sublime (version 1.3)

SuBLIME_prediction: Gets predicted probabilities from SuBLIME

Description

Takes in MRI images from followup and gets predictions (probabilities) of the enhancing of lesions

Usage

SuBLIME_prediction(baseline_flair, follow_up_flair, baseline_pd, follow_up_pd, baseline_t2, follow_up_t2, baseline_t1, follow_up_t1, time_diff, baseline_nawm_mask = NULL, follow_up_nawm_mask = baseline_nawm_mask, brain_mask, model = SuBLIME::SuBLIME_model, voxsel = TRUE, smooth.using = c("GaussSmoothArray", "none"), voxsel.sigma = diag(3, 3), voxsel.ksize = 5, s.sigma = diag(3, 3), s.ksize = 3, plot.imgs = FALSE, slice = 90, pdfname = "diag.pdf", verbose = TRUE)

Arguments

baseline_flair

Baseline FLAIR image, either array or class nifti

follow_up_flair

Followup FLAIR image, either array or class nifti

baseline_pd

Baseline PD image, either array or class nifti

follow_up_pd

Followup PD image, either array or class nifti

baseline_t2

Baseline T2 image, either array or class nifti

follow_up_t2

Followup T2 image, either array or class nifti

baseline_t1

Baseline T1 image, either array or class nifti

follow_up_t1

Followup T1 image, either array or class nifti

time_diff

Difference in time (in days) between baseline and followup, numeric

baseline_nawm_mask

Baseline Normal Appearing white matter mask, either array or class nifti. Will be coerced to logical usign baseline_nawm_mask $> 0$. If NULL, no NAWM normalization is done (assumes data is already normalized)

follow_up_nawm_mask

Followup Normal Appearing white matter mask, either array or class nifti. Will be coerced to logical usign follow_up_nawm_mask $> 0$. Defaults to baseline_nawm_mask if not specified. If NULL, no NAWM normalization is done (assumes data is already normalized)

brain_mask

Brain mask, either array or class nifti. Will be #' coerced to logical usign brain_mask $> 0$.

model

Model of class lm or set of coefficients.

voxsel

Do Voxel Selection based on normalized T2 (logical)

smooth.using

Character vector to decide if using GaussSmoothArray from AnalyzeFMRI or fslsmooth from fslr package

voxsel.sigma

Sigma passed to voxel_select

voxsel.ksize

Kernel size passed to voxel_select

s.sigma

Sigma passed to GaussSmoothArray

s.ksize

Kernel size passed to GaussSmoothArray

plot.imgs

Plot images along the way

slice

Slice to be plotted

pdfname

Name of pdf created for plot.imgs

verbose

Print Diagnostic Messages

Value

array

See Also

predict

Examples

## Not run: 
# download_data()
# modes = c("FLAIR", "PD", "T2", "VolumetricT1")
# modals = paste0(modes, "norm.nii.gz")
# base_files = system.file(file.path("01/Baseline", modals), package="SuBLIME")
# base_imgs = lapply(base_files, readNIfTI, reorient=FALSE)
# f_files = system.file(file.path("01/FollowUp", modals), package="SuBLIME")
# f_imgs = lapply(f_files, readNIfTI, reorient=FALSE) 
# names(base_imgs) = names(f_imgs) = modes
# baseline_nawm_file =  system.file("01/Baseline/nawm.nii.gz", package="SuBLIME")
# baseline_nawm_mask =  readNIfTI(baseline_nawm_file, reorient=FALSE)
# baseline_nawm_mask = drop(baseline_nawm_mask)
# follow_up_nawm_file =  system.file("01/FollowUp/nawm.nii.gz", package="SuBLIME")
# follow_up_nawm_mask =  readNIfTI(follow_up_nawm_file, reorient=FALSE) 
# brain_file =  system.file("01/duramask.nii.gz", package="SuBLIME")
# brain_mask =  readNIfTI(brain_file, reorient=FALSE) 
# brain_mask = drop(brain_mask)
# 
# follow_up_nawm_mask = NULL
# baseline_nawm_mask = NULL
# smooth.using = "GaussSmoothArray"
# verbose = TRUE
# time_diff = 10
# voxsel = TRUE
# model = SuBLIME_model
# #voxsel.sigma = s.sigma =diag(3,3)
# #s.ksize = 3
# #voxsel.ksize = 5
# 
# outimg = SuBLIME_prediction(
# baseline_flair = base_imgs[["FLAIR"]],
# follow_up_flair= f_imgs[["FLAIR"]],
# baseline_pd = base_imgs[["PD"]],
# follow_up_pd = f_imgs[["PD"]],
# baseline_t2 = base_imgs[["T2"]], 
# follow_up_t2 = f_imgs[["T2"]],
# baseline_t1 = base_imgs[["VolumetricT1"]],
# follow_up_t1 = f_imgs[["VolumetricT1"]],
# time_diff = time_diff,
# baseline_nawm_mask = baseline_nawm_mask,
# brain_mask = brain_mask,
# voxsel = voxsel,
# model = model, plot.imgs= TRUE,
# pdfname = "~/Dropbox/SuBLIME_Web_Test/01/pckg_diagnostc.pdf"
# )
# 
# names(base_imgs) = paste0("baseline_", c("flair", "pd", "t2", "t1"))
# names(f_imgs) = paste0("follow_up_", c("flair", "pd", "t2", "t1"))
# attach(base_imgs)
# attach(f_imgs)
# ## End(Not run)