Current Medical Imaging Reviews最新文献

Introduction: Prenatal intervention with fetoscopic endoluminal tracheal occlusion (FETO) using a balloon can stimulate lung growth and improve neonatal survival for moderate and severe congenital diaphragmatic hernia (CDH). Quantitative parameters measured on magnetic resonance imaging (MRI) can guide the treatment of CDH and evaluate changes after FETO treatment.

Case presentation: We reported on five cases of isolated left congenital diaphragmatic hernia (CDH) in fetuses who underwent FETO surgery. We conducted a comparison of the MRI images before and after FETO treatment and analyzed the correlation between the observed changes and the clinical outcomes of the neonates after delivery.

Conclusion: MRI can precisely provide the anatomical details of CDH and quantitatively analyze changes in fetal lung volume before and after FETO surgery.

Introduction: Artificial intelligence has significantly enhanced disease diagnosis in healthcare, particularly through Deep Learning (DL) and Federated Learning (FL) approaches. These technologies have shown promise in detecting ocular diseases using medical imaging while addressing challenges related to data privacy and security. FL enables collaborative learning without sharing sensitive medical data, making it an attractive solution for healthcare applications. This systematic review aims to analyze the advancements in AI-driven ocular disease detection, with a particular focus on FL-based approaches. The article evaluates the evolution, methodologies, challenges, and effectiveness of FL in enhancing diagnostic accuracy while ensuring data confidentiality.

Methods: The systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to ensure transparency and reliability. Research articles published between 2017 and 2024 were identified using academic databases, including Web of Science, Scopus, IEEE Xplore, and PubMed. Studies focusing on DL and FL models for detecting ocular diseases were selected based on predefined inclusion and exclusion criteria. A comparative analysis of the methodologies, architectures, datasets, and performance metrics of different FL models has been presented.

Results and discussion: The findings indicated that FL preserves data privacy while achieving diagnostic performance comparable to traditional centralized AI models. Various FL models, including FedAvg and FedProx, have been implemented for ocular disease detection, with high accuracy and efficiency. However, challenges, such as data heterogeneity, communication efficiency, and model convergence, persist.

Conclusion: FL represents a promising approach for ocular disease detection, balancing diagnostic accuracy with data privacy. Future research may focus on optimizing FL frameworks for improving scalability, communication efficiency, and integrating advanced privacy-preserving techniques.

Introduction: Early detection of gastric cancer remains challenging for many of the current imaging techniques. Recent advancements in reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) have shown promise in improving the visualization of small anatomical structures. This study aimed to evaluate and compare the diagnostic performance of rFOV DWI with multi-detector computed tomography (MDCT) and conventional full field of view (fFOV) DWI for detecting early gastric cancer (EGC).

Methods: This retrospective study included 43 patients with pathologically confirmed EGC. All participants underwent pre-treatment imaging, including CT scans and MRI with a prototype rFOV DWI and conventional fFOV DWI at 3 Tesla. Quantitative (signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR]) and qualitative (subjective image quality) assessments were performed. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves and area-under-the-curve (AUC) analysis.

Results: rFOV DWI demonstrated significantly higher SNR and CNR compared with fFOV DWI (P < 0.05). Subjective image quality scores were also superior for rFOV DWI (P < 0.05). In lesion detection, rFOV DWI showed higher sensitivity (0.705) than CT (0.636) and fFOV DWI (0.523). ROC analysis revealed that rFOV DWI had a higher AUC (0.829, 95% CI [0.764, 0.882]) than fFOV DWI (0.734, 95% CI [0.661, 0.798], P = 0.02) and a modest improvement over CT (0.799, 95% CI [0.731, 0.856], P = 0.51).

Discussion: The findings suggest that rFOV DWI provides superior image quality and diagnostic accuracy for EGC detection compared with conventional fFOV DWI. While it showed a trend toward better performance than CT, further studies with larger cohorts are needed to validate these results.

