demo/preProcessing.R
In bozenne/MRIaggr: Management, Display, and Processing of Medical Imaging Data
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%%%%%
#%%%%% test file 1.2 : pipeline of the pre processing
#%%%%%
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# setwd("V:/etude25/2891")
# cd ../../data1/etude/etude25/2891/
require(MRIaggr)
#### 0 loading (cf demo : ExtractNifti) ####
path <- system.file(file.path("nifti"), package = "MRIaggr")
param_nifti <- unlist(strsplit(list.files(path), split=c(".nii"), fixed = TRUE))
n.files <- length(param_nifti)
ls.array <- list()
for(iter_file in 1:n.files){
ls.array[[iter_file]] <- readMRI(paste(path, param_nifti[iter_file], sep = "/"),
format = "nifti")
}
MRIaggr.Pat1 <- constMRIaggr(ls.array = ls.array,
identifier = "Pat1", param = param_nifti,
default_value = NULL,pos_default_value = c(1,1,1),
rm.ls.array = TRUE)
#### Step A pre-rocessing : work on the raw contrast parameters ####
#### 1- compression ####
MRIaggr.Pat1red <- constCompressMRIaggr(MRIaggr.Pat1, compression.factor = 2, verbose = TRUE)
par(mfrow=c(2,4),mar=rep(2.5,4))
multiplot(MRIaggr.Pat1, param = "DWI_t0", window = NULL, breaks = seq(0,300,1), legend = TRUE)
multiplot(MRIaggr.Pat1red, param = "DWI_t0", window = NULL, breaks = seq(0,300,1), legend = TRUE)
#### 2- allocation of the clinical data ####
df.clinical <- data.frame(Age = 68,
Sex = "male",
NIHSS_H0 = 8,
NIHSS_t2 = 7,
NIHSS_D2 = 4,
NIHSS_W1 = NA,
NIHSS_M1 = 2,
M1_Recanalization = 0,
M1_Hemorrhage = 0,
Symptoms_admission = 330,
Treatment = 0,
FinalStroke_volume = 29.526,
AcuteStroke_volume = 25.2598)
allocClinic(MRIaggr.Pat1) <- df.clinical
selectClinic(MRIaggr.Pat1)
selectClinic(MRIaggr.Pat1, param = "Age")
#### 3- computation of the area of interest before brain extraction ####
#### conversion of the mask to logical
allocContrast(MRIaggr.Pat1, param = "MASK_DWI_t0", overwrite = TRUE) <- as.logical(selectContrast(MRIaggr.Pat1, param = "MASK_DWI_t0") > 0)
allocContrast(MRIaggr.Pat1, param = "MASK_T2_FLAIR_t2", overwrite = TRUE) <- as.logical(selectContrast(MRIaggr.Pat1, param = "MASK_T2_FLAIR_t2") > 0)
#### necrosis
res <- calcTableLesion(MRIaggr.Pat1, maskN = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"))
allocTable(MRIaggr.Pat1, type = "lesion", overwrite = TRUE) <- res
calcTableLesion(MRIaggr.Pat1, maskN = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"),
numeric2logical = TRUE, update.object = TRUE, overwrite = TRUE, verbose = TRUE)
selectTable(MRIaggr.Pat1, type = "lesion")
#### reperfusion-hypoperfusion
res <- calcTableHypoReperf(MRIaggr.Pat1, param = c("TTP","MTT"), timepoint = "t0",
overwrite = TRUE, update.object = TRUE)
selectTable(MRIaggr.Pat1, type = "hypoperfusion")
res <- calcTableHypoReperf(MRIaggr.Pat1, param = c("TTP","MTT"), timepoint = "t0",
mask = "MASK_DWI_t0",
overwrite = TRUE, update.object = TRUE)
selectTable(MRIaggr.Pat1, type = "hypoperfusion")
res <- calcTableHypoReperf(MRIaggr.Pat1, param = c("TTP","MTT"), timepoint = c("t0","t1"),
mask = "MASK_DWI_t0",
overwrite = TRUE, update.object = TRUE)
selectTable(MRIaggr.Pat1, type = "reperfusion")
#### threshold of the clinician mask
res <- calcROCthreshold(MRIaggr.Pat1, mask = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"), param = c("DWI_t0","T2_FLAIR_t2"),
numeric2logical = TRUE, plot = "boxplot_Youden")
