Numerical fitting of Extrapolated semisolid Magnetization transfer Reference signals: Improved detection of ischemic stroke

Learning-based motion artifact correction in the Z-spectral domain for chemical exchange saturation transfer MRI

PURPOSE

To develop and evaluate a physics-driven, saturation contrast-aware, deep-learning-based framework for motion artifact correction in CEST MRI.

METHODS

A neural network was designed to correct motion artifacts directly from a Z-spectrum frequency (Ω) domain rather than an image spatial domain. Motion artifacts were simulated by modeling 3D rigid-body motion and readout-related motion during k-space sampling. A saturation-contrast-specific loss function was added to preserve amide proton transfer (APT) contrast, as well as enforce image alignment between motion-corrected and ground-truth images. The proposed neural network was evaluated on simulation data and demonstrated in healthy volunteers and brain tumor patients.

RESULTS

The experimental results showed the effectiveness of motion artifact correction in the Z-spectrum frequency domain (MOCOΩ) compared to in the image spatial domain. In addition, a temporal convolution applied to a dynamic saturation image series was able to leverage motion artifacts to improve reconstruction results as a denoising process. The MOCOΩ outperformed existing techniques for motion correction in terms of image quality and computational efficiency. At 3 T, human experiments showed that the root mean squared error (RMSE) of APT images decreased from 4.7% to 2.1% at 1 μT and from 6.2% to 3.5% at 1.5 μT in case of "moderate" motion and from 8.7% to 2.8% at 1 μT and from 12.7% to 4.5% at 1.5 μT in case of "severe" motion, after motion artifact correction.

CONCLUSION

The MOCOΩ could effectively correct motion artifacts in CEST MRI without compromising saturation transfer contrast.

Prospective acceleration of whole-brain CEST imaging by golden-angle view ordering in Cartesian coordinates and joint k-space and image-space parallel imaging (KIPI)

PURPOSE

To prospectively accelerate whole-brain CEST acquisition by joint k-space and image-space parallel imaging (KIPI) with a proposed golden-angle view ordering technique (GAVOT) in Cartesian coordinates.

THEORY AND METHODS

The T2-decay effect will vary across frames with variable acceleration factors (AF) in the prospective acquisition using sequences with long echo trains. The GAVOT method uses a subset strategy to eliminate the T2-decay inconsistency, where all frames use a subset of shots from the calibration frame to form their k-space view ordering. The golden-angle rule is adapted to ensure uniform k-space coverage for arbitrary AFs in Cartesian coordinates. Phantom and in vivo studies were conducted on a 3 T scanner.

RESULTS

The GAVOT view ordering yielded a higher g-factor than conventional uniformly centric ordering, whereas the noise propagation in amide proton transfer (APT) weighted images was similar between different view ordering. Compared to centric ordering, GAVOT successfully eliminated the T2-decay inconsistency across all frames, resulting in fewer image artifacts for both KIPI and conventional parallel imaging methods. The synergy of GAVOT and KIPI mitigated strong aliasing artifacts and achieved high-quality reconstruction of prospective variable-AF datasets. GAVOT-KIPI reduced the scan time to 2.1 min for whole-brain APT weighted imaging and 4.7 min for quantitative APT signal (APT#) mapping.

CONCLUSION

GAVOT makes the prospective variable AF strategy flexible and practical, and, in conjunction with KIPI, ensures high-quality reconstruction from highly undersampled data, facilitating the clinical translation of whole-brain CEST imaging.

Amide proton transfer MRI may reflect effective reperfusion and predict functional outcomes in patients with ischemic stroke

Perfusion imaging is useful to assess tissue recovery in patients with acute ischemic stroke (AIS); however, it cannot reflect tissue metabolism. We postulated that amide proton transfer (APT) imaging can characterize the tissue status after reperfusion therapy, thus providing prognostic value for 90-day functional outcomes. We included 63 patients with AIS and large-vessel occlusion (LVO). The APT signals, including APT# and NOE# (nuclear Overhauser enhancement) were quantified. Ischemic lesions observed on APT# and diffusion-weighted imaging (DWI) were classified according to their mismatch patterns (APT# < DWI; APT# ≥ DWI). Predictors of 90-day good outcomes (modified Rankin scale score 0-2) were evaluated. Patients with successful reperfusion exhibited higher APT#, smaller percentage change of APT#, and a greater likelihood of presenting APT# < DWI compared to those with poor reperfusion (all P < 0.05). The APT# (odds ratio [OR] = 11.48, P = 0.046) and a mismatch pattern of APT# < DWI (OR = 7.41, P = 0.020) independently predicted good outcomes besides the clinical parameters. A mismatch pattern of APT# ≥ DWI was a significant marker of poor outcomes despite successful reperfusion (P = 0.002). Our study provides preliminary evidence that APT may reveal tissue recovery after reperfusion and predict good outcomes at 90 days in patients with AIS and LVO.

Amide Proton Transfer-Weighted MRI, Associations with Clinical Severity and Prognosis in Ischemic Strokes

BACKGROUND

The National Institutes of Health Stroke Scale (NIHSS) and the modified Rankin scale (mRS) scores have important shortcomings. Amide proton transfer-weighted (APTw) imaging might offer more valuable information in ischemic strokes assessment.

