搜索结果：Topics in magnetic resonance imaging : TMRI

Automated whole brain segmentation from magnetic resonance images is of great interest for the development of clinically relevant volumetric markers for various neurological diseases. Although deep learning methods have demonstrated remarkable potential in this area, they may perform poorly in nonoptimal conditions, such as limited training data availability. Manual whole brain segmentation is an incredibly tedious process, so minimizing the data set size required for training segmentation algorithms may be of wide interest. The purpose of this study was to compare the performance of the prototypical deep learning segmentation architecture (U-Net) with a previously published atlas-free traditional machine learning method, Classification using Derivative-based Features (C-DEF) for whole brain segmentation, in the setting of limited training data. C-DEF and U-Net models were evaluated after training on manually curated data from 5, 10, and 15 participants in 2 research cohorts: (1) people living with clinically diagnosed HIV infection and (2) relapsing-remitting multiple sclerosis, each acquired at separate institutions, and between 5 and 295 participants' data using a large, publicly available, and annotated data set of glioblastoma and lower grade glioma (brain tumor segmentation). Statistics was performed on the Dice similarity coefficient using repeated-measures analysis of variance and Dunnett-Hsu pairwise comparison. C-DEF produced better segmentation than U-Net in lesion (29.2%-38.9%) and cerebrospinal fluid (5.3%-11.9%) classes when trained with data from 15 or fewer participants. Unlike C-DEF, U-Net showed significant improvement when increasing the size of the training data (24%-30% higher than baseline). In the brain tumor segmentation data set, C-DEF produced equivalent or better segmentations than U-Net for enhancing tumor and peritumoral edema regions across all training data sizes explored. However, U-Net was more effective than C-DEF for segmentation of necrotic/non-enhancing tumor when trained on 10 or more participants, probably because of the inconsistent signal intensity of the tissue class. These results demonstrate that classical machine learning methods can produce more accurate brain segmentation than the far more complex deep learning methods when only small or moderate amounts of training data are available (n ≤ 15). The magnitude of this advantage varies by tissue and cohort, while U-Net may be preferable for deep gray matter and necrotic/non-enhancing tumor segmentation, particularly with larger training data sets (n ≥ 20). Given that segmentation models often need to be retrained for application to novel imaging protocols or pathology, the bottleneck associated with large-scale manual annotation could be avoided with classical machine learning algorithms, such as C-DEF.

Cardiac tumors are aggressive and asymptomatic in early stages, causing late diagnosis and locoregional metastasis. Currently, the standard of care uses gadolinium-based contrast agents for MRI, and the associated hypersensitivity reactions are a significant concern, such as gadolinium deposition disease. In addition, the proximity of cardiac lesions closer to vital structures complicates surgical interventions. We envisage the development of a scalable, Gd-free, multimodal contrast agent based on EDTA bisamide with pyridine-based fluorophore (L1). The diagnostic arm should have manganese (Mn)-enhanced high relaxivity for MRI and high sensitivity for PET and/or optical imaging (eg, fluorescence lifetime imaging), with comparable/higher than commercial diagnostic agents, along with the multikinase targeted anticancer activity and strong affinity for human serum albumin. Mn complex of EDTA bisamide of 4-(aminomethyl)pyridine (L1), MnL1, was reproduced in high yield (77%) and purity (98%), characterized by liquid chromatography-mass spectrometry (LC-MS). The solubility in water and stability in sodium acetate buffer were evaluated. T1 mapping followed by static and dynamic contrast-enhanced MRI (DCE_MRI) image acquisition, post-tail vein injection of healthy C57BL/6 mice through I.V. with 1mM of MnL1/PBS was carried out by 3T-MRI (BioSpec, Bruker), wherein standard gadobutrol was used as control. Optical properties of L1 dissolved in solvent mixtures of dimethyl sulfoxide were optimized using PhotonIMAGER RT OPTIMA by Biospace Lab with AlexaFluor750 as the positive control. Docking studies with FAP and EGFR kinases were conducted by AutoDock Vina, followed by MD simulation (My Presto). LC-MS: The highest UV absorption peak was correlated to more than 80% relative abundance of the highest molecular ion peak in mass spectra (cal: 525.18234; found 525.750), indicating strong chelation of L1 to Mn (II). 3T-MRI data of MnL1 revealed comparable performance with a standard gadobutrol. L1 exhibited multiple excitation wavelengths and NIR1 emission. DCE-MRI revealed contrasting dynamics with strong uptake in the kidney, liver, and heart. Docking studies revealed inhibition of FAP (allosteric) and EGFR (-7.0 and 6.7 Kcal/mol), validated by their respective cocrystallized ligands and commercial standards and by MD simulation, reflecting constant gyration ratios and strong hydrogen bonding. Preclinical MRI imaging justified the efficacy of Mn(II)L1. L1 validated as a promising visible and NIR1dye along with its ability to bind and inhibit pan-cancer targets, FAP (allosteric) and EGFR kinases. Previously validated features of lifetime sensing/high stokes shift and Cu (II) quenching are also noteworthy. Dual-echo acquisitions for quantitative DCE-MRI as a standalone (with T2* corrections) or in combination with PET/MRI of 64Cu-L1(separately studied) or as 52MnL1 by single injection envisaged. T1 mapping for therapy response monitoring based on the reduction of native tumor T1 upon binding of MnL1 to the kinase is hereby envisaged for the future.