calcROCthreshold(MRIaggr.Pat1, mask = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"), param = c("DWI_t0","T2_FLAIR_t2"),
numeric2logical = TRUE, plot = "ROC_Youden", update.object = TRUE, overwrite = TRUE)
selectDescStats(MRIaggr.Pat1, name = "Mask_threshold")
#### 4- filtering a contrast parameter ####
res <- calcFilter(MRIaggr.Pat1, param = c("T2_FLAIR_t2","DWI_t0","TTP_t0"),
filter="2D_M3", bilateral=FALSE, na.rm = FALSE, update.object = TRUE, overwrite = TRUE)
calcFilter(MRIaggr.Pat1, param = c("T2_FLAIR_t2","DWI_t0","TTP_t0"),
filter="2D_G3", bilateral=FALSE, na.rm = FALSE, update.object = TRUE, overwrite = TRUE)
par(mfrow=c(2,2),mar=rep(2,4))
multiplot(MRIaggr.Pat1, param = "T2_FLAIR_t2", main = "raw", num.main = FALSE,
num = 1, window = NULL, breaks = c(-100,seq(0, 600),900))
multiplot(MRIaggr.Pat1, param = "T2_FLAIR_t2_2D_G3", main = "2D_G3", num.main = FALSE,
num = 1, window = NULL, legend = FALSE, breaks = c(-100,seq(0, 600),900))
multiplot(MRIaggr.Pat1, param = "T2_FLAIR_t2_2D_M3", main = "2D_M3", num.main = FALSE,
num = 1, window = NULL, legend = FALSE, breaks = c(-100,seq(0, 600),900))
#### 5- computation of the lesion spatial groups ####
res <- calcGroupsMask(MRIaggr.Pat1, mask = "MASK_DWI_t0", W.spatial_res = c(1.875,1.875,6),
numeric2logical = TRUE, W.range = 6)
res <- calcGroupsMask(MRIaggr.Pat1, mask = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"),
W.spatial_res = c(1.875,1.875,6), numeric2logical = TRUE,
W.range = 6, update.object = TRUE, overwrite = TRUE)
selectDescStats(MRIaggr.Pat1, name = "GroupsLesion")
#### 6- brain extraction ####
#### calc.mask
res <- calcBrainMask(MRIaggr.Pat1, param = "T2_GRE_t0", type = "threshold",
th.select_optima = 2,
overwrite = TRUE, update.object = TRUE)
multiplot(MRIaggr.Pat1, param = "mask")
multiplot(MRIaggr.Pat1, param = "T2_GRE_t0")
res <- calcBrainMask(MRIaggr.Pat1, param = "T2_GRE_t0", type = "kmeans",
kmeans.n_groups = 2:4,
update.object = TRUE, overwrite = TRUE)
multiplot(MRIaggr.Pat1, param = "mask")
multiplot(MRIaggr.Pat1, param = "mask", num = 1, legend = FALSE)
multiplot(MRIaggr.Pat1, param = "T2_GRE_t0",index1 = "mask")
#### liss.mask
res <- calcSmoothMask(MRIaggr.Pat1, update.object = TRUE, overwrite = TRUE)
multiplot(MRIaggr.Pat1, param = "mask", legend = FALSE)
table(selectContrast(MRIaggr.Pat1, param = "mask"))
#### update TableLesion
res <- calcTableLesion(MRIaggr.Pat1, maskN = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"), mask = "mask",
numeric2logical = TRUE, update.object=FALSE, overwrite = TRUE)
selectTable(MRIaggr.Pat1, "lesion")
#### Step B pre-processing : work on the reduced contrast parameters ####
#### 0- contruct.red
MRIaggr.Pat1_red <- constReduceMRIaggr(MRIaggr.Pat1, mask = "mask")
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2")
#### 1- update of the area of interest ####
res <- calcTableHypoReperf(MRIaggr.Pat1_red,
param = c("TTP","MTT"),
timepoint = c("t0","t1"),
update.object = TRUE,
overwrite = TRUE
)
#### 2- probabilistic segmentation in tissue class ####
res <- calcTissueType(MRIaggr.Pat1_red, param = "T1_t0", update.object = TRUE,
niter = 100)
multiplot(MRIaggr.Pat1_red, num = 1,
param = c("CSF","WM","GM"), legend = FALSE,
palette = "rgb")
multiplot(MRIaggr.Pat1_red,
param = c("CSF","WM","GM"), legend = FALSE,
palette = "hsv", cex = 1.25)
