Reading raw data and plotting
Load the spant package:
Get the path to a data file included with spant:
fname <- system.file("extdata", "philips_spar_sdat_WS.SDAT", package = "spant")
Read the file and save to the workspace as mrs_data:
mrs_data <- read_mrs(fname, format = "spar_sdat")
Output some basic information about the data:
print(mrs_data)
#> MRS Data Parameters
#> ----------------------------------
#> Trans. freq (MHz) : 127.7861
#> FID data points : 1024
#> X,Y,Z dimensions : 1x1x1
#> Dynamics : 1
#> Coils : 1
#> Voxel resolution (mm) : 20x20x20
#> Sampling frequency (Hz) : 2000
#> Reference freq. (ppm) : 4.65
#> Nucleus : 1H
#> Spectral domain : FALSE
Plot the spectral region between 5 and 0.5 ppm:
plot(mrs_data, xlim = c(5, 0.5))
Basic preprocessing
Apply a HSVD filter to the residual water region and align the spectrum to the tNAA resonance at 2.01 ppm:
mrs_proc <- hsvd_filt(mrs_data)
mrs_proc <- align(mrs_proc, 2.01)
plot(mrs_proc, xlim = c(5, 0.5))
Basis simulation
Simulate a typical basis set for short TE brain analysis, print some basic information and plot:
basis <- sim_basis_1h_brain_press(mrs_proc)
print(basis)
#> Basis set parameters
#> -------------------------------
#> Trans. freq (MHz) : 127.786142
#> Data points : 1024
#> Sampling frequency (Hz) : 2000
#> Elements : 27
#>
#> Names
#> -------------------------------
#> -CrCH2,Ala,Asp,Cr,GABA,Glc,Gln,
#> GSH,Glu,GPC,Ins,Lac,Lip09,
#> Lip13a,Lip13b,Lip20,MM09,MM12,
#> MM14,MM17,MM20,NAA,NAAG,PCh,
#> PCr,sIns,Tau
stackplot(basis, xlim = c(4, 0.5), labels = basis$names, y_offset = 5)
Perform ABfit analysis of the processed data (mrs_proc):
fit_res <- fit_mrs(mrs_proc, basis)
Plot the fit result:
Extract the estimated amplitudes from fit_res and print as a ratio to total-creatine in column format:
amps <- fit_amps(fit_res)
print(t(amps / amps$tCr))
#> [,1]
#> X.CrCH2 0.00000000
#> Ala 0.15614827
#> Asp 0.54809326
#> Cr 0.66327583
#> GABA 0.28180988
#> Glc 0.06746507
#> Gln 0.07452946
#> GSH 0.35689231
#> Glu 1.10359839
#> GPC 0.26459013
#> Ins 0.99276784
#> Lac 0.09734879
#> Lip09 0.37880127
#> Lip13a 0.04545685
#> Lip13b 0.00000000
#> Lip20 0.00000000
#> MM09 0.17194515
#> MM12 0.11370654
#> MM14 0.44454033
#> MM17 0.42554626
#> MM20 1.53642870
#> NAA 0.97179789
#> NAAG 0.27434657
#> PCh 0.00000000
#> PCr 0.33672417
#> sIns 0.10883945
#> Tau 0.00000000
#> tNAA 1.24614446
#> tCr 1.00000000
#> tCho 0.26459013
#> Glx 1.17812785
#> tLM09 0.55074642
#> tLM13 0.60370372
#> tLM20 1.53642870
Unscaled amplitudes, CRLB error estimates and other fitting diagnostics, such as SNR, are given in the results table:
