Dynamic contrast enhanced (DCE) MRI is becoming an increasingly important tool for assessing tumour response. Important characteristics are spatial and temporal resolution and in lung this is further complicated by the effects of respiratory motion. A common approach is to acquire fast gradient-echo image utilising k-space sharing to provide accelerated temporal resolution and collect data during short ‘windows’ of breath-holds over the time course. However patient compliance during breath-hold manoeuvres can lead to tumour displacement and introduce error in analysis. Radial acquisitions can alleviate motion by oversampling the centre of k-space albeit with reduced temporal resolution. The purpose of this study was to evaluate whether such a ‘stack-of-stars’ acquisition can achieve high enough resolution for the DCE sequence to provide a complete free breathing protocol for lung cancer patients. Institutional review board approval was obtained. All images were acquired on a wide bore 3 Tesla system (Skyra, Siemens, Erlangen Germany) utilising an 18 channel surface coil and 32 channel spine coil. Breath hold (BH) DCE sequence was performed using a fast gradient-echo sequence employing k-space sharing (TWIST) acquired as 5 breath-hold periods of 20 seconds each with a spatial and temporal resolution of 1.5 x 1.5mm and 3 seconds. The free breathing (FB) protocol was performed using a radial stack-of-stars acquisition (StarVIBE) with a spatial and temporal resolution of 1.8 x 1.8mm and 14 seconds respectively. An in-house developed MR compatible motion phantom was first used to quantify motion sensitivity and signal variation in the two sequences. Image quality was subsequently assessed in vivo by imaging a healthy volunteer and subtracting sequential dynamic frames for both techniques. The final sequence was tested using contrast enhancement on a patient with lung cancer and compared to a patient using the previous breath hold technique. The DCE datasets were acquired for a period of 6minutes for both sequences. For in-vivo scans, two rapid pre-contrast measurement of T1 was acquired using two flip angle variations (2 o and 15o) of each specific sequence was acquired. Calculation of T1 map and a two-compartment model fit to the data (Tissue4D, Siemens) was done to provide pixel-by-pixel maps of the perfusion rate constant. Image quality was reviewed by a thoracic radiologist based on a 4 point scale (1, excellent to 4, poor) for volunteer and patient scans. Phantom data demonstrated a reduction in signal variation from 40% using TWIST compared to 4% using StarVIBE for the same amplitude and frequency of motion. The volunteer images showed the impact of respiratory motion to be larger during the TWIST acquisitions versus StarVIBE (Fig1). The optimum StarVIBE protocol used 300 radial views. Figure 2 shows images and analysis taken from patient DCE scans. Viewing DCE data in a cine loop revealed large movement between frames for TWIST compared to StarVIBE. A comparison of signal-time plots shows a typical result where failure to maintain and reproduce breath hold has produced large variation and discontinuities in the dataset. As a result the goodness-of-fit (chi2) was better for StarVIBE (0.2) than the corresponding value using TWIST (0.16). Although temporal resolution is much poorer with the StarVIBE sequence, it was sufficient to sample the early upslope phase of the contrast agent. Overall the StarVibe sequence performed better for both patient and volunteer scans (Fig 3).These initial results show that use of a radial k-space trajectory as a method of motion compensation provides a DCE scan of sufficient image quality and temporal resolution which can be used as part of a complete free breathing lung MRI protocol.