Search Results (156)

Early neurological deterioration is associated with poor functional outcomes in stroke patients, but the underlying mechanisms remain unclear. This study aims to understand the progression of stroke-related brain damage using a rhesus monkey model with ischemic occlusion. Multiparameter MRI was used to monitor the progressive evolution of the brain lesion following stroke. Resting-state functional MRI, dynamic susceptibility contrast perfusion MRI, diffusion tensor imaging, and T1- and T2-weighted scans were acquired prior to surgery and at 4–6 h, 48 h, and 96 h following the stroke. The results revealed a sudden increase in infarction volume after the hyper-acute phase but before 48 h on diffusion-weighted imaging (DWI), with a slight extension by 96 h. Lower relative cerebral blood flow (CBF) and time to maximum (Tmax) prior to the stroke, along with a progressive decrease post-stroke, were observed when compared to other stroke monkeys in the same cohort. Functional connectivity (FC) in the ipsilesional secondary somatosensory cortex (S2) and primary motor cortex (M1) exhibited an immediate decline on Day 0 compared to baseline and followed by a slight increase on Day 2 and a further decrease on Day 4. These findings provide valuable insights into infarction progression, emphasizing the critical role of collateral circulation and its impact on early neurological deterioration during acute stroke. Full article

Accurate detection and diagnosis of brain tumors at early stages is significant for effective treatment. While numerous methods have been developed for tumor detection and classification, several rely on traditional techniques, often resulting in suboptimal performance. In contrast, AI-based deep learning techniques have shown promising results, consistently achieving high accuracy across various tumor types while maintaining model interpretability. Inspired by these advancements, this paper introduces an improved variant of EfficientNet for multi-grade brain tumor detection and classification, addressing the gap between performance and explainability. Our approach extends the capabilities of EfficientNet to classify four tumor types: glioma, meningioma, pituitary tumor, and non-tumor. For enhanced explainability, we incorporate gradient-weighted class activation mapping (Grad-CAM) to improve model interpretability. The input MRI images undergo data augmentation before being passed through the feature extraction phase, where the underlying tumor patterns are learned. Our model achieves an average accuracy of 98.6%, surpassing other state-of-the-art methods on standard datasets while maintaining a substantially reduced parameter count. Furthermore, the explainable AI (XAI) analysis demonstrates the model’s ability to focus on relevant tumor regions, enhancing its interpretability. This accurate and interpretable model for brain tumor classification has the potential to significantly aid clinical decision-making in neuro-oncology. Full article

Background: Early and accurate grading of renal cell carcinoma (RCC) improves patient risk stratification and has implications for clinical management and mortality. However, current diagnostic approaches using imaging and renal mass biopsy have limited specificity and may lead to undergrading. Methods: This study explored the use of hyperpolarised [1-¹³C]pyruvate MRI (HP ¹³C-MRI) to identify the most aggressive areas within the tumour of patients with clear cell renal cell carcinoma (ccRCC) as a method to guide biopsy targeting and to reduce undergrading. Six patients with ccRCC underwent presurgical HP ¹³C-MRI and conventional contrast-enhanced MRI. From the imaging data, three k-means clusters were computed by combining the k_PL as a marker of metabolic activity, and the ¹³C-pyruvate signal-to-noise ratio (SNR_Pyr) as a perfusion surrogate. The combined clusters were compared to those derived from individual parameters and to those derived from the percentage of enhancement on the nephrographic phase (%NG). The diagnostic performance of each cluster was assessed based on its ability to predict the highest histological tumour grade in postsurgical tissue samples. The postsurgical tissue samples underwent immunohistochemical staining for the pyruvate transporter (monocarboxylate transporter 1, MCT1), as well as RNA and whole-exome sequencing. Results: The clustering approach combining SNR_Pyr and k_PL demonstrated the best performance for predicting the highest tumour grade: specificity 85%; sensitivity 64%; positive predictive value 82%; and negative predictive value 68%. Epithelial MCT1 was identified as the major determinant of the HP ¹³C-MRI signal. The perfusion/metabolism mismatch cluster showed an increased expression of metabolic genes and markers of aggressiveness. Conclusions: This study demonstrates the potential of using HP ¹³C-MRI-derived metabolic clusters to identify intratumoral variations in tumour grade with high specificity. This work supports the use of metabolic imaging to guide biopsies to the most aggressive tumour regions and could potentially reduce sampling error. Full article