#### 3- identification of the hemispheres ####
res <- calcHemisphere(MRIaggr.Pat1_red, param = "T2_GRE_t0",
mask = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"),
verbose = TRUE, update.object = TRUE, overwrite = TRUE)
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2",
midplane = TRUE)
#### 4- calculation of the contralateral values ####
res <- calcContralateral(MRIaggr.Pat1_red, param = c("DWI_t0","MTT_t0","TTP_t0","T2_FLAIR_t2"),
num = NULL, type = "mean", param.ref = "T1_t0",
distband = 1, lambda = 1,
verbose = TRUE, update.object = TRUE, overwrite = TRUE)
carto_test <- selectContrast(MRIaggr.Pat1_red, param = c("DWI_t0","DWI_t0_contro","T2_FLAIR_t2","T2_FLAIR_t2_contro","MASK_DWI_t0","MASK_T2_FLAIR_t2"))
carto_test$MASK_DWI_t0 <- as.logical(carto_test$MASK_DWI_t0)
carto_test$MASK_T2_FLAIR_t2 <- as.logical(carto_test$MASK_T2_FLAIR_t2)
par(mfrow = c(2,4), mar = rep(1.5,4), mgp = c(2,0.5,0))
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2", num = 1:3,
window = NULL, hemisphere = "left", main = "raw - slice ")
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2_contro", num = 1:3,
window = NULL, hemisphere = "lesion", main = "normalized - slice ")
res <- calcContralateral(MRIaggr.Pat1_red, param = c("DWI_t0","T2_FLAIR_t2"),
num = NULL, type = "mean", param.ref = "T1_t0", distband = 1, lambda = 1,
verbose = TRUE)
multiplot(res$data[,c("i","j","k")],
contrast = res$data[,"DWI_t0_contro", drop = FALSE],
index1 = res$data[res$index_plot$index.plot_lesionR,c("i","j","k"), drop = FALSE]
)
multiplot(res$data[,c("i","j","k")],
contrast = res$data[,"DWI_t0_contro", drop = FALSE],
index1 = res$data[res$index_plot$index.plot_lesionR,c("i","j","k"), drop = FALSE],
index2 = res$data[res$index_plot$index.plot_lesionL,c("i","j","k"), drop = FALSE]
)
#### 5- computation of the neighborhood matrix and the regional values ####
res <- calcW(MRIaggr.Pat1_red, range = 10, spatial_res = c(1.875,1.875,6),
upper = TRUE,
update.object = TRUE, overwrite = TRUE)
res <- calcRegionalContrast(MRIaggr.Pat1_red, param = c("DWI_t0","MTT_t0","TTP_t0","T2_FLAIR_t2"),
bandwidth = 1.875, update.object = TRUE, overwrite = TRUE)
par(mfrow = c(2,4), mar = rep(1.5,4), mgp = c(2,0.5,0))
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2", num = 1:3,
window = NULL, main = "raw - slice ")
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2_regional", num = 1:3,
window = NULL, main = "regional - slice ")
#### 5- normalisation and tissue distribution ####
res <- calcNormalization(MRIaggr.Pat1_red, param = c("DWI_t0","MTT_t0","TTP_t0","T2_FLAIR_t2","DWI_t0_regional","MTT_t0_regional","TTP_t0_regional"),
update.object = TRUE, overwrite = TRUE)
par(mfrow = c(2,4), mar = rep(1.5,4),mgp = c(2,0.5,0))
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2", num = 1:3,
window = NULL, main = "raw - slice ")
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2", num = 1:3,
window = NULL, main = "normalized norm",
norm_mu = "contralateral", norm_sigma = "contralateral")
names(selectNormalization(MRIaggr.Pat1_red))
selectNormalization(MRIaggr.Pat1_red, type = "global", mu = TRUE, sigma = FALSE)
res <- calcDistTissues(MRIaggr.Pat1_red, param = c("DWI_t0","T2_FLAIR_t2"),
param.membership = c("CSF","WM","GM","MASK_DWI_t0")
)
bozenne/MRIaggr documentation built on May 13, 2019, 1:39 a.m.
