LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping

Spiral cardiac quantitative susceptibility mapping for differential cardiac chamber oxygenation-Initial validation in relation to invasive blood sampling

PURPOSE

To develop a breath-hold cardiac quantitative susceptibility mapping (QSM) sequence for noninvasive measurement of differential cardiac chamber blood oxygen saturation (ΔSO2).

METHODS

A non-gated three-dimensional stack-of-spirals QSM sequence was implemented to continuously sample the data throughout the cardiac cycle. Measurements of ΔSO2 between the right and left heart chamber obtained by the proposed sequence and a previously validated navigator Cartesian QSM sequence were compared in three cohorts consisting of healthy volunteers, coronavirus disease 2019 survivors, and patients with pulmonary hypertension. In the pulmonary-hypertension cohort, Bland-Altman plots were used to assess the agreement of ΔSO2 values obtained by QSM and those obtained by invasive right heart catheterization (RHC).

RESULTS

Compared with navigator QSM (average acquisition time 419 ± 158 s), spiral QSM reduced the scan time on average by over 20-fold to a 20-s breath-hold. In all three cohorts, spiral QSM and navigator QSM yielded similar ΔSO2. Among healthy volunteers and coronavirus disease 2019 survivors, ΔSO2 was 17.41 ± 4.35% versus 17.67 ± 4.09% for spiral and navigator QSM, respectively. In pulmonary-hypertension patients, spiral QSM showed a slightly smaller ΔSO2 bias and narrower 95% limits of agreement than that obtained by navigator QSM (1.09% ± 6.47% vs. 2.79% ± 6.99%) when compared with right heart catheterization.

CONCLUSION

Breath-hold three-dimensional spiral cardiac QSM for measuring differential cardiac chamber blood oxygenation is feasible and provides values in good agreement with navigator cardiac QSM and with reference right heart catheterization.

Validation of deep-learning accelerated quantitative susceptibility mapping for deep brain nuclei

Purpose

To test the feasibility and consistency of a deep-learning (DL) accelerated QSM method for deep brain nuclei evaluation.

Methods

Participants were scanned with both parallel imaging (PI)-QSM and DL-QSM methods. The PI- and DL-QSM scans had identical imaging parameters other than acceleration factors (AF). The DL-QSM employed Poisson disk style under-sampling scheme and a previously developed cascaded CNN based reconstruction model, with acquisition time of 4:35, 3:15, and 2:11 for AF of 3, 4, and 5, respectively. For PI-QSM acquisition, the AF was 2 and the acquisition time was 6:46. The overall image similarity was assessed between PI- and DL-QSM images using the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). QSM values from 7 deep brain nuclei were extracted and agreements between images with different Afs were assessed. Finally, the correlations between age and QSM values in the selected deep brain nuclei were evaluated.

Results

59 participants were recruited. Compared to PI-QSM images, the mean SSIM of DL images were 0.87, 0.86, and 0.85 for AF of 3, 4, and 5. The mean PSNR were 44.56, 44.53, and 44.23. Susceptibility values from DL-QSM were highly consistent with routine PI-QSM images, with differences of less than 5% at the group level. Furthermore, the associations between age and QSM values could be consistently revealed.

Conclusion

DL-QSM could be used for measuring susceptibility values of deep brain nucleus. An AF up to 5 did not significantly impact the correlation between age and susceptibility in deep brain nuclei.

Trajectories and sex differences of brain structure, oxygenation and perfusion functions in normal aging

BACKGROUND

Brain structure, oxygenation and perfusion are important factors in aging. Coupling between regional cerebral oxygen consumption and perfusion also reflects functions of neurovascular unit (NVU). Their trajectories and sex differences during normal aging important for clinical interpretation are still not well defined. In this study, we aim to investigate the relationship between brain structure, functions and age, and exam the sex disparities.

