RESUMEN
Background: Quantification of in vivo chemical exchange saturation transfer (CEST) magnetic resonance signals is challenging due to contamination from coexisting effects, including the direct water effect and asymmetric magnetization transfer. Fitting-based analysis allows the calculation of multiple types of signals from the line shape of Z-spectra. However, the conventional voxelwise method has several drawbacks, including its long computation time and its susceptibility to image noise and Z-spectra oscillations, and it is difficult to determine the initial fitting parameters. Methods: Herein, we propose a K-means clustering method for accelerated Lorentzian estimation (KALE) in CEST quantification. Briefly, voxels in CEST images are clustered into K groups according to their Z-spectra characteristics. A 'groupwise' fitting process is then performed with preset initial values, yielding a set of fitted spectra and fitted parameters for each group. With the updated initial values, each group is further clustered into subgroups, and groupwise fitting is performed again. This hierarchical K-means clustering and parameter updating process continues until the pixel number or intensity error meets the termination criteria. Voxelwise fitting could be further conducted to improve the quantification images (termed voxel-K) by utilizing the previous groupwise KALE results as the initial values (termed group-K). Results: Incorporated with Lorentzian difference (LD) quantification, KALE was first optimized and evaluated on 5 healthy human brain datasets at 3 Tesla. Compared with traditional voxel-by-voxel LD quantification, the computation times of group-K and voxel-K were significantly reduced by ~85% and ~70%, respectively (P<0.001). Furthermore, the group-K images exhibited better denoising performance than traditional LD and voxel-K. KALE was further validated on six ischemic rat brains acquired at 7 Tesla, with both LD_group-K and LD_voxel-K displaying almost identical contrast maps with traditional voxelwise maps. When incorporated with the five-pool Lorentzian fitting (LF), KALE exhibited an improved contrast-to-noise ratio (CNR) for amplitude maps of each pool [P=0.003, 0.015, 0.047, and 0.047 for amide, nuclear Overhauser effect (NOE), magnetic transfer (MT) and guanidine amine, respectively] and improved fitting goodness (P=0.033). Conclusions: KALE quantification provides comparable or even superior contrast maps to traditional voxelwise fitting, with significantly reduced computation time. The 'smart' and hierarchical voxel-clustering and parameter updating process of KALE may facilitate more preclinical and clinical CEST applications.
RESUMEN
PURPOSE: Design an efficient CEST scheme for exchange-dependent images with high contrast-to-noise ratio. THEORY: Reassembled saturation transfer (REST) signals were defined as Δ $$ \Delta $$ r.Z = r.Zref - r.ZCEST and the reassembled exchange-dependen magnetization transfer ratio r.MTRRex = r.1/Zref - r.1/ZCEST , utilizing the averages over loosely sampled reference frequency offsets as Zref and over densely sampled target offsets as ZCEST . Using r.MTRRex measured under 2 B1,sat values, exchange rate could be estimated. METHODS: The REST approach was optimized and assessed quantitatively by simulations for various exchange rates, pool concentration, and water T1 . In vivo evaluation was performed on ischemic rat brains at 7 Tesla and human brains at 3 Tesla, in comparison with conventional asymmetrical analysis, Lorentzian difference (LD), an MTRRex_ LD. RESULTS: For a broad choice of Δ ω ref $$ \Delta {\omega}_{ref} $$ ranges and numbers, Δr.Z and r.MTRRex exhibited comparable quantification features with conventional LD and MTRRex _LD, respectively, when B1,sat ≤ 1 µT. The subtraction of 2 REST values under distinct B1,sat values showed linear relationships with exchange rate and obtained immunity to field inhomogeneity and variation in MT and water T1 . For both rat and human studies, REST images exhibited similar contrast distribution to MTRRex _LD, with superiority in contrast-to-noise ratio and acquisition efficiency. Compared with MTRRex _LD, 2-B1,sat subtraction REST images displayed better resistance to B1 inhomogeneity, with more specific enhanced regions. They also showed higher signals for amide than for nuclear Overhauser enhancement effect in human brain, presumably reflecting the higher increment from faster-exchanging species as B1,sat increased. CONCLUSION: Featuring high contrast-to-noise ratio efficiency, REST could be a practical exchange-dependent approach readily applicable to either retrospective Z-spectra analysis or perspective 6-offset acquisition.