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Parallel imaging is a widely-used technique to accelerate magnetic resonanceimaging (MRI). However, current methods still perform poorly in reconstructingartifact-free MRI images from highly undersampled k-space data. Recently,implicit neural representation (INR) has emerged as a new deep learningparadigm for learning the internal continuity of an object. In this study, weadopted INR to parallel MRI reconstruction. The MRI image was modeled as acontinuous function of spatial coordinates. This function was parameterized bya neural network and learned directly from the measured k-space itself withoutadditional fully sampled high-quality training data. Benefitting from thepowerful continuous representations provided by INR, the proposed methodoutperforms existing methods by suppressing the aliasing artifacts and noise,especially at higher acceleration rates and smaller sizes of theauto-calibration signals. The high-quality results and scanning specificitymake the proposed method hold the potential for further accelerating the dataacquisition of parallel MRI.

A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation