Nonuniform Fourier-decomposition MRI for ventilation- and perfusion-weighted imaging of the lung

Abstract

Purpose: To improve the robustness of pulmonary ventilation- and perfusion-weighted imaging with Fourier decomposition (FD) MRI in the presence of respiratory and cardiac frequency variations by replacing the standard fast Fourier transform with the more general nonuniform Fourier transform.

Theory and methods: Dynamic coronal single-slice MRI of the thorax was performed in 11 patients and 5 healthy volunteers on a 1.5T whole-body scanner using a 2D ultra-fast balanced steady-state free-precession sequence with temporal resolutions of 4-9 images/s. For the proposed nonuniform Fourier-decomposition (NUFD) approach, the original signal with variable physiological frequencies that was acquired with constant sampling rate was retrospectively transformed into a signal with (ventilation or perfusion) frequency-adapted sampling rate. For that purpose, frequency tracking was performed with the synchro-squeezed wavelet transform. Ventilation- and perfusion-weighted NUFD amplitude and signal delay maps were generated and quantitatively compared with regularly sampled FD maps based on their signal-to-noise ratio (SNR).

Results: Volunteers and patients showed statistically significant increases of SNR in frequency-adapted NUFD results compared to regularly sampled FD results. For ventilation data, the mean SNR increased by $43.4\%\pm 25.3\%$ and $24.4\%\pm 31.9\%$ in volunteers and patients, respectively; for perfusion data, SNR increased by $93.0\%\pm 36.1\%$ and $75.6\%\pm 62.8\%$ . Two patients showed perfusion signal in pulmonary areas with NUFD that could not be imaged with FD.

Conclusion: This study demonstrates that using nonuniform Fourier transform in combination with frequency tracking can significantly increase SNR and reduce frequency overlaps by collecting the signal intensity onto single frequency bins.

Keywords: Fourier decomposition; lung; nonuniform Fourier transform; pulmonary MRI.

Figure 1

Constant and frequency‐adapted sampling rate. A, Variable‐frequency signal with constant sampling rate; the frequency varies every signal cycle. B, Constant‐frequency signal with variable sampling rates; the same signal as above has been transformed into a constant‐frequency signal by appropriately modifying the sampling rates

Figure 2

Workflow of NUFD MRI image processing. A, Extraction of ventilation and perfusion signal from large ROI by band‐pass filtering. B, Resulting time‐frequency map from SWT with tracked frequencies from ridge detection. C, Ventilation and perfusion signals displayed with recalculated sampling points (frequency variations are reduced in comparison to A. D, Resulting NUFFT spectra from the curves in C, showing that intensity has indeed been collected onto a single frequency bin for both ventilation and perfusion

Figure 3

Frequency spectra from NUFD (A,C) and FD (B,D) MRI based on extracted and averaged ROI signal for ventilation (A,B) and perfusion (C,D) component

Figure 4

SNR maps generated from NUFD and regularly sampled FD spectra in a healthy volunteer. A,B, Ventilation (V) SNR maps displaying average SNRs of 195.3 and 144.2, respectively, corresponding to a +35.4% increase of SNR. C,D, Perfusion (Q) SNR maps displaying average SNRs of 80 and 39 corresponding to a +106% increase of SNR

Figure 5

Influence of frequency variability. A‐H, Ventilation‐weighted (V) amplitude and signal delay maps from 2 measurements; the standard deviations of the ventilation frequencies were 0.01 Hz and 0.11 Hz, respectively. I‐P, Perfusion‐weighted (Q) amplitude and signal delay maps from 2 measurements; the standard deviations of cardiac frequencies were 0.04 Hz and 0.03 Hz, respectively

Figure 6

Perfusion‐weighted NUFD and FD maps of patients with suspected PAH and CTEPH. A,B,F,G, FD and NUFD perfusion‐weighted amplitude maps. D,E,I,J, Perfusion time delay maps. C,H, These 2 patients also had iodine‐enhanced dual‐energy CT pulmonary angiogram (CTPA) measurements (100/140Sn kV, 165/140 mAref, pitch = 1.2 for PAH patient and 90/150Sn kV, 60/46 mAref, pitch = 1.2 for CTEPH patient) performed as part of clinical routine within 3 months of their MR scans. The comparison shows that perfusion signal improvements from NUFD coincides better with displayed iodine concentration in both CTPA images. For the CTEPH patient, the CTPA image displays the decreasing, yet still existing signal intensity in the upper and lower part of the right lung where the lower part is stronger than the upper. This coincides better with the NUFD perfusion amplitude map than the one generated with FD