Data acquisition is an important step in magnetic resonance imaging (MRI). Acquisition time and image quality heavily depend on how we sample k-space and how we reconstruct images from the acquired data. In other words, data acquisition and image reconstruction are inseparable. Usually, new imaging theory raises the request on novel sampling scheme. The first part of this talk will briefly review traditional data acquisition connected with traditional reconstruction methods. The second part of the talk will discuss non-traditional acquisition motivated by advanced reconstruction ideas such as compressed sensing (CS), low-rank and deep learning based methods.