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%%%%%
#%%%%% test file 1.2 : pipeline of the pre processing
#%%%%%
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# setwd("V:/etude25/2891")
# cd ../../data1/etude/etude25/2891/
require(MRIaggr)
#### 0 loading (cf demo : ExtractNifti) ####
path <- system.file(file.path("nifti"), package = "MRIaggr")
param_nifti <- unlist(strsplit(list.files(path), split=c(".nii"), fixed = TRUE))
n.files <- length(param_nifti)
ls.array <- list()
for(iter_file in 1:n.files){
ls.array[[iter_file]] <- readMRI(paste(path, param_nifti[iter_file], sep = "/"),
format = "nifti")
}
MRIaggr.Pat1 <- constMRIaggr(ls.array = ls.array,
identifier = "Pat1", param = param_nifti,
default_value = NULL,pos_default_value = c(1,1,1),
rm.ls.array = TRUE)
#### Step A pre-rocessing : work on the raw contrast parameters ####
#### 1- compression ####
MRIaggr.Pat1red <- constCompressMRIaggr(MRIaggr.Pat1, compression.factor = 2, verbose = TRUE)
par(mfrow=c(2,4),mar=rep(2.5,4))
multiplot(MRIaggr.Pat1, param = "DWI_t0", window = NULL, breaks = seq(0,300,1), legend = TRUE)
multiplot(MRIaggr.Pat1red, param = "DWI_t0", window = NULL, breaks = seq(0,300,1), legend = TRUE)
#### 2- allocation of the clinical data ####
df.clinical <- data.frame(Age = 68,
Sex = "male",
NIHSS_H0 = 8,
NIHSS_t2 = 7,
NIHSS_D2 = 4,
NIHSS_W1 = NA,
NIHSS_M1 = 2,
M1_Recanalization = 0,
M1_Hemorrhage = 0,
Symptoms_admission = 330,
Treatment = 0,
FinalStroke_volume = 29.526,
AcuteStroke_volume = 25.2598)
allocClinic(MRIaggr.Pat1) <- df.clinical
selectClinic(MRIaggr.Pat1)
selectClinic(MRIaggr.Pat1, param = "Age")
#### 3- computation of the area of interest before brain extraction ####
#### conversion of the mask to logical
allocContrast(MRIaggr.Pat1, param = "MASK_DWI_t0", overwrite = TRUE) <- as.logical(selectContrast(MRIaggr.Pat1, param = "MASK_DWI_t0") > 0)
allocContrast(MRIaggr.Pat1, param = "MASK_T2_FLAIR_t2", overwrite = TRUE) <- as.logical(selectContrast(MRIaggr.Pat1, param = "MASK_T2_FLAIR_t2") > 0)
#### necrosis
res <- calcTableLesion(MRIaggr.Pat1, maskN = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"))
allocTable(MRIaggr.Pat1, type = "lesion", overwrite = TRUE) <- res
calcTableLesion(MRIaggr.Pat1, maskN = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"),
numeric2logical = TRUE, update.object = TRUE, overwrite = TRUE, verbose = TRUE)
selectTable(MRIaggr.Pat1, type = "lesion")
#### reperfusion-hypoperfusion
res <- calcTableHypoReperf(MRIaggr.Pat1, param = c("TTP","MTT"), timepoint = "t0",
overwrite = TRUE, update.object = TRUE)
selectTable(MRIaggr.Pat1, type = "hypoperfusion")
res <- calcTableHypoReperf(MRIaggr.Pat1, param = c("TTP","MTT"), timepoint = "t0",
mask = "MASK_DWI_t0",
overwrite = TRUE, update.object = TRUE)
selectTable(MRIaggr.Pat1, type = "hypoperfusion")
res <- calcTableHypoReperf(MRIaggr.Pat1, param = c("TTP","MTT"), timepoint = c("t0","t1"),
mask = "MASK_DWI_t0",
overwrite = TRUE, update.object = TRUE)
selectTable(MRIaggr.Pat1, type = "reperfusion")
#### threshold of the clinician mask
res <- calcROCthreshold(MRIaggr.Pat1, mask = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"), param = c("DWI_t0","T2_FLAIR_t2"),
numeric2logical = TRUE, plot = "boxplot_Youden")
