1. 目的
- MRtrixを用いた拡散MRIのノイズ除去（Denoise）
2. コマンド
拡散MRIのノイズ除去には、MRtrixのdwidenoiseを用いる。dwidenoiseは、Marchenko-Pastur PCAを用いたデノイズである。
拡散MRIのノイズ除去は、前処理の一番最初に実行する必要がある。
dwidenoiseのヘルプは、次の通り。
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SYNOPSIS
dMRI noise level estimation and denoising using Marchenko-Pastur PCA
USAGE
dwidenoise [ options ] dwi out
dwi the input diffusion-weighted image.
out the output denoised DWI image.
DESCRIPTION
DWI data denoising and noise map estimation by exploiting data redundancy
in the PCA domain using the prior knowledge that the eigenspectrum of
random covariance matrices is described by the universal Marchenko-Pastur
(MP) distribution. Fitting the MP distribution to the spectrum of
patch-wise signal matrices hence provides an estimator of the noise level
'sigma', as was first shown in Veraart et al. (2016) and later improved in
Cordero-Grande et al. (2019). This noise level estimate then determines
the optimal cut-off for PCA denoising.
Important note: image denoising must be performed as the first step of the
image processing pipeline. The routine will fail if interpolation or
smoothing has been applied to the data prior to denoising.
Note that this function does not correct for non-Gaussian noise biases
present in magnitude-reconstructed MRI images. If available, including the
MRI phase data can reduce such non-Gaussian biases, and the command now
supports complex input data.
OPTIONS
-mask image
Only process voxels within the specified binary brain mask image.
-extent window
Set the patch size of the denoising filter. By default, the command will
select the smallest isotropic patch size that exceeds the number of DW
images in the input data, e.g., 5x5x5 for data with <= 125 DWI volumes,
7x7x7 for data with <= 343 DWI volumes, etc.
-noise level
The output noise map, i.e., the estimated noise level 'sigma' in the data.
Note that on complex input data, this will be the total noise level across
real and imaginary channels, so a scale factor sqrt(2) applies.
-datatype float32/float64
Datatype for the eigenvalue decomposition (single or double precision).
For complex input data, this will select complex float32 or complex
float64 datatypes.
-estimator Exp1/Exp2
Select the noise level estimator (default = Exp2), either:
* Exp1: the original estimator used in Veraart et al. (2016), or
* Exp2: the improved estimator introduced in Cordero-Grande et al. (2019).
Standard options
-info
display information messages.
-quiet
do not display information messages or progress status; alternatively,
this can be achieved by setting the MRTRIX_QUIET environment variable to a
non-empty string.
-debug
display debugging messages.
-force
force overwrite of output files (caution: using the same file as input and
output might cause unexpected behaviour).
-nthreads number
use this number of threads in multi-threaded applications (set to 0 to
disable multi-threading).
-config key value (multiple uses permitted)
temporarily set the value of an MRtrix config file entry.
-help
display this information page and exit.
-version
display version information and exit.
基本的な使い方は、次の通り。
dwidenoise <入力画像> <出力画像>
2.1. 使用例
前処理する前の拡散MRI（DWI.nii.gz）に、dwidenoiseを実行する。
dwidenoise DWI.nii.gz DWI_denoised.nii.gz
処理後の画像は、以下。
著者情報： 斎藤 勇哉
順天堂大学医学部 大学院医学研究科 放射線診断学講座所属脳MRI 画像解析が専門であり、テーマは①神経変性疾患の機序解明、②医用人工知能の開発、③多施設データのハーモナイゼーション、④速読が脳に与える影響や学習効果、⑤SNS解析を用いたマーケティング戦略の改善。
医療分野に関わらず、自然言語処理・スクレイピング・データ分析・Web アプリ開発を得意とし、企業や他大学の研究を支援。
主な使用言語は、Python、Shell Script、MATLAB、HTML、CSS
