Journal of Magnetic Resonance Imaging最新文献

Background: Quantitative Susceptibility Mapping (QSM) provides a non-invasive post-processing method to investigate alterations in magnetic susceptibility (χ), reflecting iron content within brain regions implicated in neurodegenerative diseases (NDDs).

Purpose: To investigate alterations in thalamic χ in patients with NDDs using QSM.

Study type: Systematic review and meta-analysis.

Population: A total of 696 patients with NDDs and 760 healthy controls (HCs) were included in 27 studies.

Field strength/sequence: Three-dimensional multi-echo gradient echo sequence for QSM at mostly 3 Tesla.

Assessment: Studies reporting QSM values in the thalamus of patients with NDDs were included. Following PRISMA 2020, we searched the four major databases including PubMed, Scopus, Web of Science, and Embase for peer-reviewed studies published until October 2024.

Statistical tests: Meta-analysis was conducted using a random-effects model to calculate the standardized mean difference (SMD) between patients and HCs.

Results: The pooled SMD indicated a significant increase in thalamic χ in NDDs compared to HCs (SMD = 0.42, 95% CI: 0.05-0.79; k = 27). Notably, amyotrophic lateral sclerosis patients showed a significant increase in thalamic χ (1.09, 95% CI: 0.65-1.53, k = 2) compared to HCs. Subgroup analyses revealed significant χ alterations in younger patients (mean age ≤ 62 years; 0.56, 95% CI: 0.10-1.02, k = 11) and studies using greater coil channels (coil channels > 16; 0.64, 95% CI: 0.28-1.00, k = 9). Publication bias was not detected and quality assessment indicated that studies with a lower risk of bias presented more reliable findings (0.75, 95% CI: 0.32-1.18, k = 9). Disease type was the primary driver of heterogeneity, while other factors, such as coil type and geographic location, also contributed to variability.

Data conclusion: Our findings support the potential of QSM for investigating thalamic involvement in NDDs. Future research should focus on disease-specific patterns, thalamic-specific nucleus analysis, and temporal evolution.

Plain language summary: Our research investigated changes in iron levels within the thalamus, a brain region crucial for motor and cognitive functions, in patients with various neurodegenerative diseases (NDDs). The study utilized a specific magnetic resonance imaging technique called Quantitative Susceptibility Mapping (QSM) to measure iron content. It identified a significant increase in thalamic iron levels in NDD patients compared to healthy individuals. This increase was particularly prominent in patients with Amyotrophic Lateral Sclerosis, younger individuals, and studies employing advanced imaging equipment.

Level of evidence: 2 TECHNICAL EFFICACY: Stage 2.

Osteoarthritis (OA) is heterogeneous and involves structural changes in the whole joint, such as cartilage, meniscus/labrum, ligaments, and tendons, mainly with short T2 relaxation times. Detecting OA before the onset of irreversible changes is crucial for early proactive management and limit growing disease burden. The more recent advanced quantitative imaging techniques and deep learning (DL) algorithms in musculoskeletal imaging have shown great potential for visualizing "pre-OA." In this review, we first focus on ultrashort echo time-based magnetic resonance imaging (MRI) techniques for direct visualization as well as quantitative morphological and compositional assessment of both short- and long-T2 musculoskeletal tissues, and second explore how DL revolutionize the way of MRI analysis (eg, automatic tissue segmentation and extraction of quantitative image biomarkers) and the classification, prediction, and management of OA. PLAIN LANGUAGE SUMMARY: Detecting osteoarthritis (OA) before the onset of irreversible changes is crucial for early proactive management. OA is heterogeneous and involves structural changes in the whole joint, such as cartilage, meniscus/labrum, ligaments, and tendons, mainly with short T2 relaxation times. Ultrashort echo time-based magnetic resonance imaging (MRI), in particular, enables direct visualization and quantitative compositional assessment of short-T2 tissues. Deep learning is revolutionizing the way of MRI analysis (eg, automatic tissue segmentation and extraction of quantitative image biomarkers) and the detection, classification, and prediction of disease. They together have made further advances toward identification of imaging biomarkers/features for pre-OA. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Pancreatic ductal adenocarcinoma (PDAC) is the deadliest malignant tumor, with a grim 5-year overall survival rate of about 12%. As its incidence and mortality rates rise, it is likely to become the second-leading cause of cancer-related death. The radiological assessment determined the stage and management of PDAC. However, it is a highly heterogeneous disease with the complexity of the tumor microenvironment, and it is challenging to adequately reflect the biological aggressiveness and prognosis accurately through morphological evaluation alone. With the dramatic development of artificial intelligence (AI), multiparametric magnetic resonance imaging (mpMRI) using specific contrast media and special techniques can provide morphological and functional information with high image quality and become a powerful tool in quantifying intratumor characteristics. Besides, AI has been widespread in the field of medical imaging analysis. Radiomics is the high-throughput mining of quantitative image features from medical imaging that enables data to be extracted and applied for better decision support. Deep learning is a subset of artificial neural network algorithms that can automatically learn feature representations from data. AI-enabled imaging biomarkers of mpMRI have enormous promise to bridge the gap between medical imaging and personalized medicine and demonstrate huge advantages in predicting biological characteristics and the prognosis of PDAC. However, current AI-based models of PDAC operate mainly in the realm of a single modality with a relatively small sample size, and the technical reproducibility and biological interpretation present a barrage of new potential challenges. In the future, the integration of multi-omics data, such as radiomics and genomics, alongside the establishment of standardized analytical frameworks will provide opportunities to increase the robustness and interpretability of AI-enabled image biomarkers and bring these biomarkers closer to clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 4.

Breast cancer continues to be a major health concern, and early detection is vital for enhancing survival rates. Magnetic resonance imaging (MRI) is a key tool due to its substantial sensitivity for invasive breast cancers. Computer-aided detection (CADe) systems enhance the effectiveness of MRI by identifying potential lesions, aiding radiologists in focusing on areas of interest, extracting quantitative features, and integrating with computer-aided diagnosis (CADx) pipelines. This review aims to provide a comprehensive overview of the current state of CADe systems in breast MRI, focusing on the technical details of pipelines and segmentation models including classical intensity-based methods, supervised and unsupervised machine learning (ML) approaches, and the latest deep learning (DL) architectures. It highlights recent advancements from traditional algorithms to sophisticated DL models such as U-Nets, emphasizing CADe implementation of multi-parametric MRI acquisitions. Despite these advancements, CADe systems face challenges like variable false-positive and negative rates, complexity in interpreting extensive imaging data, variability in system performance, and lack of large-scale studies and multicentric models, limiting the generalizability and suitability for clinical implementation. Technical issues, including image artefacts and the need for reproducible and explainable detection algorithms, remain significant hurdles. Future directions emphasize developing more robust and generalizable algorithms, integrating explainable AI to improve transparency and trust among clinicians, developing multi-purpose AI systems, and incorporating large language models to enhance diagnostic reporting and patient management. Additionally, efforts to standardize and streamline MRI protocols aim to increase accessibility and reduce costs, optimizing the use of CADe systems in clinical practice. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.