Breast cancer is the most commonly diagnosed cancer in women globally [1]. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which assesses both lesion morphology and enhancement kinetics, plays a crucial role in managing patients with breast cancer [[2], [3], [4]]. DCE-MRI pharmacokinetic analysis, which measures the density, permeability, and leakage space of tissues, can improve the characterization of breast cancer and assessment of tumor response to chemotherapy [[5], [6], [7]]. The 2-compartment model is widely used to fit DCE-MRI data and extract quantitative parameters, including the forward volume transfer constant (Ktrans), reverse volume transfer constant (kep), and fraction of extravascular extracellular space (ve). However, implementing DCE pharmacokinetic analysis in clinical applications poses challenges, particularly in obtaining bilateral acquisitions with high spatio-temporal resolution and requiring an arterial input function (AIF) [8,9]. The main reason for acquiring high–temporal resolution data is to accurately characterize changes in the contrast agent concentration in the blood plasma, known as the AIF.

Ultrafast DCE (UF-DCE) MRI is a new approach to provide ultra-high temporal resolution while preserving spatial resolution in breast imaging. Fast acquisition techniques in UF-DCE MRI including sophisticated parallel imaging, view sharing, and compressed sensing have been used [10,11]. At present, there is no standard protocol for UF-DCE MRI and quantitative parameters [12].

Population-averaged AIFs are often used instead of individual AIFs due to difficulties in obtaining high-quality individual AIFs from every participant at every time point. Previous studies have compared DCE pharmacokinetic parameters using population-averaged AIFs and various distinct approaches for measuring individual AIFs in varying regions, such as osteosarcomas [13], breast [14,15], head and neck [16,17], and prostate [18,19]. Also, no substantial differences were observed in the quantitative parameters derived from these two methods.

The influence of temporal resolution on pharmacokinetic parameters in breast DCE-MRI has been explored in prior work [20]. However, prior work did not include ultrafast acquisitions below 10 s or evaluate diagnostic performance across a wide range of temporal resolutions. In this study, we aimed to evaluate the influence of temporal resolution on pharmacokinetic parameters derived from UF-DCE MRI using a population-averaged AIF. We also investigated the diagnostic performance of these parameters across nigh temporal resolutions in diagnosing breast cancer.