Magnetic Resonance Materials in Physics, Biology and Medicine最新文献

Objective: Within gradient-spoiled transient-state MR sequences like Magnetic Resonance Fingerprinting or Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT), it is examined whether an optimized RF phase modulation can help to improve the precision of the resulting relaxometry maps.

Methods: Using a Cramer-Rao based method called BLAKJac, optimized sequences of RF pulses have been generated for two scenarios (amplitude-only modulation and amplitude + phase modulation) and for several conditions. These sequences have been tested on a phantom, a healthy human brain and a healthy human leg, to reconstruct parametric maps ( $T_1$ and $T_2$ ) as well as their standard deviations.

Results: The amplitude + phase modulation scenario systematically resulted in lower noise levels than the amplitude-only modulation scenario. On average, the difference was around 34%, but it was substantially larger for scans acquired under SAR restrictions. Compared to amplitude-only, in the amplitude + phase modulation scenario, the relevance of an inversion pulse and of a pause were greatly reduced, at least considering overall precision and in-phantom accuracy.

Conclusion: The application of an optimized RF phase modulation in quantitative transient-states MRI is beneficial for almost all tested scenarios and conditions, in particular under SAR restrictions Furthermore, RF phase modulation reduces the need for inversions pulses and pauses.

Objectives: Magnetic resonance imaging (MRI) is a complex medical imaging method where multiple technical and physiological factors may lead to undesired changes in image quality. The quality control methods utilizing test objects are useful in measuring technical performance, but they may not fully detect all factors present in clinical imaging. In this study, we developed methodologies to quantify observer-based image quality and to compare these observations with technical quality control (QC) parameters.

Materials and methods: We analysed 150 brain MRI 3D-FLAIR volumes from 15 scanners, measuring image quality both quantitatively and by visually ranking the images using forced-choice comparison.

Results: Significant differences were found between different scanners based on the forced choice comparison. In imaging study-specific analysis, a weak correlation was observed with contrast-to-noise ratio (CNR) (R² = 0.17) and brain white matter-gray matter (WM/GM) contrast (R² = 0.14). With device-specific median correlation, the CNR and WM/GM contrast R² were 0.21 and 0.34, respectively. Additionally, using device-specific median values, a correlation was found with image quality index (QI) (R² = 0.21) and some modulation transfer function (MTF) based resolution-specific parameters (MTF10 FH, R² = 0.19; MTF10 AP, R² = 0.20; MTF50 AP, R² = 0.17).

Discussion: The forced choice comparison can be effectively utilized to rank image quality across multiple MRI scanners. Technical image quality parameters, directly analysed from anatomical image volumes, can offer prospective maintenance value. Additionally, the quality of clinical image volumes can be assessed using both forced choice comparison and calculational image analysis methods.

Objective: Epicardial and paracardial adipose tissues (EAT and PAT) are two types of fat depots around the heart and they have important roles in cardiac physiology. Manual quantification of EAT and PAT from cardiac MR (CMR) is time-consuming and prone to human bias. Leveraging the cardiac motion, we aimed to develop deep learning neural networks for automated segmentation and quantification of EAT and PAT in short-axis cine CMR.

Materials and methods: A modified U-Net equipped with modules of multi-resolution convolution, motion information extraction, feature fusion, and dual attention mechanisms, was developed. Multiple steps of ablation studies were performed to verify the efficacy of each module. The performance of different networks was also compared.

Results: The final network incorporating all modules achieved segmentation Dice indices of 77.72% ± 2.53% and 77.18% ± 3.54% for EAT and PAT, respectively, which were significantly higher than the baseline U-Net. It also achieved the highest performance compared to other networks. With our model, the determination coefficients of EAT and PAT volumes to the reference were 0.8550 and 0.8025, respectively.

Conclusion: Our proposed network can provide accurate and quick quantification of EAT and PAT on routine short-axis cine CMR, which can potentially aid cardiologists in clinical settings.

