QMRITools`
QMRITools`

PCADeNoise

PCADeNoise[data]

removes rician noise from the data with PCA.

PCADeNoise[data,mask]

removes rician noise from the data with PCA only withing the mask.

PCADeNoise[data,mask,sig]

removes rician noise from the data with PCA only withing the mask using sig as prior knowledge or fixed value. Output is de {data denoise, sigma map} by default if PCAOutput is Full then fitted {data dnoise , {sigma fit, average sigma}, {number components, number of fitted voxesl, number of max fits}, total fit -time per 500 ittt}.

PCADeNoise[]

is based on DOI: 10.1016/j.neuroimage.2016.08.016 and 10.1002/mrm.26059.

Details

  • The following options can be given:
  • PCAKernel5PCAKernel is an option of PCADeNoise. It sets the kernel size.
    PCAOutputFalsePCAOutput is an option of PCADeNoise. If output is full the output is {datao, {output[[1]], sigmat}, {output[[2]], output[[3]], j}, timetot}. Else the output is {datao, sigmat}.
    PCATollerance0PCATollerance is an option of PCADeNoise and shuld be an integer > 0. Default value is 0. When increased the denoise method removes less noise.
    PCAWeightingTruePCAWeighting is an option of PCADeNoise and can be True of False. Default value is False. When True the weights of the per voxel result are calculated based on the number of non noise components.
    PCAClippingTruePCAClipping is an option of PCADeNoise and can be True of False. If True the output is clipped between 0 and the max absolute value of the input data.
    PCAComplexFalsePCAComplex is an option of PCADeNoise and can be True of False. If set true the input data is expexted to be {real, imag}.
    Method"Similarity"Method is an option for various algorithm-intensive functions that specifies what internal methods they should use.
    MonitorCalcFalseMonitorCalc is an option for many processing functions. When true the proceses of the calculation is shown.

Examples