Conclusion: rFOV DWI offers improved image quality and diagnostic performance for early gastric cancer detection compared with fFOV DWI, with a potential advantage over CT. This technique may enhance early diagnosis and clinical decision-making in gastric cancer management.

Introduction: Liver fibrosis is a key pathological process that can progress to cirrhosis and liver failure. Although magnetic resonance elastography (MRE) is an established noninvasive method for fibrosis staging, its clinical application is limited by hardware dependence. The diagnostic utility of diffusionweighted imaging-based virtual MRE (vMRE) and B1-corrected T1 mapping in liver fibrosis assessment remains to be further investigated.

Methods: Forty rabbits were included in the final analysis: CCl4-induced fibrosis (n=33) and control (n=7). Following Gd-EOB-DTPA administration, DWI and T1 mapping sequences were executed at 5 and 10 minutes. Diagnostic efficacy and correlations of vMRE and T1 mapping in a rabbit liver fibrosis model were evaluated.

Results: Rabbits were classified into three groups: Control (n=7), Nonadvanced fibrosis (F1-F2, n=20), and Advanced fibrosis (F3-F4, n=13). The AUC values for T1post_5min, T1post_10min, rΔT1_10min, and μdiff in distinguishing controls from nonadvanced and advanced fibrosis groups were (0.78, 0.82, 0.71), (0.82, 0.85, 0.77), and (0.62, 0.69, 0.74), respectively, with μdiff showing (0.90, 0.93, 0.66). A significant positive correlation existed between μdiff and liver fibrosis grade (r=0.534, p<0.001).

Discussion: μdiff correlated well with fibrosis severity and effectively identified fibrotic livers, but showed limited ability to distinguish fibrosis stages, likely due to overlapping tissue stiffness. B1-corrected T1 mapping offered complementary functional information, with the 10-minute post-contrast time point providing the best staging performance, thereby enhancing the overall diagnostic value.

Conclusion: Gd-EOB-DTPA-enhanced T1 mapping and DWI-based vMRE provide substantial noninvasive assessment of liver fibrosis.

Introduction: This study aimed to investigate nerve fiber bundle damage associated with spinocerebellar degeneration, a dominant inherited neurological disorder, using magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI).

Methods: Four cases of spinocerebellar degeneration and ten matched healthy subjects were retrospectively enrolled. DTI software was used for processing and analysis.

Results: All patients had an abnormal spinocerebellar ataxia (SCA) type 3 gene mutation, with cerebellar and brainstem atrophy, a decreased signal in the pons and projection fibers. Significant interruption and destruction were revealed in the midline of the cerebellar peduncle, cerebellar arcuate fibers, and the spinothalamic and spinocerebellar tracts. Significant (p <0.05) decreases were detected in FA values in the cerebellar peduncle (0.51±0.04 vs. 0.68±0.02), cerebellar arcuate fibers (0.37±0.08 vs. 0.51±0.05), spinothalamic tract (0.42±0.03 vs. 0.49±0.05), and spinocerebellar tract (0.44±0.06 vs. 0.52±0.06) compared with healthy controls. Compared with healthy controls, significant (p <0.05) increases were detected in ADC values in the cerebellar peduncle (0.84±0.11 vs. 0.67±0.03), cerebellar arcuate fibers (0.87±0.12 vs. 0.66±0.05), spinothalamic tract (0.89±0.13 vs. 0.70±0.03) within the brainstem, and spinocerebellar tract (0.79±0.07 vs. 0.69±0.06).

Discussion: The MRI DTI technique provides sufficient information for studying spinocerebellar degeneration and for conducting further research on its etiology and diagnosis. Some limitations were present, including the retrospective and single-center study design, a limited patient sample, and enrollment of only Chinese patients.

Conclusion: The MRI DTI technique can clearly demonstrate the degree of damage to nerve fiber bundles in the cerebellum and the adjacent relationship between the fiber bundles entering and exiting the cerebellum in patients with spinocerebellar degeneration.