Objectives: To explore the feasibility of assessing liver regeneration (LR) after partial hepatectomy (PH) in a rat model of metabolic dysfunction-associated fatty liver (MAFL) using intravoxel incoherent motion (IVIM) and T2^* mapping.

Animal model: Eighty Sprague-Dawley rats with MAFLD were randomly assigned to a longitudinal MRI group and pathology group. The MRI group (n = 10) included hepatectomy (MRph, n = 5) and control (MRctrl, n = 5) subgroups, which underwent serials MRI scans. The pathology group (n = 70) included hepatectomy (PAph, n = 35) and control (PActrl, n = 35) subgroups, which underwent MRI scans at baseline, days 1, 2, 3, 5, 7, and 14 (five rats per group), followed with histopathological analysis. Correlations between MRI parameters, liver function indicators (ALT, AST, TBIL), and histopathology (Ki-67, hepatocyte hypertrophy rate [ΔH], liver volume [LV]) were analyzed.

Results: In the MRph group, D and T2^* values increased and then decreased post-PH, while D^* and PF values decreased and then increased, with all parameters trending toward baseline. The Ki-67 index, hepatocyte size, ΔH, and liver function indicators initially increased, and then gradually decreased. D^* was significantly negatively correlated with the Ki-67, hepatocyte size, ΔH, ALT, AST, TBIL, and LV (|r|= 0.53-0.83; all P < 0.05).

Conclusions: IVIM and T2^* mapping enabled non-invasive monitoring of LR in MAFL rats. IVIM-derived liver D^* correlated with liver function and pathology, highlighting its potential as a novel LR marker.

Purpose: To reconstruct whole-heart images from free-running acquisitions through automated selection of data acceptance windows (ES: end-systole, MD: mid-diastole, ED: end-diastole) that account for heart rate variability (HRV).

Methods: SYMPHONIC was developed and validated in simulated (N = 1000) and volunteer (N = 14) data. To validate SYMPHONIC, the position of the detected acceptance windows, total duration, and resulting ventricular volume were compared to the simulated ground truth to establish metrics for temporal error, quiescent interval duration, and volumetric error, respectively. SYMPHONIC MD images and those using manually defined acceptance windows with fixed (MANUAL_FIXED) or adaptive (MANUAL_ADAPT) width were compared by measuring vessel sharpness (VS). The impact of HRV was assessed in patients (N = 6).

Results: Mean temporal error was larger for MD than for ED and ED in both simulations and volunteers. Mean volumetric errors were comparable. Interval duration differed for ES (p = 0.04) and ED (p < 10^-3), but not for MD (p = 0.08). In simulations, SYMPHONIC and MANUAL_ADAPT provided consistent VS for increasing HRV, while VS decreased for MANUAL_FIXED. In volunteers, VS differed between MANUAL_ADAPT and MANUAL_FIXED (p < 0.01), but not between SYMPHONIC and MANUAL_ADAPT (p = 0.03) or MANUAL_FIXED (p = 0.42).

Conclusion: SYMPHONIC accurately detected quiescent cardiac phases in free-running data and resulted in high-quality whole-heart images despite the presence of HRV.

Introduction: Breast cancer, the most prevalent cancer among women globally, is classified into molecular subtypes (luminal, HER2-enriched, and triple-negative) to guide treatment and prognosis. Traditional subtyping methods, such as gene profiling and immunohistochemistry, are invasive and limited by intratumoural heterogeneity. MRI radiomics analysis offers a non-invasive alternative by extracting quantitative imaging features, yet its application in diverse, multi-ethnic populations remains underexplored.

Objective: This study aimed to identify predictive radiomic features from multiple MRI sequences to classify breast cancer subtypes, compare the performance of four MRI sequences, and determine the optimal machine learning (ML) model for this task. A total of 162 retrospective breast cancer MRI cases were semi-automatically segmented, and 256 radiomic features were extracted. A multimodal ML framework integrating random forest and recursive feature elimination was developed to identify the most predictive features based on the area under the receiver operating characteristic curve (AUROC).

