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https://github.com/magneticresonanceimaging/perk.jl

PERK: Parameter Estimation via Regression with Kernels
https://github.com/magneticresonanceimaging/perk.jl

estimation kernel-machine-learning kernel-ridge-regression

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PERK: Parameter Estimation via Regression with Kernels

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README 

        
# PERK.jl



https://github.com/MagneticResonanceImaging/PERK.jl



[![action status][action-img]][action-url]

[![codecov][codecov-img]][codecov-url]

[![license][license-img]][license-url]

[![docs stable][docs-stable-img]][docs-stable-url]

[![docs dev][docs-dev-img]][docs-dev-url]



This package implements PERK, a parameter estimation technique presented in

[G. Nataraj, J.-F. Nielsen, C. Scott, and J. A. Fessler. Dictionary-free MRI PERK: Parameter estimation via regression with kernels. IEEE Trans. Med. Imag., 37(9):2103-14, September 2018](https://ieeexplore.ieee.org/document/8320384/).

This code was inspired by the MATLAB code written by Gopal Nataraj,

which can be found [here](https://github.com/gopal-nataraj/perk).



## Getting Started

At the Julia REPL, type `]` to enter the package prompt.

Then type `add https://github.com/MagneticResonanceImaging/PERK.jl`

to add the latest version.

(Append `#v0.3.2` to specify a specific version.)

Hit backspace to return to the normal Julia prompt,

and then type `using PERK` to load the package.



## Overview

The function `perk` provides the main functionality.

Training is done by generating synthetic data

using randomly generated parameters.

Distributions for these parameters can be passed directly to `perk`,

which will use them for training

and then estimate the latent parameters from the given test data.

Alternatively, one can pass the parameter distributions to `PERK.train`,

which will create a `TrainingData` object that can then be passed to `perk`

(and used multiple times, if desired).

One must also pass a `Kernel` object to `perk`.

Three are provided in this package:

`GaussianKernel`, `GaussianRFF`, and `EuclideanKernel`.

`GaussianKernel` and `GaussianRFF` are described in the paper.

Using `EuclideanKernel` indicates to solve ridge regression

instead of kernel ridge regression.



Because PERK utilizes kernel ridge regression at its core,

one can also use this package for solving kernel ridge regression.

(This can be useful if, e.g., one already has training data

and therefore does not need to generate some.)

Calling `PERK.krr_train` returns a `TrainingData` object

that can be passed to `PERK.krr`.

As with `perk`,

both `PERK.krr_train` and `PERK.krr` must be passed a `Kernel` object.



## Related packages



These are more general and not MRI specific.

- https://github.com/JuliaGaussianProcesses/KernelFunctions.jl

  [an example](https://juliagaussianprocesses.github.io/KernelFunctions.jl/dev/examples/kernel-ridge-regression)

- https://github.com/gdkrmr/KernelRidgeRegression.jl



## Status



This repo is not yet registered in part because its name

does not meet

[Julia naming guidelines](https://pkgdocs.julialang.org/v1/creating-packages/#Package-naming-guidelines).

If you want to be registered,

please post an issue and suggest a name.



On 2024-04-08,

the package was relocated here from

https://github.com/StevenWhitaker/PERK.jl

Some links may be broken by this transition.



[action-img]: https://github.com/MagneticResonanceImaging/PERK.jl/workflows/runtests/badge.svg

[action-url]: https://github.com/MagneticResonanceImaging/PERK.jl/actions

[codecov-img]: (https://codecov.io/github/MagneticResonanceImaging/PERK.jl/coverage.svg?branch=main

[codecov-url]: https://codecov.io/github/MagneticResonanceImaging/PERK.jl?branch=main

[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg

[docs-stable-url]: https://MagneticResonanceImaging.github.io/PERK.jl/stable

[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg

[docs-dev-url]: https://MagneticResonanceImaging.github.io/PERK.jl/dev

[license-img]: http://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat

[license-url]: LICENSE