Since magnetic resonance imaging (MRI) is an extensively used and fundamental diagnostic imaging method and anxiety is one of the most important confounding factors in its performance, using guided imagery is recommended. This study aimed to assess the effectiveness of guided imagery on the anxiety of patients undergoing MRI in 2023. 88 patients were randomly assigned to intervention and control groups. The intervention group listened to the nature-based guided imagery audio file during their scan, and the control group did not receive any intervention. Data were collected using demographic information and the Spielberger Anxiety Questionnaire before and after the scan. There was no significant difference between the 2 groups before the intervention regarding demographic data and anxiety. In the intervention group, the mean anxiety decreased from 104.0 &#xb1; 14.6 to 92.4 &#xb1; 9.0, showing a significant reduction in the level of anxiety in both subscales (state and trait) and the total score (P < 0.001), compared with the control group and before the intervention. The results showed that using guided imagery could decrease anxiety levels in patients undergoing MRI. Since patients' anxiety is one of the most important nursing diagnoses, performing cognitive methods, including guided imagery, as a simple, safe, inexpensive, and effective intervention should be considered.

Magnetic resonance imaging (MRI) is used for diagnosing placenta accreta spectrum disorders (PASDs) because of its advanced soft-tissue contrast and spatial resolution capabilities, offering better contrast, improved spatial resolution, and a wider field of view compared with ultrasound. Using a 1.5-Tesla MRI protocol with multiple sequences, MRI can detect indicative signs of PASD such as placental signal heterogeneity, interruption of the myometrium-placenta interface, and abnormal vascularization. Specific sequences such as T2 SSFSE, FIESTA, and T1-weighted and diffusion-weighted imaging are used to assess placental attachment, myometrial invasion, and intraplacental hemorrhages. Significant MRI findings include thick low-signal T2 intraplacental bands, invasions into the cervix or bladder, and abnormal periplacental vascularity. MRI complements ultrasound and is crucial for the prenatal diagnosis of PASD, aiding in treatment planning and patient management, thereby reducing the associated fetal and maternal morbidity and mortality. The objective of this pictorial review was to outline the placental MRI technique and review the main imaging findings in placental MRI for PASD. This review encompasses anonymized patient images obtained following written consent.

