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Sampling k-space asymmetrically (ie, partial Fourier sampling) in the readout direction is a common way to reduce the echo time (TE) during magnetic resonance image acquisitions. This technique requires overlap around the center of k-space to provide a calibration region for reconstruction, which limits the minimum fractional echo to ~60% before artifacts are observed. The present study describes a method for reconstructing images from exact half echoes using two separate acquisitions with reversed readout polarity, effectively providing a full line of k-space without additional data around central k-space. This approach can benefit sequences or applications that prioritize short TE, short inter-echo spacing or short repetition time. An example of the latter is demonstrated to reduce banding artifacts in balanced steady-state free precession.

The aim of this study was to develop a deep neural network for respiratory motion compensation in free-breathing cine MRI and evaluate its performance. An adversarial autoencoder network was trained using unpaired training data from healthy volunteers and patients who underwent clinically indicated cardiac MRI examinations. A U-net structure was used for the encoder and decoder parts of the network and the code space was regularized by an adversarial objective. The autoencoder learns the identity map for the free-breathing motion-corrupted images and preserves the structural content of the images, while the discriminator, which interacts with the output of the encoder, forces the encoder to remove motion artifacts. The network was first evaluated based on data that were artificially corrupted with simulated rigid motion with regard to motion-correction accuracy and the presence of any artificially created structures. Subsequently, to demonstrate the feasibility of the proposed approach in vivo, our network was trained on respiratory motion-corrupted images in an unpaired manner and was tested on volunteer and patient data. In the simulation study, mean structural similarity index scores for the synthesized motion-corrupted images and motion-corrected images were 0.76 and 0.93 (out of 1), respectively. The proposed method increased the Tenengrad focus measure of the motion-corrupted images by 12% in the simulation study and by 7% in the in vivo study. The average overall subjective image quality scores for the motion-corrupted images, motion-corrected images and breath-held images were 2.5, 3.5 and 4.1 (out of 5.0), respectively. Nonparametric-paired comparisons showed that there was significant difference between the image quality scores of the motion-corrupted and breath-held images (P < .05); however, after correction there was no significant difference between the image quality scores of the motion-corrected and breath-held images. This feasibility study demonstrates the potential of an adversarial autoencoder network for correcting respiratory motion-related image artifacts without requiring paired data.

The divided subtracted inversion recovery (dSIR) is a high T1 contrast technique that shows changes in white matter in patients with traumatic brain injury and hypoxic injury. The changes can be explained by small differences in T1; however, to date, there has been no independent validation of the technique using a standard reference. The present study develops the theory of the dSIR signal and performs validation using the NIST/ISMRM T1 phantom. Non‐idealities are explored, including the influence of noise bias and finite repetition time (TR), which leads to the introduction of an optimally efficient TR for inversion recovery acquisitions. Results show excellent agreement with theoretical calculations. [ABSTRACT FROM AUTHOR]

Purpose: Balanced steady‐state free precession (bSSFP) imaging is susceptible to outflow effects where excited spins leaving the slice as part of the blood stream are misprojected back onto the imaging plane. Previous work proposed using slice‐encoding steps to localize these outflow effects from corrupting the target slice, at the expense of prolonged scan time. This present study extends this idea by proposing a means of significantly reducing most of the outflowing signal from the imaged slice using a coil localization method that acquires a slice‐encoded calibration scan in addition to the 2D data, without being nearly as time‐demanding as our previous method. This coil localization method is titled UNfolding Coil Localized Errors from an imperfect slice profile using a Structured Autocalibration Matrix (UNCLE SAM). Methods: Retrospective and prospective evaluations were carried out. Both featured a 2D acquisition and a separate slice‐encoded calibration of the center in‐plane k‐space lines across all desired slice‐encoding steps. Results: Retrospective results featured a slice‐by‐slice comparison of the slice‐encoded images with UNCLE SAM. UNCLE SAM's subtraction from the slice‐encoded image was compared with a subtraction from the flow‐corrupted 2D image, to demonstrate UNCLE SAM's capability to unfold outflowing spins. UNCLE SAM's comparison with slice encoding showed that UNCLE SAM was able to unfold up to 74% of what slice encoding achieved. Prospective results showed significant reduction in outflow effects with only a marginal increase in scan time from the 2D acquisition. Conclusions: We developed a method that effectively unfolds most outflowing spins from corrupting the target slice and does not require the explicit use of slice‐encoding gradients. This development offers a method to reduce most outflow effects from the target slice within a clinically feasible scan duration compared with the fully sampled slice‐encoding technique. [ABSTRACT FROM AUTHOR]