PURPOSE

To utilize APTw, apparent diffusion coefficient (ADC), and computed tomography perfusion (CTP) for the assessment of clinical symptom severity and 90-day prognosis in patients diagnosed with ischemic stroke.

STUDY TYPE

Prospective.

SUBJECTS

61 patients (mean age 63.2 ± 9.7 years; 46 males, 15 females) with ischemic strokes were included in the study.

FIELD STRENGTH/SEQUENCE

3T/turbo spin echo (TSE) T1-weighted imaging, T2-weighted imaging, T2-fluid attenuated inversion recovery (T2-FLAIR), diffusion-weighted imaging (DWI), and single-shot TSE APTw imaging.

ASSESSMENT

APTw, ADC, and CTP were used to compare patient subgroups and construct a prognostic nomogram model.

STATISTICAL TESTS

Kolmogorov-Smirnov test, t-test, Mann-Whitney U test, chi-square test, Pearson correlation analysis, multivariate logistic regression analysis, decision curve analysis (DCA), receiver operating characteristic curves (ROCs). The significance threshold was set at P < 0.05.

RESULTS

Correlation analysis revealed that APTw and NIHSS exhibit the highest correlation (r = -0.634, 95% confidence interval [CI] -0.418 to -0.782), surpassing that of ADC and lesion size. Multivariable analysis revealed APTw (odds ratio [OR] 0.905, 95% CI 0.845-0.970), ADC (OR 0.745, 95% CI 0.609-0.911), and infarct core-cerebral blood volume (IC-CBV) (OR 0.547, 95% CI 0.310-0.964) as potential risk factors associated with a poor prognosis. The nomogram model demonstrated the highest predictive efficacy, with an area under the curve (AUC) of 0.960 (95% CI 0.911-0.988), exceeding that of APTw, ADC, and IC-CBV individually.

DATA CONCLUSION

The APTw technique holds potential value in categorizing and managing patients with ischemic stroke, offering guidance for the implementation of clinical treatment strategies.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 2.

Unraveling contributions to the Z-spectrum signal at 3.5 ppm of human brain tumors

PURPOSE

To evaluate the influence of the confounding factors, direct water saturation (DWS), and magnetization transfer contrast (MTC) effects on measured Z-spectra and amide proton transfer (APT) contrast in brain tumors.

METHODS

High-grade glioma patients were scanned using an RF saturation-encoded 3D MR fingerprinting (MRF) sequence at 3 T. For MRF reconstruction, a recurrent neural network was designed to learn free water and semisolid macromolecule parameter mappings of the underlying multiple tissue properties from saturation-transfer MRF signals. The DWS spectra and MTC spectra were synthesized by solving Bloch-McConnell equations and evaluated in brain tumors.

RESULTS

The dominant contribution to the saturation effect at 3.5 ppm was from DWS and MTC effects, but 25%-33% of the saturated signal in the gadolinium-enhancing tumor (13%-20% for normal tissue) was due to the APT effect. The APT# signal of the gadolinium-enhancing tumor was significantly higher than that of the normal-appearing white matter (10.1% vs. 8.3% at 1 μT and 11.2% vs. 7.8% at 1.5 μT).

CONCLUSION

The RF saturation-encoded MRF allowed us to separate contributions to the saturation signal at 3.5 ppm in the Z-spectrum. Although free water and semisolid MTC are the main contributors, significant APT contrast between tumor and normal tissues was observed.

CEST and nuclear Overhauser enhancement imaging with deep learning-extrapolated semisolid magnetization transfer reference: Scan-rescan reproducibility and reliability studies

PURPOSE

To develop a novel MR physics-driven, deep-learning, extrapolated semisolid magnetization transfer reference (DeepEMR) framework to provide fast, reliable magnetization transfer contrast (MTC) and CEST signal estimations, and to determine the reproducibility and reliability of the estimates from the DeepEMR.

METHODS

A neural network was designed to predict a direct water saturation and MTC-dominated signal at a certain CEST frequency offset using a few high-frequency offset features in the Z-spectrum. The accuracy, scan-rescan reproducibility, and reliability of MTC, CEST, and relayed nuclear Overhauser enhancement (rNOE) signals estimated from the DeepEMR were evaluated on numerical phantoms and in heathy volunteers at 3 T. In addition, we applied the DeepEMR method to brain tumor patients and compared tissue contrast with other CEST calculation metrics.

RESULTS

The DeepEMR method demonstrated a high degree of accuracy in the estimation of reference MTC signals at ±3.5 ppm for APT and rNOE imaging, and computational efficiency (˜190-fold) compared with a conventional fitting approach. In addition, the DeepEMR method achieved high reproducibility and reliability (intraclass correlation coefficient = 0.97, intersubject coefficient of variation = 3.5%, and intrasubject coefficient of variation = 1.3%) of the estimation of MTC signals at ±3.5 ppm. In tumor patients, DeepEMR-based amide proton transfer images provided higher tumor contrast than a conventional MT ratio asymmetry image, particularly at higher B1 strengths (>1.5 μT), with a distinct delineation of the tumor core from normal tissue or peritumoral edema.

CONCLUSION

The DeepEMR approach is feasible for measuring clean APT and rNOE effects in longitudinal and cross-sectional studies with low scan-rescan variability.