Neurons within early visual cortex are selective for basic image statistics, including spatial frequency. However, these neurons are thought to act as band-pass filters, with the window of spatial frequency sensitivity varying across the visual field and across visual areas. Although a handful of previous functional MRI studies have examined human spatial frequency sensitivity using conventional designs and analysis methods, these measurements are time consuming and fail to capture the precision of spatial frequency tuning (bandwidth). In this study, we introduce a model-driven approach to fMRI analyses that allows for fast and efficient estimation of popu- lation spatial frequency tuning (pSFT) for individual voxels. Blood oxygen level-dependent (BOLD) responses within early visual cortex were acquired while subjects viewed a series of full-field stimuli that swept through a large range of spatial frequency content. Each stimulus was generated by band-pass filtering white noise with a central frequency that changed periodically between a minimum of 0.5 cycles/degree (cpd) and a maximum of 12 cpd. To estimate the underlying frequency tuning of each voxel, we assumed a log- Gaussian pSFT and optimized the parameters of this function by comparing our model output against the measured BOLD time series. Consistent with previous studies, our results show that an increase in eccentricity within each visual area is accompanied by a drop in the peak spatial frequency of the pSFT. Moreover, we found that pSFT bandwidth depends on eccentricity and is correlated with the pSFT peak; populations with lower peaks possess broader bandwidths in logarithmic scale, whereas in linear scale this relationship is reversed.