The purpose of this study is to investigate the feasibility of using internal respiratory (IR) surrogates to sort four-dimensional (4D) magnetic resonance (MR) images. The 4D MR images were constructed by acquiring fast 2D cine MR images sequentially, with each slice scanned for more than one breathing cycle. The 4D volume was then sorted retrospectively using the IR signal. In this study, we propose to use multiple low-frequency components in the Fourier space as well as the anterior body boundary as potential IR surrogates. From these potential IR surrogates, we used a clustering algorithm to identify those that best represented the respiratory pattern to derive the IR signal. A study with healthy volunteers was performed to assess the feasibility of the proposed IR signal. We compared this proposed IR signal with the respiratory signal obtained using respiratory bellows. Overall, 99% of the IR signals matched the bellows signals. The average difference between the end inspiration times in the IR signal and bellows signal was 0.18 s in this cohort of matching signals. For the acquired images corresponding to the other 1% of non-matching signal pairs, the respiratory motion shown in the images was coherent with the respiratory phases determined by the IR signal, but not the bellows signal. This suggested that the IR signal determined by the proposed method could potentially correct the faulty bellows signal. The sorted 4D images showed minimal mismatched artefacts and potential clinical applicability. The proposed IR signal therefore provides a feasible alternative to effectively sort MR images in 4D.