Computed Tomography (CT) is one of the most important imaging modalities in the medical domain. Ongoing demand for reduction of the X-ray radiation dose and advanced reconstruction algorithms induce ultra-low dose CT acquisitions more and more. However, though advanced reconstructions lead to improved image quality, the ratio between electronic detector noise and incoming signal decreases in ultra-low dose scans causing a degradation of the image quality and, therefore, building a boundary for radiation dose reduction. Future generations of CT scanners may allow sparse sampled data acquisitions, where the source can be switched on and off at any source position. Sparse sampled CT acquisitions could reduce photon starvation in ultra-low dose scans by distributing the energy of skipped projections to the remaining ones. In this work, we simulated sparse sampled CT acquisitions from clinical projection raw data and evaluated the diagnostic value of the reconstructions compared to conventional CT. Therefore, we simulated radiation dose reduction with different degrees of sparse sampling and with a tube current simulator. Up to four experienced radiologists rated the diagnostic quality of each dataset. By a dose reduction to 25% of the clinical dosage, images generated with 4-times sparse sampling – meaning a gap of three projections between two sampling positions – were consistently rated as diagnostic, while about 20% of the ratings for conventional CT were non-diagnostic. Therefore, our data give an initial indication that with sparse sampling a reduction to 25% of the clinical dose is feasible without loss of diagnostic value.