Background and Objectives: This study aimed to identify and analyze imaging and pathological features that differentiate liver metastases from primary liver cancer in patients with histopathological confirmation, and to evaluate the diagnostic accuracy of imaging modalities. Materials and Methods: This retrospective study included 137 patients who underwent liver biopsy or resection between 2016 and 2024, comprising 126 patients with liver metastases and 11 patients with primary liver cancer (hepatocellular carcinoma). Imaging features on contrast-enhanced MRI were evaluated, including lesion number, size, margins, enhancement patterns, presence of capsule, T1/T2 signal characteristics, diffusion-weighted imaging (DWI) signal, and portal vein thrombosis. Laboratory data such as liver function tests and alpha-fetoprotein (AFP) levels were collected. Pathological features recorded included tumor differentiation, vascular invasion, necrosis, and fibrosis. Statistical analyses were performed using chi-squared tests, t-tests, and logistic regression, with a significance level of p < 0.05. The diagnostic accuracy of imaging features was assessed using receiver operating characteristic (ROC) curve analysis. Results: Liver metastases were more likely to present as multiple lesions (82.5% vs. 27.3%, p < 0.001), had irregular margins (78.6% vs. 36.4%, p = 0.002), rim enhancement (74.6% vs. 18.2%, p < 0.001), and were hypointense on T1-weighted images (85.7% vs. 45.5%, p = 0.004). Primary liver cancers were more likely to be solitary (72.7% vs. 17.5%, p < 0.001), have smooth margins (63.6% vs. 21.4%, p = 0.002), exhibit arterial phase hyperenhancement (81.8% vs. 23.8%, p < 0.001), and portal venous washout (72.7% vs. 19.0%, p < 0.001). Vascular invasion was more common in primary liver cancer (45.5% vs. 11.1%, p = 0.01). AFP levels > 400 ng/mL were significantly associated with primary liver cancer (63.6% vs. 4.8%, p < 0.001). ROC curve analysis showed that a combination of imaging features had an area under the curve (AUC) of 0.91 for differentiating the two entities. Conclusions: Imaging features such as lesion number, margin characteristics, enhancement patterns, T1/T2 signal characteristics, and portal venous washout, along with pathological features like vascular invasion and AFP levels, can effectively differentiate liver metastases from primary liver cancer. The diagnostic accuracy of imaging is high when multiple features are combined. Full article

Background: This study aimed to evaluate the diagnostic performance of the Kaiser score (KS) on the modified abbreviated breast magnetic resonance imaging (AB-MRI) protocol for characterizing breast lesions by comparing it with full-protocol MRI (FP-MRI), using the histological data as the reference standard. Methods: Breast MRIs detecting histologically verified contrast-enhancing breast lesions were evaluated retrospectively. A modified AB-MRI protocol was created from the standard FP-MRI, which comprised axial fat-suppressed T2-weighted imaging (T2WI), pre-contrast T1-weighted imaging (T1WI), and first, second, and fourth post-contrast phases. Two radiologists reviewed both protocols, recording the KS for each detected lesion. Sensitivity, specificity, and positive and negative predictive values, as well as accuracy, were calculated for each protocol. Receiver operating characteristic (ROC) analysis was performed to determine the diagnostic performance of the modified AB-MRI compared to the FP-MRI. Results: In total, 154 patients with 158 histopathologically proven lesions (107 malignant, 51 benign) were included. For the diagnostic performance of the KS for modified AB-MRI and FP-MRI, the sensitivity was 96.3% vs. 98.1%, the specificity was 78.4% vs. 74.5%, PPV was 90.4% vs. 89%, NPV was 90.9% vs. 95%, and the diagnostic accuracy was 90.5% vs. 90.5%. The area under the curve (AUC) obtained from the ROC curve analysis was 0.873 and 0.863 for modified AB-MRI and FP-MRI for reader 1, respectively, and 0.859 and 0.878 for modified AB-MRI and FP-MRI for reader 2, respectively, (p < 0.001). Conclusions: Our modified AB-MRI protocol revealed comparable results in terms of the diagnostic value of the KS in characterizing breast lesions compared to FP-MRI and reduced both scanning and interpretation time. Full article