Currently, there is no evidence that MRI produces harmful effects on premature newborns, as well as short-term and long-term safety issues regarding radiofrequency fields and loud acoustic environment, while the examination that is being performed has not been clearly investigated. MRI of the brain conducted on preterm infants should be part of the diagnostic workup, when necessary. This article is intended to evaluate the short-term safety of MRI performed in preterm infants, when required, by analyzing all vital parameters available before, during, and after the MRI procedures. We conducted a systematic review of the literature on electronic medical databases (PubMed and ClinicalTrials.gov) following the Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We included all preterm infants who underwent MRI whose clinical, hemodynamic, and respiratory parameters were reported. The quality of the included articles was assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. Six studies were included with a total of 311 preterm infants. No severe adverse event, such as death, occurred during MRI procedures. Vital signs remained stable in about two-thirds of all patients. Given the general clinical safety of MRI, we suggest it as a tool to be used in preterm infants in Neonatal Intensive Care Units, when necessary. We further suggest the development of standard protocols to guide the use of MRI in preterm infants to maximize the clinical safety of the procedure.

Previous work used phantoms to calibrate the nonlinear relationship between the gadolinium contrast concentration and the intensity of the magnetic resonance imaging signal. This work proposes a new nonlinear calibration procedure without phantoms and considers the variation of contrast agent mass minimum combined with the multiple input blood flow system. This also proposes a new single-input method with meaningful variables that is not influenced by reperfusion or noise generated by aliasing. The reperfusion in the lung is usually neglected and is not considered by the indicator dilution method. However, in cases of lung cancer, reperfusion cannot be neglected. A new multiple input method is formulated, and the contribution of the pulmonary artery and bronchial artery to lung perfusion can be considered and evaluated separately. The calibration procedure applies the minimum variation of contrast agent mass in 3 different regions: (1) pulmonary artery, (2) left atrium, and (3) aorta. It was compared with four dimensional computerized tomography with iodine, which has a very high proportional relationship between contrast agent concentration and signal intensity. Nonlinear calibration was performed without phantoms, and it is in the range of phantom calibration. It successfully separated the contributions of the pulmonary and bronchial arteries. The proposed multiple input method was verified in 6 subjects with lung cancer, and perfusion from the bronchial artery, rich in oxygen, was identified as very high in the cancer region. Nonlinear calibration of the contrast agent without phantoms is possible. Separate contributions of the pulmonary artery and aorta can be determined.

7T small animal magnetic resonance imaging (MRI) was used to analyze the growth characteristics of hepatic alveolar echinococcosis (HAE). A mouse model of HAE was established by intraperitoneal injection of alveolar Echinococcus tissue suspension. Ten mouse models successfully inoculated by ultrasound screening were selected. The mouse model was scanned with T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) sequence by 7T small animal MRI. Size, morphology, boundary, signal, and relationship with surrounding tissues of the lesions were recorded as characteristic alterations. Mice were killed at the end of the experiment, and the pathological specimens were taken for routine hematoxylin and eosin staining. Lesions were mainly located in the right lobe of the liver. The multivesicular structure is the characteristic manifestation of this disease. In the liver, lesions invaded the portal vein and were mainly distributed at the hepatic hilum. The left branch of the portal vein was mainly invaded. The mean diameter of the lesions in the left lobe of the liver was larger than in other parts of the liver. The mean diameter of the cystic solid lesions was greater than the multilocular cystic lesions. HAE showed hypointense on T1WI, hyperintense on T2WI, and hypointense on DWI; the marginal zone of the lesion showed hyperintensity on DWI and grew toward the hilum. The MRI features of intraperitoneal lesions were similar to those of intrahepatic lesions. Intraperitoneal lesions increased faster than intrahepatic lesions in the same period. Polyvesicular structure is a characteristic manifestation of hepatic alveolar echinococcosis in mice. The noninvasive monitoring of liver HAE in mice by 7T small animal MRI provides a visual basis for the diagnosis and treatment integration of HAE.