Introduction: The use of magnetic resonance imaging (MRI), which has greater soft tissue contrast than other imaging modalities, has increased over the last 30 years. Studies have shown that MRI is frequently used for diagnosing neurodegenerative diseases. The incidence of Alzheimer's disease, a neurodegenerative condition, is increasing due to population aging and has a detrimental impact on quality of life. Volumetric changes in many important anatomical structures have been detected in magnetic resonance (MR) images of Alzheimer's disease patients. Various software programs, such as OsiriX, Horos, and VolBrain, are currently used to perform area and volume measurements in various brain structures. In this study, we compared the VolBrain and Horos applications for volume measurements of the cerebellum, whose relationship with Alzheimer's disease is not yet fully understood. We aimed to assess the consistency between the applications using various statistical methods and to highlight their respective advantages and disadvantages for researchers.

Methods: This was a retrospective study. The patient group comprised 50 individuals with Alzheimer's disease aged 30-65 years. T1 MR images of 50 Alzheimer's disease patients were first acquired via the VolBrain program and then via the Horos program.

Results: The applications used yielded almost identical measurement results, and no significant differences were observed.

Discussion: Both applications have been found to produce consistent results. This indicates that the methods are reliable and that either application can be effectively used for the intended purpose.

Conclusion: In conclusion, the choice between the two applications depends largely on the user's data requirements, software preferences, and hardware capabilities. These factors play a decisive role in the selection process.

Introduction: Incontinentia Pigmenti (IP) is a rare X-linked dominant neurocutaneous disorder characterized by cutaneous, ocular, and neurological manifestations. We present a fatal case of IP with atypical neuroimaging findings.

Case presentation: A 4-month-old female infant presented with generalized hyperpigmentation, palatal cleft, and acute encephalopathy. Initial non-contrast cranial Computed Tomography (CT) demonstrated cerebellar hypoattenuation with punctate calcifications and ventriculomegaly. Subsequent Magnetic Resonance Imaging (MRI) demonstrated extensive ischemia, edema, and hemorrhagic lesions in the brainstem, cerebellum, and cervical spinal cord. Trio-based whole-exome sequencing did not detect pathogenic variants in the Inhibitor of Nuclear Factor Kappa-B Kinase Regulatory Subunit Gamma (IKBKG) gene (NM_003639.3).

Conclusion: This case highlights the critical role of neuroimaging in diagnosing IP-related neurological complications and emphasizes the need for early multimodal imaging evaluation. The discordance between clinical phenotype and genetic findings warrants further investigation into novel pathogenic mechanisms.

Background: Advancements in computational methods in medicine have brought about extensive improvement in the diagnosis of illness, with machine learning models such as Convolutional Neural Networks leading the charge. This work introduces PneumoNet, a novel deep-learning model designed for accurate pneumonia detection from chest X-ray images. Pneumonia detection from chest X-ray images is one of the greatest challenges in diagnostic practice and medical imaging. Proper identification of standard chest X-ray views or pneumonia-specific views is required to perform this task effectively. Contemporary methods, such as classical machine learning models and initial deep learning methods, guarantee good performance but are generally marred by accuracy, generalizability, and preprocessing issues. These techniques are generally marred by clinical usage constraints like high false positives and poor performance over a broad spectrum of datasets.

Materials and methods: A novel deep learning architecture, PneumoNet, has been proposed as a solution to these problems. PneumoNet applies a convolutional neural network (CNN) structure specifically employed for the improvement of accuracy and precision in image classification. The model employs several layers of convolution as well as pooling, followed by fully connected dense layers, for efficient extraction of intricate features in X-ray images. The innovation of this approach lies in its advanced layer structure and its training, which are optimized to enhance feature extraction and classification performance greatly. The model proposed here, PneumoNet, has been cross-validated and trained on a well-curated dataset that includes a balanced representation of normal and pneumonia cases.