Background: Accurate measurements of flow and ventricular volume and function are critical for clinical decision-making in cardiovascular medicine. Cardiac magnetic resonance (CMR) is the current gold standard for ventricular functional evaluation but is relatively expensive and time-consuming, thus limiting the scale of clinical applications. New volumetric acquisition techniques, such as four-dimensional flow (4D-flow) and three-dimensional volumetric cine (3D-cine) MRI, could potentially reduce acquisition time without loss in accuracy; however, this has not been formally tested on a large scale. Methods: 4DCarE (4D-flow MRI for cardiovascular evaluation) is a prospective, multi-centre study designed to test the non-inferiority of a compressed 20 min exam based on volumetric CMR compared with a conventional CMR exam (45–60 min). The compressed exam utilises 4D-flow together with a single breath-hold 3D-cine to provide a rapid, accurate quantitative assessment of the whole heart function. Outcome measures are (i) flow and chamber volume measurements and (ii) overall functional evaluation. Secondary analyses will explore clinical applications of 4D-flow-derived parameters, including wall shear stress, flow kinetic energy quantification, and vortex analysis in large-scale cohorts. A target of 1200 participants will enter the study across three sites. The analysis will be performed at a single core laboratory site. Pilot Results: We present a pilot analysis of 196 participants comparing flow measurements obtained by 4D-flow and conventional 2D phase contrast, which demonstrated moderate–good consistency in ascending aorta and main pulmonary artery flow measurements between the two techniques. Four-dimensional flow underestimated the flow compared with 2D-PC, by approximately 3 mL/beat in both vessels. Conclusions: We present the study protocol of a prospective non-inferiority study of a rapid cardiac MRI exam compared with conventional CMR. The pilot analysis supports the continuation of the study. Study Registration: This study is registered with the Australia and New Zealand Clinical Trials Registry (Registry number ACTRN12622000047796, Universal Trial Number: U1111-1270-6509, registered 17 January 2022—Retrospectively registered). Full article

(1) Background and Objectives: Flow assessment using cardiovascular magnetic resonance (CMR) provides important implications in determining physiologic parameters and clinically important markers. However, post-processing of CMR images remains labor- and time-intensive. This study aims to assess the validity and repeatability of fully automated segmentation of phase contrast velocity-encoded aortic root plane. (2) Materials and Methods: Aortic root images from 125 patients are segmented by artificial intelligence (AI), developed using convolutional neural networks and trained with a multicentre cohort of 160 subjects. Derived simple flow indices (forward and backward flow, systolic flow and velocity) and complex indices (aortic maximum area, systolic flow reversal ratio, flow displacement, and its angle change) were compared with those derived from manual contours. (3) Results: AI-derived simple flow indices yielded excellent repeatability compared to human segmentation (p < 0.001), with an insignificant level of bias. Complex flow indices feature good to excellent repeatability (p < 0.001), with insignificant levels of bias except flow displacement angle change and systolic retrograde flow yielding significant levels of bias (p < 0.001 and p < 0.05, respectively). (4) Conclusions: Automated flow quantification using aortic root images is comparable to human segmentation and has good to excellent repeatability. However, flow helicity and systolic retrograde flow are associated with a significant level of bias. Overall, all parameters show clinical repeatability. Full article

Background/Objectives: Focused ultrasound (FUS) and microbubble (MB) exposure is a promising technique for targeted drug delivery to the brain; however, refinement of protocols suitable for large-volume treatments in a clinical setting remains underexplored. Methods: Here, the impacts of various sonication parameters on blood–brain barrier (BBB) permeability enhancement and tissue damage were explored in rabbits using a clinical-prototype hemispherical phased array developed in-house, with real-time 3D MB cavitation imaging for exposure calibration. Initial experiments revealed that continuous manual agitation of MBs during infusion resulted in greater gadolinium (Gd) extravasation compared to gravity drip infusion. Subsequent experiments used low-dose MB infusion with continuous agitation and a low burst repetition frequency (0.2 Hz) to mimic conditions amenable to long-duration clinical treatments. Results: Key sonication parameters—target level (proportional to peak negative pressure), number of bursts, and burst length—significantly affected BBB permeability enhancement, with all parameters displaying a positive relationship with relative Gd contrast enhancement (p < 0.01). Even at high levels of BBB permeability enhancement, tissue damage was minimal, with low occurrences of hypointensities on T2*-weighted MRI. When accounting for relative Gd contrast enhancement, burst length had a significant impact on red blood cell extravasation detected in histological sections, with 1 ms bursts producing significantly greater levels compared to 10 ms bursts (p = 0.03), potentially due to the higher pressure levels required to generate equal levels of BBB permeability enhancement. Additionally, albumin and IgG extravasation correlated strongly with relative Gd contrast enhancement across sonication parameters, suggesting that protein extravasation can be predicted from non-invasive imaging. Conclusions: These findings contribute to the development of safer and more effective clinical protocols for FUS + MB exposure, potentially improving the efficacy of the approach. Full article