The purpose of this work was to investigate sources of bias in magnetic resonance imaging (MRI) liver fat quantification that lead to a dependence of the proton density fat fraction (PDFF) on the number of echoes. This was a retrospective analysis of liver MRI data from 463 subjects. The magnitude signal variation with TE from spoiled gradient echo images was curve fitted to estimate the PDFF using a model that included monoexponential R2 * decay and a multi-peak fat spectrum. Additional corrections for non-exponential decay (Gaussian), bi-exponential decay, degree of fat saturation, water frequency shift and noise bias were introduced. The fitting error was minimized with respect to 463 × 3 = 1389 subject-specific parameters and seven additional parameters associated with these corrections. The effect on PDFF was analyzed, notably the dependence on the number of echoes. The effects on R2 * were also analyzed. The results showed that the inclusion of bias corrections resulted in an increase in the quality of fit (r2 ) in 427 of 463 subjects (i.e. 92.2%) and a reduction in the total fitting error (residual norm) of 43.6%. This was largely a result of the Gaussian decay (57.8% of the reduction), fat spectrum (31.0%) and biexponential decay (8.8%) terms. The inclusion of corrections was also accompanied by a decrease in the dependence of PDFF on the number of echoes. Similar analysis of R2 * showed a decrease in the dependence on the number of echoes. Comparison of PDFF with spectroscopy indicated excellent agreement before and after correction, but the latter exhibited lower bias on a Bland-Altman plot (1.35% versus 0.41%). In conclusion, correction for known and expected biases in PDFF quantification in liver reduces the fitting error, decreases the dependence on the number of echoes and increases the accuracy.

A theoretical triglyceride model was developed for in vivo human liver fat 1H MRS characterization, using the number of double bonds (CHCH), number of methylene-interrupted double bonds (CHCHCH2CHCH) and average fatty acid chain length. Five 3 T, single-voxel, stimulated echo acquisition mode spectra (STEAM) were acquired consecutively at progressively longer TEs in a fat-water emulsion phantom and in 121 human subjects with known or suspected nonalcoholic fatty liver disease. T2-corrected peak areas were calculated. Phantom data were used to validate the model. Human data were used in the model to determine the complete liver fat spectrum. In the fat-water emulsion phantom, the spectrum predicted by the model (based on known fatty acid chain distribution) agreed closely with spectroscopic measurement. In human subjects, areas of CH2 peaks at 2.1 and 1.3 ppm were linearly correlated (slope, 0.172; r = 0.991), as were the 0.9 ppm CH3 and 1.3 ppm CH2 peaks (slope, 0.125; r = 0.989). The 2.75 ppm CH2 peak represented 0.6% of the total fat signal in high-liver-fat subjects. These values predict that 8.6% of the total fat signal overlies the water peak. The triglyceride model can characterize human liver fat spectra. This allows more accurate determination of liver fat fraction from MRI and MRS. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The divided subtracted inversion recovery (dSIR) is a high T 1 contrast technique that shows changes in white matter in patients with traumatic brain injury and hypoxic injury. The changes can be explained by small differences in T 1 ; however, to date, there has been no independent validation of the technique using a standard reference. The present study develops the theory of the dSIR signal and performs validation using the NIST/ISMRM T 1 phantom. Non-idealities are explored, including the influence of noise bias and finite repetition time (TR), which leads to the introduction of an optimally efficient TR for inversion recovery acquisitions. Results show excellent agreement with theoretical calculations., (© 2024 John Wiley & Sons Ltd.)

A theoretical triglyceride model was developed for in vivo human liver fat (1) H MRS characterization, using the number of double bonds (-CH=CH-), number of methylene-interrupted double bonds (-CH=CH-CH(2)-CH=CH-) and average fatty acid chain length. Five 3 T, single-voxel, stimulated echo acquisition mode spectra (STEAM) were acquired consecutively at progressively longer TEs in a fat-water emulsion phantom and in 121 human subjects with known or suspected nonalcoholic fatty liver disease. T(2)-corrected peak areas were calculated. Phantom data were used to validate the model. Human data were used in the model to determine the complete liver fat spectrum. In the fat-water emulsion phantom, the spectrum predicted by the model (based on known fatty acid chain distribution) agreed closely with spectroscopic measurement. In human subjects, areas of CH(2) peaks at 2.1 and 1.3 ppm were linearly correlated (slope, 0.172; r = 0.991), as were the 0.9 ppm CH(3) and 1.3 ppm CH(2) peaks (slope, 0.125; r = 0.989). The 2.75 ppm CH(2) peak represented 0.6% of the total fat signal in high-liver-fat subjects. These values predict that 8.6% of the total fat signal overlies the water peak. The triglyceride model can characterize human liver fat spectra. This allows more accurate determination of liver fat fraction from MRI and MRS., (Copyright © 2010 John Wiley & Sons, Ltd.)