MICSI DENOISING
MICSI denoising is a deterministic "smart averaging" approach that combines many different measurements across the MRI exam to quantify and remove noise with remarkable precision. MICSI denoising, or "MP-PCA" as it's known in the research community, has been an enabling technology for many multi-measurement applications including diffusion and functional MRI in the MRI community. The boost in signal-to-noise ratio allows for higher spatial and temporal resolution as well as enable otherwise noisy image modalities on lower field systems.
Following widespread praise in the research community and funding from the National Cancer Institute, we are bringing this tool into clinical practice.
BRINGING MPPCA INTO CLINICAL PRACTICE
STANDARD
STANDARD
MICSI
REFERENCES
1. Ades-aron et al. Improved Task-based Functional MRI Language Mapping in Patients with Brain Tumors through Marchenko-Pastur Principal Component Analysis Denoising. (2021) Radiology. 298, 365-373
2. Veraart et al. Diffusion MRI noise mapping using random matrix theory. (2016) Magn Reson Med. 76, 1582-1593
3. Veraart et al. Denoising of diffusion MRI using random matrix theory. (2016) Neuroimage. 142, 394-406
4. Veraart et al. Gibbs ringing in diffusion MRI. (2016) Magn Reson Med. 76, 301-14
5. Lemberskiy et al. System, method and computer-accessible medium for facilitating noise removal in magnetic resonance imaging. (2019). US20210076972A1.
6. Novikov et al. System, method and computer accessible medium for noise estimation, noise removal and Gibbs ringing removal. (2016). US10698065B2.
7. Non-invasive neurosurgical planning with Random Matrix Theory MRI. (2020) NIH National Cancer Institute. STTR Phase I.
You can study the power-spectrum of the residuals, defined as the density-density autocorrelation function on the difference between noisy and denoised images. Ideal residuals, should not have structure over any frequencies, Γ(k)=1.
MICSI denoising has near perfect residuals, whereas all other denoising methods will remove significant signal.
How do you know if your denoising algorithm is removing noise and not signal?
POWER SPECTRUM
OF THE RESIDUALS
MICSI denoising is a deterministic "smart averaging" approach that combines many different measurements across the MRI exam to quantify and remove noise with remarkable precision. MICSI denoising, or "MP-PCA" as it's known in the research community, has been an enabling technology for many multi-measurement applications including diffusion and functional MRI in the MRI community. The boost in signal-to-noise ratio allows for higher spatial and temporal resolution as well as enable otherwise noisy image modalities on lower field systems.
Following widespread praise in the research community and funding from the National Cancer Institute, we are bringing this tool into clinical practice.
STANDARD
MICSI
BRINGING MPPCA INTO CLINICAL PRACTICE
You can study the power-spectrum of the residuals, defined as the density-density autocorrelation function on the difference between noisy and denoised images. Ideal residuals, should not have structure over any frequencies, Γ(k)=1.
MICSI denoising has near perfect residuals, whereas all other denoising methods will remove significant signal.
How do you know if your denoising algorithm is removing noise and not signal?
POWER SPECTRUM
OF THE RESIDUALS
References
1. Ades-aron et al. Improved Task-based Functional MRI Language Mapping in Patients with Brain Tumors through Marchenko-Pastur Principal Component Analysis Denoising. (2021) Radiology. 298, 365-373
2. Veraart et al. Diffusion MRI noise mapping using random matrix theory. (2016) Magn Reson Med. 76, 1582-1593
3. Veraart et al. Denoising of diffusion MRI using random matrix theory. (2016) Neuroimage. 142, 394-406
4. Veraart et al. Gibbs ringing in diffusion MRI. (2016) Magn Reson Med. 76, 301-14
5. Lemberskiy et al. System, method and computer-accessible medium for facilitating noise removal in magnetic resonance imaging. (2019). US20210076972A1.
6. Novikov et al. System, method and computer accessible medium for noise estimation, noise removal and Gibbs ringing removal. (2016). US10698065B2.
7. Non-invasive neurosurgical planning with Random Matrix Theory MRI. (2020) NIH National Cancer Institute. STTR Phase I.
MICSI denoising is a deterministic "smart averaging" approach that combines many different measurements across the MRI exam to quantify and remove noise with remarkable precision. MICSI denoising, or "MP-PCA" as it's known in the research community, has been an enabling technology for many multi-measurement applications including diffusion and functional MRI in the MRI community. The boost in signal-to-noise ratio allows for higher spatial and temporal resolution as well as enable otherwise noisy image modalities on lower field systems.
Following widespread praise in the research community and funding from the National Cancer Institute, we are bringing this tool into clinical practice.
BRINGING MPPCA INTO CLINICAL PRACTICE
You can study the power-spectrum of the residuals, defined as the density-density autocorrelation function on the difference between noisy and denoised images. Ideal residuals, should not have structure over any frequencies, Γ(k)=1.
MICSI denoising has near perfect residuals, whereas all other denoising methods will remove significant signal.
How do you know if your denoising algorithm is removing noise and not signal?
REFERENCES
1. Ades-aron et al. Improved Task-based Functional MRI Language Mapping in Patients with Brain Tumors through Marchenko-Pastur Principal Component Analysis Denoising. (2021) Radiology. 298, 365-373
2. Veraart et al. Diffusion MRI noise mapping using random matrix theory. (2016) Magn Reson Med. 76, 1582-1593
3. Veraart et al. Denoising of diffusion MRI using random matrix theory. (2016) Neuroimage. 142, 394-406
4. Veraart et al. Gibbs ringing in diffusion MRI. (2016) Magn Reson Med. 76, 301-14
5. Lemberskiy et al. System, method and computer-accessible medium for facilitating noise removal in magnetic resonance imaging. (2019). US20210076972A1.
6. Novikov et al. System, method and computer accessible medium for noise estimation, noise removal and Gibbs ringing removal. (2016). US10698065B2.
7. Non-invasive neurosurgical planning with Random Matrix Theory MRI. (2020) NIH National Cancer Institute. STTR Phase I.