calcROCthreshold(MRIaggr.Pat1, mask = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"), param = c("DWI_t0","T2_FLAIR_t2"),
numeric2logical = TRUE, plot = "ROC_Youden", update.object = TRUE, overwrite = TRUE)
selectDescStats(MRIaggr.Pat1, name = "Mask_threshold")
#### 4- filtering a contrast parameter ####
res <- calcFilter(MRIaggr.Pat1, param = c("T2_FLAIR_t2","DWI_t0","TTP_t0"),
filter="2D_M3", bilateral=FALSE, na.rm = FALSE, update.object = TRUE, overwrite = TRUE)
calcFilter(MRIaggr.Pat1, param = c("T2_FLAIR_t2","DWI_t0","TTP_t0"),
filter="2D_G3", bilateral=FALSE, na.rm = FALSE, update.object = TRUE, overwrite = TRUE)
par(mfrow=c(2,2),mar=rep(2,4))
multiplot(MRIaggr.Pat1, param = "T2_FLAIR_t2", main = "raw", num.main = FALSE,
num = 1, window = NULL, breaks = c(-100,seq(0, 600),900))
multiplot(MRIaggr.Pat1, param = "T2_FLAIR_t2_2D_G3", main = "2D_G3", num.main = FALSE,
num = 1, window = NULL, legend = FALSE, breaks = c(-100,seq(0, 600),900))
multiplot(MRIaggr.Pat1, param = "T2_FLAIR_t2_2D_M3", main = "2D_M3", num.main = FALSE,
num = 1, window = NULL, legend = FALSE, breaks = c(-100,seq(0, 600),900))
#### 5- computation of the lesion spatial groups ####
res <- calcGroupsMask(MRIaggr.Pat1, mask = "MASK_DWI_t0", W.spatial_res = c(1.875,1.875,6),
numeric2logical = TRUE, W.range = 6)
res <- calcGroupsMask(MRIaggr.Pat1, mask = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"),
W.spatial_res = c(1.875,1.875,6), numeric2logical = TRUE,
W.range = 6, update.object = TRUE, overwrite = TRUE)
selectDescStats(MRIaggr.Pat1, name = "GroupsLesion")
#### 6- brain extraction ####
#### calc.mask
res <- calcBrainMask(MRIaggr.Pat1, param = "T2_GRE_t0", type = "threshold",
th.select_optima = 2,
overwrite = TRUE, update.object = TRUE)
multiplot(MRIaggr.Pat1, param = "mask")
multiplot(MRIaggr.Pat1, param = "T2_GRE_t0")
res <- calcBrainMask(MRIaggr.Pat1, param = "T2_GRE_t0", type = "kmeans",
kmeans.n_groups = 2:4,
update.object = TRUE, overwrite = TRUE)
multiplot(MRIaggr.Pat1, param = "mask")
multiplot(MRIaggr.Pat1, param = "mask", num = 1, legend = FALSE)
multiplot(MRIaggr.Pat1, param = "T2_GRE_t0",index1 = "mask")
#### liss.mask
res <- calcSmoothMask(MRIaggr.Pat1, update.object = TRUE, overwrite = TRUE)
multiplot(MRIaggr.Pat1, param = "mask", legend = FALSE)
table(selectContrast(MRIaggr.Pat1, param = "mask"))
#### update TableLesion
res <- calcTableLesion(MRIaggr.Pat1, maskN = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"), mask = "mask",
numeric2logical = TRUE, update.object=FALSE, overwrite = TRUE)
selectTable(MRIaggr.Pat1, "lesion")
#### Step B pre-processing : work on the reduced contrast parameters ####
#### 0- contruct.red
MRIaggr.Pat1_red <- constReduceMRIaggr(MRIaggr.Pat1, mask = "mask")
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2")
#### 1- update of the area of interest ####
res <- calcTableHypoReperf(MRIaggr.Pat1_red,
param = c("TTP","MTT"),
timepoint = c("t0","t1"),
update.object = TRUE,
overwrite = TRUE
)
#### 2- probabilistic segmentation in tissue class ####
res <- calcTissueType(MRIaggr.Pat1_red, param = "T1_t0", update.object = TRUE,
niter = 100)
multiplot(MRIaggr.Pat1_red, num = 1,
param = c("CSF","WM","GM"), legend = FALSE,
palette = "rgb")
multiplot(MRIaggr.Pat1_red,
param = c("CSF","WM","GM"), legend = FALSE,
palette = "hsv", cex = 1.25)
