Impact of undersampling on preclinical lung T2* mapping with 3D radial UTE MRI at 7 T

Lung diseases are almost invariably heterogeneous and progressive, making it imperative to capture temporally and spatially explicit information to understand the disease initiation and progression. Imaging the lung with MRI—particularly in the preclinical setting—has historically been challenging because of relatively low lung tissue density, rapid cardiac and respiratory motion, and rapid transverse (T₂*) relaxation. These limitations can largely be mitigated using ultrashort-echo-time (UTE) sequences, which are intrinsically robust to motion and avoid significant T₂* decay. A significant disadvantage of common radial UTE sequences is that they require inefficient, center-out k-space sampling, resulting in long acquisition times relative to conventional Cartesian sequences. Therefore, pulmonary images acquired with radial UTE are often undersampled to reduce acquisition time. However, undersampling reduces image SNR, introduces image artifacts, and degrades true image resolution. The level of undersampling is further increased if offline gating techniques like retrospective gating are employed, because only a portion (∼40–50%) of the data is used in the final image reconstruction. Here, we explore the impact of undersampling on SNR and T₂* mapping in mouse lung imaging using simulation and in-vivo data. Increased scatter in both metrics was noticeable at around 50% sampling. Parenchymal apparent SNR only decreased slightly (average decrease ∼ 1.4) with as little as 10% sampling. Apparent T₂* remained similar across undersampling levels, but it became significantly increased (p < 0.05) below 80% sampling. These trends suggest that undersampling can generate quantifiable, but moderate changes in the apparent value of T₂*. Moreover, these approaches to assess the impact of undersampling are straightforward to implement and can readily be expanded to assess the quantitative impact of other MR acquisition and reconstruction parameters.