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

In this paper, we review the value of phantoms for body MRI in the context of their uses for quantitative MRI methods research, clinical trials, and clinical imaging. Certain uses of phantoms are common throughout the body MRI community, including measuring bias, assessing reproducibility, and training. In addition to these uses, phantoms in body MRI methods research are used for novel methods development and the design of motion compensation and mitigation techniques. For clinical trials, phantoms are an essential part of quality management strategies, facilitating the conduct of ethically sound, reliable, and regulatorily compliant clinical research of both novel MRI methods and therapeutic agents. In the clinic, phantoms are used for development of protocols, mitigation of cost, quality control, and radiotherapy. We briefly review phantoms developed for quantitative body MRI, and finally, we review open questions regarding the most effective use of a phantom for body MRI.

Objective: Previous studies have revealed a substantial between-centre variability in DCE-MRI biomarkers of hepatocellular function in rats. This study aims to identify the main sources of variability by comparing data measured at different centres and field strengths, at different days in the same subjects, and over the course of several months in the same centre.

Materials and methods: 13 substudies were conducted across three facilities on two 4.7 T and two 7 T scanners using a 3D spoiled gradient echo acquisition. All substudies included 3-6 male Wistar-Han rats each, either scanned once with vehicle (n = 76) or twice with either vehicle (n = 19) or 10 mg/kg of rifampicin (n = 13) at follow-up. Absolute values, between-centre reproducibility, within-subject repeatability, detection limits, and effect sizes were derived for hepatocellular uptake rate (K^trans) and biliary excretion rate (k_bh). Sources of variability were identified using analysis of variance and stratification by centre, field strength, and time period.

Results: Data showed significant differences between substudies of 31% for K^trans (p = 0.013) and 43% for k_bh (p < 0.001). Within-subject differences were substantially smaller for k_bh (8%) but less so for K^trans (25%). Rifampicin-induced inhibition was safely above the detection limits, with an effect size of 75 ± 3% in K^trans and 67 ± 8% in k_bh. Most of the variability in individual data was accounted for by between-subject (K^trans = 23.5%; k_bh = 42.5%) and between-centre (K^trans = 44.9%; k_bh = 50.9%) variability, substantially more than the between-day variation (K^trans = 0.1%; k_bh = 5.6%). Significant differences in k_bh were found between field strengths at the same centre, between centres at the same field strength, and between repeat experiments over 2 months apart in the same centre.

Discussion: Between-centre bias caused by factors such as hardware differences, subject preparations, and operator dependence is the main source of variability in DCE-MRI of liver function in rats, closely followed by biological between-subject differences. Future method development should focus on reducing these sources of error to minimise the sample sizes needed to detect more subtle levels of inhibition.

Object: Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of López-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC).

Materials and methods: The OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials.

Results: The DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps.

Discussion: In the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.

Objectives: To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI).

Materials and methods: We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle.

Results: Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (p < 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (p = 0.002 ~ p < 0.001).

Discussion: DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.

Object: To review recent advances of artificial intelligence (AI) in enhancing the efficiency and throughput of the MRI acquisition workflow in neuroimaging, including planning, sequence design, and correction of acquisition artifacts.

Materials and methods: A comprehensive analysis was conducted on recent AI-based methods in neuro MRI acquisition. The study focused on key technological advances, their impact on clinical practice, and potential risks associated with these methods.

Results: The findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput. Specific algorithms were identified as particularly effective in optimizing acquisition steps, with reported improvements in workflow efficiency.

Discussion: The review highlights the transformative potential of AI in neuro MRI acquisition, emphasizing the technological advances and clinical benefits. However, it also discusses potential risks and challenges, suggesting areas for future research to mitigate these concerns and further enhance AI integration in MRI acquisition.

Background: Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have found a variety of applications on the process of MRS data. A quantification strategy compatible to DL base MRS spectral processing method is, therefore, useful.

Materials and methods: This study aims to investigate whether metabolite concentrations quantified using a convolutional neural network (CNN) based method, coupled with a scaling procedure that normalizes spectral signals for CNN input and linear regression, can effectively reflect variations in metabolite concentrations in IU across different brain regions with varying signal-to-noise ratios (SNR) and linewidths (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels associated with metabolite predictions. In vivo MRS spectra were acquired from three brain regions of 43 subjects using a 3T system.

Results: The metabolite concentrations in IU of five major metabolites, quantified using CNN and LCModel, exhibit similar ranges with Pearson's correlation coefficients ranging from 0.24 to 0.78. The SE of the metabolites shows a positive correlation with Cramer-Rao lower bound (CRLB) (r=0.46) and  absolute CRLB (r=0.81), calculated by multiplying CRLBs with the quantified metabolite content.

Conclusion: In conclusion, the CNN based method with the proposed scaling procedures can be employed to quantify in vivo MRS spectra and derive metabolites concentrations in IU. The SE can be used as error index, indicating predicted uncertainties for metabolites and sharing information similar to the absolute CRLB.

Object: To enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability.

Materials and methods: While PG-DL has emerged as a powerful image reconstruction method, its application to large-scale 3D non-Cartesian MRI is hindered by hardware limitations and limited availability of training data. We combine several recent advances in deep learning and MRI reconstruction to tackle the former challenge, and we further propose a 2.5D reconstruction using 2D convolutional neural networks, which treat 3D volumes as batches of 2D images to train the network with a limited amount of training data. Both 3D and 2.5D variants of the PG-DL networks were compared to conventional methods for high-resolution 3D kooshball coronary MRI.

Results: Proposed PG-DL reconstructions of 3D non-Cartesian coronary MRI with 3D and 2.5D processing outperformed all conventional methods both quantitatively and qualitatively in terms of image assessment by an experienced cardiologist. The 2.5D variant further improved vessel sharpness compared to 3D processing, and scored higher in terms of qualitative image quality.

Discussion: PG-DL reconstruction of large-scale 3D non-Cartesian MRI without compromising image size or network complexity is achieved, and the proposed 2.5D processing enables high-quality reconstruction with limited training data.