#### 3- identification of the hemispheres ####
res <- calcHemisphere(MRIaggr.Pat1_red, param = "T2_GRE_t0",
mask = c("MASK_DWI_t0","MASK_T2_FLAIR_t2"),
verbose = TRUE, update.object = TRUE, overwrite = TRUE)
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2",
midplane = TRUE)
#### 4- calculation of the contralateral values ####
res <- calcContralateral(MRIaggr.Pat1_red, param = c("DWI_t0","MTT_t0","TTP_t0","T2_FLAIR_t2"),
num = NULL, type = "mean", param.ref = "T1_t0",
distband = 1, lambda = 1,
verbose = TRUE, update.object = TRUE, overwrite = TRUE)
carto_test <- selectContrast(MRIaggr.Pat1_red, param = c("DWI_t0","DWI_t0_contro","T2_FLAIR_t2","T2_FLAIR_t2_contro","MASK_DWI_t0","MASK_T2_FLAIR_t2"))
carto_test$MASK_DWI_t0 <- as.logical(carto_test$MASK_DWI_t0)
carto_test$MASK_T2_FLAIR_t2 <- as.logical(carto_test$MASK_T2_FLAIR_t2)
par(mfrow = c(2,4), mar = rep(1.5,4), mgp = c(2,0.5,0))
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2", num = 1:3,
window = NULL, hemisphere = "left", main = "raw - slice ")
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2_contro", num = 1:3,
window = NULL, hemisphere = "lesion", main = "normalized - slice ")
res <- calcContralateral(MRIaggr.Pat1_red, param = c("DWI_t0","T2_FLAIR_t2"),
num = NULL, type = "mean", param.ref = "T1_t0", distband = 1, lambda = 1,
verbose = TRUE)
multiplot(res$data[,c("i","j","k")],
contrast = res$data[,"DWI_t0_contro", drop = FALSE],
index1 = res$data[res$index_plot$index.plot_lesionR,c("i","j","k"), drop = FALSE]
)
multiplot(res$data[,c("i","j","k")],
contrast = res$data[,"DWI_t0_contro", drop = FALSE],
index1 = res$data[res$index_plot$index.plot_lesionR,c("i","j","k"), drop = FALSE],
index2 = res$data[res$index_plot$index.plot_lesionL,c("i","j","k"), drop = FALSE]
)
#### 5- computation of the neighborhood matrix and the regional values ####
res <- calcW(MRIaggr.Pat1_red, range = 10, spatial_res = c(1.875,1.875,6),
upper = TRUE,
update.object = TRUE, overwrite = TRUE)
res <- calcRegionalContrast(MRIaggr.Pat1_red, param = c("DWI_t0","MTT_t0","TTP_t0","T2_FLAIR_t2"),
bandwidth = 1.875, update.object = TRUE, overwrite = TRUE)
par(mfrow = c(2,4), mar = rep(1.5,4), mgp = c(2,0.5,0))
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2", num = 1:3,
window = NULL, main = "raw - slice ")
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2_regional", num = 1:3,
window = NULL, main = "regional - slice ")
#### 5- normalisation and tissue distribution ####
res <- calcNormalization(MRIaggr.Pat1_red, param = c("DWI_t0","MTT_t0","TTP_t0","T2_FLAIR_t2","DWI_t0_regional","MTT_t0_regional","TTP_t0_regional"),
update.object = TRUE, overwrite = TRUE)
par(mfrow = c(2,4), mar = rep(1.5,4),mgp = c(2,0.5,0))
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2", num = 1:3,
window = NULL, main = "raw - slice ")
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2", num = 1:3,
window = NULL, main = "normalized norm",
norm_mu = "contralateral", norm_sigma = "contralateral")
names(selectNormalization(MRIaggr.Pat1_red))
selectNormalization(MRIaggr.Pat1_red, type = "global", mu = TRUE, sigma = FALSE)
res <- calcDistTissues(MRIaggr.Pat1_red, param = c("DWI_t0","T2_FLAIR_t2"),
param.membership = c("CSF","WM","GM","MASK_DWI_t0")
)
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