Background and objectives: The premanifest phase of Huntington’s disease (HD) is characterized by the absence of motor symptoms and exhibits structural changes in imaging that precede clinical manifestation. This study aimed to analyze volumetric changes identified through brain magnetic resonance imaging (MRI) processed using artificial intelligence (AI) software in premanifest HD individuals, focusing on the relationship between CAG triplet expansion and structural biomarkers. Methods: The study included 36 individuals descending from families affected by HD in the Department of Atlántico. Sociodemographic data were collected, followed by peripheral blood sampling to extract genomic DNA for quantifying CAG trinucleotide repeats in the Huntingtin gene. Brain volumes were evaluated using AI software (Entelai/IMEXHS, v4.3.4) based on MRI volumetric images. Correlations between brain volumes and variables such as age, sex, and disease status were determined. All analyses were conducted using SPSS (v. IBM SPSS Statistics 26), with significance set at p < 0.05. Results: The analysis of brain volumes according to CAG repeat expansion shows that individuals with ≥40 repeats evidence significant increases in cerebrospinal fluid (CSF) volume and subcortical structures such as the amygdalae and left caudate nucleus, along with marked reductions in cerebral white matter, the cerebellum, brainstem, and left pallidum. In contrast, those with <40 repeats show minimal or moderate volumetric changes, primarily in white matter and CSF. Conclusions: These findings suggest that CAG expansion selectively impacts key brain regions, potentially influencing the progression of Huntington’s disease, and that AI in neuroimaging could identify structural biomarkers long before clinical symptoms appear. Full article

Over the past year, there has been a significant rise in interest in the application of open-source artificial intelligence models (OSAIM) in the field of medicine. An increasing number of studies focus on evaluating the capabilities of these models in image analysis, including magnetic resonance imaging (MRI). This study aimed to investigate whether two of the most popular open-source AI models, namely ChatGPT 4o and Gemini Pro, can analyze MRI video sequences with single-phase contrast in sagittal and frontal projections, depicting a posterior fossa tumor corresponding to a medulloblastoma in a child. The study utilized video files from single-phase contrast-enhanced head MRI in two planes (frontal and sagittal) of a child diagnosed with a posterior fossa tumor, type medulloblastoma, confirmed by histopathological examination. Each model was separately provided with the video file, first in the sagittal plane, analyzing three different sets of commands from the most general to the most specific. The same procedure was applied to the video file in the frontal plane. The Gemini Pro model did not conduct a detailed analysis of the pathological change but correctly identified the content of the video file, indicating it was a brain MRI, and suggested that a specialist in the field should perform the evaluation. Conversely, ChatGPT 4o conducted image analysis but failed to recognize that the content was MRI. The attempts to detect the lesion were random and varied depending on the plane. These models could not accurately identify the video content or indicate the area of the neoplastic change, even after applying detailed queries. The results suggest that despite their widespread use in various fields, these models require further improvements and specialized training to effectively support medical diagnostics. Full article

Cardiac magnetic resonance imaging (MRI) is widely used for non-invasive assessment of cardiac morphology, function, and tissue characteristics due to its exquisite soft-tissue contrast. However, it remains time-consuming and requires proficiency, making it costly and limiting its widespread use. Traditional cardiac MRI is inefficient as signal acquisition is often limited to specific cardiac phases and requires complex view planning, parameter adjustments, and management of both respiratory and cardiac motion. Recent efforts have aimed to make cardiac MRI more efficient and accessible. Among these innovations, the free-running framework enables 5D whole-heart imaging without the need for an electrocardiogram signal, respiratory breath-holding, or complex planning. It uses a fully self-gated approach to extract cardiac and respiratory signals directly from the acquired image data, allowing for more efficient coverage in time and space without the need for electrocardiogram gating, triggering, navigators, or breath-holds. This review provides a comprehensive overview of the free-running framework, detailing its history, concepts, recent improvements, and clinical applications. Full article