fit_res$res_tab
#> X Y Z Dynamic Coil X.CrCH2 Ala Asp Cr
#> 1 1 1 1 1 1 0 9.497403e-06 3.333667e-05 4.034241e-05
#> GABA Glc Gln GSH Glu GPC
#> 1 1.714052e-05 4.103427e-06 4.533104e-06 2.170725e-05 6.712414e-05 1.609316e-05
#> Ins Lac Lip09 Lip13a Lip13b Lip20 MM09
#> 1 6.03831e-05 5.921044e-06 2.303982e-05 2.764821e-06 0 0 1.045822e-05
#> MM12 MM14 MM17 MM20 NAA NAAG
#> 1 6.915971e-06 2.703827e-05 2.588299e-05 9.345017e-05 5.910764e-05 1.668658e-05
#> PCh PCr sIns Tau tNAA tCr tCho
#> 1 0 2.048057e-05 6.61994e-06 0 7.579422e-05 6.082298e-05 1.609316e-05
#> Glx tLM09 tLM13 tLM20 X.CrCH2.sd Ala.sd
#> 1 7.165725e-05 3.349804e-05 3.671906e-05 9.345017e-05 2.353862e-06 4.359105e-06
#> Asp.sd Cr.sd GABA.sd Glc.sd Gln.sd GSH.sd
#> 1 8.967175e-06 3.769567e-06 4.496036e-06 4.330375e-06 4.891686e-06 2.019976e-06
#> Glu.sd GPC.sd Ins.sd Lac.sd Lip09.sd Lip13a.sd
#> 1 4.91104e-06 2.340561e-06 2.023096e-06 5.343971e-06 4.067371e-06 1.338191e-05
#> Lip13b.sd Lip20.sd MM09.sd MM12.sd MM14.sd MM17.sd
#> 1 6.502035e-06 7.383157e-06 3.767182e-06 4.507067e-06 7.122517e-06 3.579249e-06
#> MM20.sd NAA.sd NAAG.sd PCh.sd PCr.sd sIns.sd
#> 1 8.280709e-06 1.021342e-06 1.289272e-06 1.99876e-06 3.16282e-06 7.079371e-07
#> Tau.sd tNAA.sd tCr.sd tCho.sd Glx.sd tLM09.sd
#> 1 3.741979e-06 7.13756e-07 5.84487e-07 2.125818e-07 2.883711e-06 9.731158e-07
#> tLM13.sd tLM20.sd phase lw shift asym
#> 1 1.520083e-06 2.886552e-06 10.83969 5.024144 -0.002971689 0.1771382
#> res.deviance res.niter res.info
#> 1 7.455757e-05 27 2
#> res.message bl_ed_pppm
#> 1 Relative error between `par' and the solution is at most `ptol'. 1.969325
#> max_bl_flex_used full_res fit_pts ppm_range SNR SRR FQN
#> 1 FALSE 8.202627e-05 497 3.8 63.23665 51.27884 1.520762
#> tNAA_lw tCr_lw tCho_lw auto_bl_crit_7 auto_bl_crit_5.901
#> 1 0.04585125 0.0516791 0.05460794 -8.872611 -8.918514
#> auto_bl_crit_4.942 auto_bl_crit_4.12 auto_bl_crit_3.425 auto_bl_crit_2.844
#> 1 -8.954323 -8.980263 -8.99748 -9.009404
#> auto_bl_crit_2.364 auto_bl_crit_1.969 auto_bl_crit_1.647 auto_bl_crit_1.384
#> 1 -9.017235 -9.02016 -9.008892 -8.957936
#> auto_bl_crit_1.17 auto_bl_crit_0.997 auto_bl_crit_0.856 auto_bl_crit_0.743
#> 1 -8.841034 -8.684534 -8.555253 -8.478498
#> auto_bl_crit_0.654 auto_bl_crit_0.593 auto_bl_crit_0.558 auto_bl_crit_0.54
#> 1 -8.441088 -8.424843 -8.418121 -8.415343
#> auto_bl_crit_0.532 auto_bl_crit_0.529
#> 1 -8.414182 -8.413692
Spectral SNR:
fit_res$res_tab$SNR
#> [1] 63.23665
Linewidth of the tNAA resonance in PPM:
fit_res$res_tab$tNAA_lw
#> [1] 0.04585125