METHOD

A total of 137 healthy subjects between 20∼69 years old were enrolled with conventional MRI, structural three-dimensional T1-weighted imaging (3D-T1WI), 3D multi-echo gradient echo sequence (3D-mGRE), and 3D pseudo-continuous arterial spin labeling (3D-pCASL). Oxygen extraction fraction (OEF) and cerebral blood flow (CBF) were respectively reconstructed from 3D-mGRE and 3D-pCASL images. Cerebral metabolic rate of oxygen (CMRO2) were calculated as follows: CMRO2=CBF·OEF·[H]a, [H]a=7.377 μmol/mL. Brains were segmented into global gray matter (GM), global white matter (WM), and 148 cortical subregions. OEF, CBF, CMRO2, and volumes of GM/WM relative to intracranial volumes (rel_GM/rel_WM) were compared between males and females. Generalized additive models were used to evaluate the aging trajectories of brain structure and functions. The coupling between OEF and CBF was analyzed by correlation analysis. P or PFDR < 0.05 was considered statistically significant.

RESULTS

Females had larger rel_GM, higher CMRO2 and CBF of GM/WM than males (P < 0.05). With control of sex, CBF of GM significantly declined between 20 and 32 years, CMRO2 of GM declined subsequently from 33 to 41 years and rel_GM decreased significantly at all ages (R2 = 0.27, P < 0.001; R2 = 0.17, P < 0.001; R2 = 0.52, P < 0.001). In subregion analysis, CBF declined dispersedly while CMRO2 declined widely across most subregions of the cortex during aging. Robust negative coupling between OEF and CBF was found in most of the subregions (r range = -0.12∼-0.48, PFDR < 0.05).

CONCLUSION

The sex disparities, age trajectories of brain structure and functions as well as the coupling of NVU in healthy individuals provide insights into normal aging which are potential targets for study of pathological conditions.

DF-QSM: Data Fidelity based Hybrid Approach for Improved Quantitative Susceptibility Mapping of the Brain

Quantitative Susceptibility Mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique to quantify the magnetic susceptibility of the tissue under investigation. Deep learning methods have shown promising results in deconvolving the susceptibility distribution from the measured local field obtained from the MR phase. Although existing deep learning based QSM methods can produce high-quality reconstruction, they are highly biased toward training data distribution with less scope for generalizability. This work proposes a hybrid two-step reconstruction approach to improve deep learning based QSM reconstruction. The susceptibility map prediction obtained from the deep learning methods has been refined in the framework developed in this work to ensure consistency with the measured local field. The developed method was validated on existing deep learning and model-based deep learning methods for susceptibility mapping of the brain. The developed method resulted in improved reconstruction for MRI volumes obtained with different acquisition settings, including deep learning models trained on constrained (limited) data settings.

mcLARO: Multi-contrast learned acquisition and reconstruction optimization for simultaneous quantitative multi-parametric mapping

PURPOSE

To develop a method for rapid sub-millimeter T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan using multi-contrast learned acquisition and reconstruction optimization (mcLARO).

METHODS

A pulse sequence was developed by interleaving inversion recovery and T2 magnetization preparations and single-echo and multi-echo gradient echo acquisitions, which sensitized k-space data to T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and magnetic susceptibility. The proposed mcLARO optimized both the multi-contrast k-space under-sampling pattern and image reconstruction based on image feature fusion using a deep learning framework. The proposed mcLARO method withR = 8 $$ R=8 $$ under-sampling was validated in a retrospective ablation study and compared with other deep learning reconstruction methods, including MoDL and Wave-MoDL, using fully sampled data as reference. Various under-sampling ratios in mcLARO were investigated. mcLARO was also evaluated in a prospective study using separately acquired conventionally sampled quantitative maps as reference standard.

RESULTS

The retrospective ablation study showed improved image sharpness of mcLARO compared to the baseline network without the multi-contrast sampling pattern optimization or image feature fusion module. The under-sampling ratio experiment showed that higher under-sampling ratios resulted in blurrier images and lower precision of quantitative values. The prospective study showed that small or negligible bias and narrow 95% limits of agreement on regional T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM values by mcLARO (5:39 mins) compared to reference scans (40:03 mins in total). mcLARO outperformed MoDL and Wave-MoDL in terms of image sharpness, noise suppression, and artifact removal.

CONCLUSION

mcLARO enabled fast sub-millimeter T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan.