MR KLEAN: a Generalized Acquisition-agnostic LLR k-Space Denoising Method for High-dimensional Imaging

PurposeHigh-dimensional and dynamic MRI are often limited by thermal noise, particularly in accelerated acquisitions. Although image-domain low-rank denoising methods (e.g., MP-PCA and NORDIC) are effective, reliance on a stationary noise distribution limits applicability to non-Cartesian sampling and advanced reconstruction methods. This work introduces MR KLEAN, a k-space low-rank denoising framework agnostic to acquisition trajectory and reconstruction strategy. Theory and MethodsMR KLEAN exploits locally low-rank structure in multichannel, high-dimensional k-space. Data are prewhitened using a noise-only calibration scan to enforce independent and identically distributed, zero-mean, unit-variance noise. Casorati matrices are generated through local k-space patches and denoised via singular-value thresholding, with thresholds from Monte-Carlo simulations under known noise statistics. MR KLEAN was evaluated in (1) a phantom study using Cartesian 3D FLASH, (2) an ASL study with spiral readout and compressed sensing reconstruction to assess generaliz-ability and preservation of temporal information via resting-state connectivity analysis, and (3) an accelerated cardiac cine study to assess performance under rapid temporal dynamics. ResultsMR KLEAN increased SNR and CNR in phantom study. In vivo ASL showed reduced noise for perfusion images, improved relative SNR, and substantially enhanced resting-state network detectability and functional connectivity sensitivity. In cardiac imaging, MR KLEAN reduced noise and improved delin-eation of fine anatomical features while preserving temporal fidelity across cardiac phases. ConclusionMR KLEAN provides robust, acquisition-and reconstruction-agnostic k-space denoising, improving image quality and allowing flexible spatial-temporal trade-offs. Results further support that high-dimensional k-space data retain intrinsic low-rank structure analogous to image space despite temporal signal variations.