Risk stratification of abdominal tumors in children with amide proton transfer imaging

Effects of Breathing Patterns on Amide Proton Transfer MRI in the Kidney: A Preliminary Comparative Study in Healthy Volunteers and Patients With Tumors

BACKGROUND

The amide proton transfer-weighted (APTw) imaging for kidney diseases is important. However, the breathing patterns on APTw imaging remains unexplored.

PURPOSE

This study aimed to investigate the effects of intermittent breath-hold (IBH) and free breathing (FB) on renal 3D-APTw imaging.

STUDY TYPE

Healthy volunteers were enrolled prospectively, and renal clear cell carcinoma (RCCC) patients were included retrospectively.

POPULATION

58 healthy volunteers and 10 RCCC patients.

FIELD STRENGTH/SEQUENCE

3-T, turbo spin echo, and fast field echo.

ASSESSMENT

3D-APTw imaging was scanned using the IBH and FB methods in volunteers and using the IBH method in RCCC patients. The image quality was evaluated by three observers according to the 5-point Likert scale. Optimal images rated at three points or higher were used to measure the APT values.

STATISTICAL ANALYSIS

The measurement repeatability was assessed using the intraclass correlation coefficient (ICC) and the Bland-Altman plot. The APT values were analyzed using McNemar's test, one-way analysis of variance, and t test.

RESULTS

50 healthy volunteers and 8 RCCC patients were enrolled. Renal 3D-APTw imaging using the IBH method revealed a higher success rate (88% vs 78%). The ICCs were excellent in the IBH group (ICCs > 0.74) and were good in the FB group (ICCs < 0.74). No significant differences in the APT values among various zones using the IBH (P = 0.263) or FB method (P = 0.506). The mean APT value using the IBH method (2.091% ± 0.388%) was slightly lower than the FB method (2.176% ± 0.292%), but no significant difference (P = 0.233). The APT value of RCCC (4.832% ± 1.361%) was considerably higher than normal renal using the IBH method.

CONCLUSIONS

The study demonstrated that the IBH method substantially increased the image quality of renal 3D-APTw imaging. Furthermore, APT values may vary between normal and tumor tissues.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Amide proton transfer-weighted imaging of the abdomen: Current progress and future directions

The chemical exchange saturation transfer technique serves as a valuable tool for generating in vivo image contrast based on the content of various proton groups, including amide protons, amine protons, and aliphatic protons. Among these, amide proton transfer-weighted (APTw) imaging has seen extensive development as a means to assess the biochemical status of lesions. The exchange from saturated amide protons to bulk water protons during and following the saturation ratio frequency pulse contributes to detectable APT signals. While APTw imaging has garnered significant attention in the central nervous system, demonstrating noteworthy findings in cerebral neoplasia, stroke, and Alzheimer's disease over the past decade, its application in the abdomen has been a relatively recent progression. Notably, studies have explored its utility in hepatocellular carcinoma, prostate cancer, and cervical carcinoma within the abdominal context. Despite these advancements, there is a paucity of reviews on APTw imaging in abdominal applications. This paper aims to fill this gap by providing a concise overview of the fundamental theories underpinning APTw imaging. Additionally, we systematically summarize its diverse clinical applications in the abdomen, with a particular focus on the digestive and urogenital systems. Finally, the manuscript concludes by discussing technical limitations and factors influencing APTw imaging in abdominal applications, along with prospects for future research.

Accelerating CEST imaging using a model-based deep neural network with synthetic training data

PURPOSE

To develop a model-based deep neural network for high-quality image reconstruction of undersampled multi-coil CEST data.

THEORY AND METHODS

Inspired by the variational network (VN), the CEST image reconstruction equation is unrolled into a deep neural network (CEST-VN) with a k-space data-sharing block that takes advantage of the inherent redundancy in adjacent CEST frames and 3D spatial-frequential convolution kernels that exploit correlations in the x-ω domain. Additionally, a new pipeline based on multiple-pool Bloch-McConnell simulations is devised to synthesize multi-coil CEST data from publicly available anatomical MRI data. The proposed network is trained on simulated data with a CEST-specific loss function that jointly measures the structural and CEST contrast. The performance of CEST-VN was evaluated on four healthy volunteers and five brain tumor patients using retrospectively or prospectively undersampled data with various acceleration factors, and then compared with other conventional and state-of-the-art reconstruction methods.

RESULTS

The proposed CEST-VN method generated high-quality CEST source images and amide proton transfer-weighted maps in healthy and brain tumor subjects, consistently outperforming GRAPPA, blind compressed sensing, and the original VN. With the acceleration factors increasing from 3 to 6, CEST-VN with the same hyperparameters yielded similar and accurate reconstruction without apparent loss of details or increase of artifacts. The ablation studies confirmed the effectiveness of the CEST-specific loss function and data-sharing block used.

CONCLUSIONS

The proposed CEST-VN method can offer high-quality CEST source images and amide proton transfer-weighted maps from highly undersampled multi-coil data by integrating the deep learning prior and multi-coil sensitivity encoding model.