Augmented T1 -weighted steady state magnetic resonance imaging

Application of T1-weighted and augmented T1-weighted images of multi-parametric MR sequence in detecting neonatal punctate white matter lesions

BACKGROUND AND PURPOSE

Punctate White Matter Lesion (PWML) is common in neonates. Multi-parametric MR imaging with flexible design (MULTIPLEX, MTP) generates multiple contrasts requires only about 6 min for full-head coverage. This study aimed to evaluate the value of T1WI and aT1WI contrasts of MTP in detecting neonatal punctate white matter lesions.

MATERIALS AND METHODS

Twenty-one neonates with punctate white matter damage underwent multi-parametric MR imaging between November 2022 to July 2024. For subjective image quality, two pediatric neuroradiologists assessed overall image quality, and visualization of structures using a 4-point assessment scale. To analyze objective image quality, the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), contrast, number and sharpness of lesions were quantified.

RESULTS

With regard to sharpness of the lesion, MTP T1WI and aT1WI are comparable to conventional T1W. For subjective assessment, MTP-T1WI exhibited superior overall image quality and anatomical structure display compared to conventional T1WI (P < 0.01). Regarding objective assessment, MTP-T1WI had significantly higher SNR values for gray matter, white matter and lesions than the other two groups. The CNR values of MTP-T1WI and MTP-aT1WI of the white matter to lesion (WM-Lesion) were higher than conventional T1WI. The contrast of aT1WI surpassed that of the other two groups in WM-Lesion contrast. MTP-aT1W can detect more white matter lesions than conventional T1WI (conventional T1WI vs MTP-T1WI vs MTP-aT1WI,123 vs 165 vs 161).

CONCLUSIONS

The MTP-T1W and aT1W images can enhance lesion contrast and precisely delineate the extent and boundaries of the lesions, and could be more sensitive to PWML than conventional T1WI.

Prediction of epileptogenicity in patients with tuberous sclerosis complex using multimodal cerebral MRI

OBJECTIVE

Epilepsy is the most common complication and cause of morbidity and mortality in tuberous sclerosis complex (TSC). Surgery is associated with an increased probability of achieving seizure-freedom. However, the preoperative noninvasive localisation of epileptogenic tubers remains challenging. This study aimed to identify multimodal magnetic resonance imaging (MRI) biomarkers of epilepsy in patients with TSC and develop a prediction model of epileptogenicity in these patients.

METHODS

Patients with TSC, with or without epilepsy, were recruited. All patients underwent MRI scanning, including T1WI, T2WI, T2W-FLAIR, DTI, and multi-parametric MR with a flexible design (MULTIPLEX). We compared the multimodal cerebral MRI characteristics of the cortical tubers, subependymal nodules, and perilesional tissue between patients with TSC with or without epilepsy and developed a prediction model for epileptogenicity.

RESULTS

Among the patients with TSC, 32 with and 16 without epilepsy underwent MRI. Higher proton-density mapping (PD) of cortical tubers and decreased fractional anisotropy (FA), increased mean diffusivity (MD), and increased radial diffusivity (RD) of subependymal nodules were associated with epileptogenicity in both the centre and perilesional tissue, independent of TSC gene variation. Based on the above findings, we developed a prediction model for epileptogenicity with an area under the curve of 0.973, specificity of 0.909, and sensitivity of 0.963 (P < 0.001).

CONCLUSION

In patients with TSC, high PD of the cortical tubers, decreased FA, and elevated MD/RD of the subependymal nodules were significantly associated with epileptogenicity. A prediction model based on multimodal cerebral MRI characteristics has the potential to evaluate the likelihood of epilepsy in patients with TSC.

On the noise in "augmented T1-weighted steady state magnetic resonance imaging"

Recently, Ye and colleagues proposed a method for "augmented T1-weighted imaging" (aT₁ W). The key operation is a complex division of gradient-echo (GRE) images obtained with different flip angles. Ye and colleagues provide an equation for the standard deviation of the obtained aT₁ W signal. Here, we show that this equation leads to wrong values of the standard deviation of such an aT₁ W signal. This is demonstrated by Monte Carlo simulations. The derivation of the equation provided by Ye and colleagues is shown to be erroneous. The error consists of a wrong handling of random variables and their standard deviations and of the wrong assumption of correlated noise in independently acquired GRE images. Instead, the probability distribution obtained with the aT₁ W-method should have been carefully analyzed, perhaps on the basis of previous literature on ratio distributions and their normal approximations.

Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media

Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of 'virtual' and 'augmented' contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media.