Results: Key predictive features included age, tumour size, margin characteristics, and intensity patterns within the tumour. Among MRI sequences, inversion recovery and T1 post-contrast performed best for subtyping. In addition, texture-based ML models effectively emulated visual assessment, demonstrating the potential of radiomics in non-invasive breast cancer subtyping. With the top ten features, the AUROC values are 0.735, 0.630, and 0.747 for luminal, HER2-enriched, and triple-negative, respectively.

Conclusion: These findings highlight the role of MRI-based texture features and advanced ML in enhancing breast cancer diagnosis, offering a non-invasive tool for personalised treatment planning while complementing existing clinical workflows.

Objective: The Polar Fourier Transform (PFT) has been proposed as a direct alternative to gridding for reconstructing radially acquired MRI data. This study evaluates the feasibility of inline PFT implementation on a clinical MRI scanner and assesses its computational performance and image quality under acceleration.

Materials and methods: PFT was implemented as modular components within the Siemens Image Calculation Environment, using a recursive numerical Hankel transform. Phantom and in vivo brain datasets acquired with 2D radial trajectories were reconstructed using both PFT and vendor-supplied gridding. Reconstruction time, SNR, artifact behavior, and spatial resolution were assessed across multiple undersampling levels (up to 8 ×), using simulations and repeated scans.

Results: PFT was successfully integrated with a runtime of ~ 6-9 × acquisition time. It exhibited spatially variant behavior, concentrating resolution in central region while shifting undersampling-induced blurring outward. Compared to gridding, PFT reduced structured streaks and better preserved image quality under acceleration. Gradient delay artifacts were reduced by alternating spoke polarity. Notably, the pituitary gland and basilar artery remained visible at high acceleration, highlighting preserved central fidelity.

Discussion: PFT enables effective inline reconstruction for radial MRI and preserves image quality in small central regions of interest under aggressive undersampling-supporting dynamic and ROI-focused applications.

Introduction: This study proposes a framework for determining the calculation error in online SAR supervision introduced by directional couplers.

Materials and methods: A mathematical framework is introduced showing how the error in the measured excitation vector compared to the actual excitation vector can be rewritten as a new set of virtual observation points (VOPs). By comparing the new set of VOPs to the original VOPs through an optimization, the maximum underestimation of SAR can be calculated. The framework is then applied to five different RF arrays.

Results: The results show that the error in SAR calculation is very dependent on the position of the reference plane of the directional coupler measurements and the S-parameters of the array. To have an error of less than 5%, directional couplers with a directivity better than 40 dB are necessary for the worst case of the investigated arrays.

Discussion: The framework presented in this paper provides an approach for calculating a safety factor to account for the inaccuracies introduced by directional coupler measurements in online SAR supervision. The framework can also be adapted to other types of measurements.

Objective: This study aimed to describe important criteria for phantom design, while designing an open-source phantom that uses accessible materials and fabrication processes, and that can be easily reproduced and modified by others in the MRI research community.

Materials and methods: We enumerate considerations related to designing a phantom based on literature and previous experience. We design and use an open-source phantom on a low-field MRI system. The phantom was 3D printed and assembled, and the imaged samples were made from commonly available materials. T1-weighted and T2-weighted axial and coronal images were acquired at 64 mT, and signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and geometric distortion along one dimension were assessed for each image.

Results: Two iterations of the phantom design were made to improve the construction materials and overall form factor for imaging. T1-weighted and T2-weighted images showed contrast between samples and background. T2-weighted images had an 8-10× increase in SNR and CNR compared to T1-weighted images. Geometric distortion measurements were within one-pixel spacing for all scans.

Discussion: An open-source phantom was created to assess MRI scans at low-field. Future users may modify the phantom to suit their needs. User-designed inserts can be added, allowing for validation of many MRI-related measurements.