Results: Quantitative results demonstrate the model's performance, with an overall accuracy of 98% and precision values of 96% for normal and 98% for pneumonia cases. The recall values for normal and pneumonia cases are 96% and 98%, respectively, highlighting the consistency of the model.

Conclusion: These performance measures collectively indicate the promise of the proposed model to improve the diagnostic process, with a substantial advancement over current methods and paving the way for its application in clinical practice.

Introduction: Construct and compare multiple machine learning models to predict lymph node (LN) metastasis in cervical cancer, utilizing radiomic features extracted from preoperative multi-parametric magnetic resonance imaging (MRI).

Methods: This study retrospectively enrolled 407 patients with cervical cancer who were randomly divided into a training cohort (n=284) and a validation cohort (n=123). A total of 4065 radiomic features were extracted from the tumor regions of interest on contrast-enhanced T1-weighted imaging, T2-weighted imaging, and diffusion-weighted imaging for each patient. The Mann-Whitney U test, Spearman correlation analysis, and selection operator Cox regression analysis were employed for radiomic feature selection. The relationship between MRI radiomic features and LN status was analyzed using five machine-learning algorithms. Model performance was evaluated by measuring the area under the receiver-operating characteristic curve (AUC) and accuracy (ACC). Moreover, Kaplan-Meier analysis was used to validate the prognostic value of selected clinical and radiomic characteristics.

Results: LN metastasis was pathologically detected in 24.3% (99/407) of patients. Following a three-step feature selection, 18 radiomic features were employed for model construction. The XGBoost model exhibited superior performance compared to other models, achieving an AUC, accuracy, sensitivity, specificity, and F1 score of 0.9268, 0.8969, 0.7419, 0.9891, and 0.8364, respectively, on the validation set. Additionally, Kaplan-Meier curves indicated a significant correlation between radiomic scores and progression-free survival in cervical cancer patients (p < 0.05).

Discussion: Among the machine learning models, XGBoost demonstrated the best predictive ability for LN metastasis and showed prognostic value through its radiomic score, highlighting its clinical potential.

Conclusion: Machine learning-based multi-parametric MRI radiomic analysis demonstrated promising performance in the preoperative prediction of LN metastasis and clinical prognosis in cervical cancer.

Introduction: In chronic liver disease (CLD) and liver cirrhosis (LC), assessing hepatic function and disease severity is crucial for patient management. This study aimed to evaluate the relationship between platelet-albumin-bilirubin (PALBI) grade and aspartate aminotransferase/platelet ratio index (APRI) with the functional liver imaging score (FLIS) and liver-to-kidney enhancement ratio (LKER) using gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced hepatobiliary phase (HBP) magnetic resonance imaging (MRI).

Methods: After applying exclusion criteria, 86 patients with CLD or LC who underwent Gd-EOB-DTPA-enhanced MRI between January 2018 and October 2023 were included. APRI and PALBI grades were calculated from laboratory data. FLIS was determined as the sum of three HBP imaging features (liver parenchymal enhancement, biliary excretion, and portal vein sign), with each scoring 0-2. LKER was calculated by dividing liver signal intensity by kidney intensity using region of interest (ROI) measurements. Spearman's correlation was used to assess relationships between the variables.

Results: APRI showed a weak negative correlation with both FLIS (r = -0.327, p = 0.02) and LKER (r = -0.308, p = 0.004). PALBI showed a moderate negative correlation with FLIS (r = -0.495, p = 0.001) and LKER (r = -0.554, p = 0.0001).

Discussion: FLIS and LKER moderately correlated with PALBI and weakly with APRI. LKER may be a more practical tool due to its quantitative nature. Despite limitations, combining imaging and lab-based scores could enhance liver function assessment.

Conclusion: FLIS and LKER can validate, rather than predict or exclude, liver dysfunction in CLD and LC.