Sparse　sampling　is　an　effective　strategy　for　accelerating　the　acquisition　of　multi-dimensional　magnetic　resonance　spectroscopy　(MRS),　crucial　in　disciplines　such　as　chemistry　and　structural　biology.　The　state-of-the-art　low-rank　reconstruction　methods　enable　the　high-fidelity　recovery　of　sparsely-sampled　MRS　but　are　limited　by　lengthy　reconstruction　times,　posing　a　significant　challenge.　In　this　work,　we　introduce　a　novel　approach　that　significantly　reduces　the　dimensionality　of　the　constructed　low-rank　Hankel-like　matrix.　This　reduction　leads　to　lower　computational　complexity　and,　as　a　result,　a　substantial　acceleration　in　reconstruction　times　compared　to　conventional　low-rank　methods.　Experimental　evaluations　on　both　simulated　and　real　MRS　demonstrate　that　our　method　achieves　a　reduction　in　reconstruction　times　by　over　fourfold　without　sacrificing　the　quality　of　spectrum　reconstructions.　©　2024　IEEE.