, for which the CNN-predicted fully sampled data were analyzed using the LCModel and the results were compared with those from the original, fully sampled data. RESULTS: The best result on the simulated data was obtained with spec CNNspec , which effectively recovered the spectral details even for those input spectra that appear as a hump due to substantial FID truncation (spectra from tFID16 and tFID32 ). Overall, its performance was significantly degraded on the in vivo data. Nonetheless, using spec CNNspec , several coupled spins in addition to the major singlets can be quantified from tFID128 with the error no larger than 10%. CONCLUSION: Upon the availability of more realistically simulated training data, CNNs can also be used in the reconstruction of spectra from truncated FIDs.
PURPOSE: To explore the applicability of convolutional neural networks (CNNs) in the reconstruction of spectra from truncated FIDs (tFIDs) in 1 H-MRS, which can be valuable in situations in which data sampling is highly limited, such as spectroscopic magnetic resonance fingerprinting. METHODS: Rat brain FIDs were simulated at 9.4 T based on in vivo data (N = 11) and randomly truncated by retaining 8, 16, 32, 64, 128, 256, 512, and 1024 (null truncation) points (denoted as tFID8 , tFID16 , … tFID1024 ). Using a U-net, 3 CNNs were individually trained (N = 40 000) in time domain only (FID to FID [FID CNNFID ]), in frequency domain only (spectrum to spectrum [spec CNNspec ]), and across the domains (FID to spectrum [FID CNNspec ]) to map the truncated data to their fully sampled versions. The CNNs were tested on the simulated data (N = 5000), and the CNN with the best performance was further tested on the in vivo data, for which the CNN-predicted fully sampled data were analyzed using the LCModel and the results were compared with those from the original, fully sampled data. RESULTS: The best result on the simulated data was obtained with spec CNNspec , which effectively recovered the spectral details even for those input spectra that appear as a hump due to substantial FID truncation (spectra from tFID16 and tFID32 ). Overall, its performance was significantly degraded on the in vivo data. Nonetheless, using spec CNNspec , several coupled spins in addition to the major singlets can be quantified from tFID128 with the error no larger than 10%. CONCLUSION: Upon the availability of more realistically simulated training data, CNNs can also be used in the reconstruction of spectra from truncated FIDs.
Authors: Peter C M van Zijl; Kevin Brindle; Hanzhang Lu; Peter B Barker; Richard Edden; Nirbhay Yadav; Linda Knutsson Journal: Curr Opin Chem Biol Date: 2021-07-20 Impact factor: 8.972
Authors: Sofie Tapper; Mark Mikkelsen; Blake E Dewey; Helge J Zöllner; Steve C N Hui; Georg Oeltzschner; Richard A E Edden Journal: Magn Reson Med Date: 2020-11-18 Impact factor: 4.668