An incidental focal liver lesion (IFLL) is defined as a hepatic lesion identified in a patient imaged for an unrelated reason. They are frequently encountered in daily practice, sometimes leading to unnecessary, invasive and potentially harmful follow-up investigations. The clinical presentation and the imaging aspects play an important role in deciding if, and what further evaluation, is needed. In low-risk patients (i.e., without a history of malignant or chronic liver disease or related symptoms), especially in those younger than 40 years old, more than 95% of IFLLs are likely benign. Shear Wave liver Elastography (SWE) of the surrounding liver parenchyma should be considered to exclude liver cirrhosis and for further risk stratification. If an IFLL in a low-risk patient has a typical appearance on B-mode ultrasound of a benign lesion (e.g., simple cyst, calcification, focal fatty change, typical hemangioma), no further imaging is needed. Contrast-Enhanced Ultrasound (CEUS) should be considered as the first-line contrast imaging modality to differentiate benign from malignant IFLLs, since it has a similar accuracy to contrast-enhanced (CE)-MRI. On CEUS, hypoenhancement of a lesion in the late vascular phase is characteristic for malignancy. CE-CT should be avoided for characterizing probable benign FLL and reserved for staging once a lesion is proven malignant. In high-risk patients (i.e., with chronic liver disease or an oncological history), each IFLL should initially be considered as potentially malignant, and every effort should be made to confirm or exclude malignancy. US-guided biopsy should be considered in those with unresectable malignant lesions, particularly if the diagnosis remains unclear, or when a specific tissue diagnosis is needed. Full article

Amplified MRI (aMRI) is a promising new technique that can visualize pulsatile brain tissue motion by amplifying sub-voxel motion in cine MRI data, but it lacks the ability to quantify the sub-voxel motion field in physical units. Here, we introduce a novel post-processing algorithm called 3D quantitative amplified MRI (3D q-aMRI). This algorithm enables the visualization and quantification of pulsatile brain motion. 3D q-aMRI was validated and optimized on a 3D digital phantom and was applied in vivo on healthy volunteers for its ability to accurately measure brain parenchyma and CSF voxel displacement. Simulation results show that 3D q-aMRI can accurately quantify sub-voxel motions in the order of 0.01 of a voxel size. The algorithm hyperparameters were optimized and tested on in vivo data. The repeatability and reproducibility of 3D q-aMRI were shown on six healthy volunteers. The voxel displacement field extracted by 3D q-aMRI is highly correlated with the displacement measurements estimated by phase contrast (PC) MRI. In addition, the voxel displacement profile through the cerebral aqueduct resembled the CSF flow profile reported in previous literature. Differences in brain motion was observed in patients with dementia compared with age-matched healthy controls. In summary, 3D q-aMRI is a promising new technique that can both visualize and quantify pulsatile brain motion. Its ability to accurately quantify sub-voxel motion in physical units holds potential for the assessment of pulsatile brain motion as well as the indirect assessment of CSF homeostasis. While further research is warranted, 3D q-aMRI may provide important diagnostic information for neurological disorders such as Alzheimer’s disease. Full article

In the present work, iron–sillenite (Bi₂₅FeO₄₀) was synthesized using a simple solid-state reaction method and characterized. The effects of the synthesis conditions on the phase purity of Bi₂O₃/Fe₃O₄, morphological features, and possible application as an XRD/MRI dual-contrast agent were investigated. For the synthesis, the stoichiometric amounts of Bi₂O₃ and Fe₃O₄ were mixed and subsequently milled in a planetary ball mill for 10 min with a speed of 300 rpm. The milled mixture was calcined at various temperatures (550 $°$C, 700 $°$C, 750 $°$C, 800 $°$C, and 850 $°$C) for 1 h in air at a heating rate of 5 °C/min. For phase identification, powder X-ray diffraction (XRD) analysis was performed and infrared (FTIR) spectra were recorded. The surface morphology of synthesized samples was studied by field-emission scanning electron microscopy (FE-SEM). For the radiopacity measurements, iron–sillenite specimens were synthesized at different temperatures and mixed with different amounts of BaSO₄ and Laponite solution. It was demonstrated that iron–sillenite Bi₂₅FeO₄₀ possessed sufficient radiopacity and could be a potential candidate to meet the requirements of its application as an XRD/MRI dual-contrast agent. Full article

This study uses magnetic resonance imaging (MRI) to investigate the potential of the hepatospecific contrast agent gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA) in distinguishing G1- from G2/G3-differentiated hepatocellular carcinoma (HCC). Our approach involved analyzing the dynamic behavior of the contrast agent in different phases of imaging by signal intensity (SI) and lesion contrast (C), to surrounding liver parenchyma, and comparing it across distinct groups of patients differentiated based on the histopathological grading of their HCC lesions and the presence of liver cirrhosis. Our results highlighted a significant contrast between well- and poorly-differentiated lesions regarding the lesion contrast in the arterial and late arterial phases. Furthermore, the hepatobiliary phase showed limited diagnostic value in cirrhotic liver parenchyma due to altered pharmacokinetics. Ultimately, our findings underscore the potential of Gd-EOB-DTPA-enhanced MRI as a tool for improving preoperative diagnosis and treatment selection for HCC while emphasizing the need for continued research to overcome the diagnostic complexities posed by the disease. Full article