Magnetic resonance elastography (MRE) allows noninvasive assessment of intracranial tumor mechanics and may thus be predictive of intraoperative conditions. Variations in the use of technical terms complicate reading of current literature, and there is need of a review using consolidated nomenclature. We present an overview of current literature on MRE relating to human intracranial neoplasms using standardized nomenclature suggested by the MRE guidelines committee. We then discuss the implications of the findings, and suggest approaches for future research. We performed a systematic literature search in PubMed, Embase, and Web of Science; the articles were screened for relevance and then subjected to full text review. Technical terms were consolidated. We identified 12 studies on MRE in patients with intracranial tumors, including meningiomas, glial tumors including glioblastomas, vestibular schwannomas, hemangiopericytoma, central nervous system lymphoma, pituitary macroadenomas, and brain metastases. The studies had varying objectives that included prediction of intraoperative consistency, histological separation, prediction of adhesiveness, and exploration of the mechanobiology of tumor invasiveness and malignancy. The technical terms were translated using standardized nomenclature. The literature was highly heterogeneous in terms of image acquisition techniques, post-processing, and study design and was generally limited by small and variable cohorts. MRE shows potential in predicting tumor consistency, adhesion, and mechanical homogeneity. Furthermore, MRE provides insight into malignant tumor behavior and its relation to tissue mechanics. MRE is still at a preclinical stage, but technical advances, improved understanding of soft tissue rheological impact, and larger samples are likely to enable future clinical introduction.

Skeletal bone age assessment for medical reasons is usually performed by conventional x-ray with use of ionizing radiation. Few pilot studies have shown the possible use of magnetic resonance imaging (MRI). To comprehensively evaluate feasibility and value of MRI for skeletal bone age (SBA) assessment in healthy male children. In this prospective cross-sectional study, 63 male soccer athletes with mean age of 12.35 &#xb1; 1.1 years were examined. All participants underwent 3.0 Tesla MRI with coronal T1-weighted turbo spin echo (TSE), coronal proton density (PD)-weighted turbo spin echo (TSE), and T1-weighted three-dimensional (3D) volume interpolated breath-hold examination (VIBE) sequence. Subsequently, SBA was assessed by 3 independent blinded radiologists with different levels of experience using the common Greulich-Pyle (GP) atlas and the Tanner-Whitehouse (TW2) method. In a mean total acquisition time of 5:04 &#xb1; 0:47 min, MR image quality was sufficient in all cases. MRI appraisal was significantly faster ( P < 0.0001) by GP with mean duration of 1:22 &#xb1; 0:08 min vs. 7:39 &#xb1; 0:28 min by TW. SBA assessment by GP resulted in mean age of 12.8 &#xb1; 1.2 years, by TW 13.0 &#xb1; 1.4 years. Interrater reliabilities were excellent for both GP (ICC = 0.912 (95% confidence interval [CI] = 0.868-0.944) and TW (ICC = 0.988 (95% CI = 0.980-0.992) and showed statistical significance ( P < 0.001). Subdivided, for GP, ICCs were 0.822 (95% CI = 0.680-0.907) and 0.843 (95% CI = 0.713-0.919) in Under 12 and Under 14 group. For TW, ICCs were 0.978 (95% CI = 0.958-0.989) in Under 12 and 0.979 (95% CI = 0.961-0.989) in Under 14 group. MRI is a clinically feasible, rapidly evaluable method to assess skeletal bone age of healthy male children. Using the Greulich-Pyle (GP) atlas or the Tanner-Whitehouse (TW2) method, reliable results are obtained independent of the radiologist's experience level.

Magnetic resonance imaging (MRI) is increasingly used in postmortem fetal imaging. Several factors influence the quality of MRI in this setting, such as small size, autolytic and maceration changes, and temperature. Knowing the fetal temperature at the time of scanning can improve the MRI interpretation. Temperature can be calculated using diffusion-weighted imaging with measurements of the apparent diffusion coefficient (ADC) in the cerebrospinal fluid (CSF). However, this is complicated by small ventricle size and hemorrhage and, therefore, may be unreliable in postmortem imaging. The current study evaluated the feasibility and reliability of using the ADC for temperature measurements of the vitreous body compared to that of CSF. Two lambs were scanned postmortem at five different time points over 28&#x200a;hours. Furthermore, 10 stillborn fetuses were scanned once, at 4 to 62&#x200a;hours after birth. The temperature was measured with a digital thermometer and calculated using the ADCs of the vitreous body (lambs and fetuses) and CSF (fetuses). There was an excellent correlation between measured and calculated temperatures in vitreous bodies of lambs (r&#x200a;=&#x200a;0.997, P&#x200a;<&#x200a;0.001) and fetuses (r&#x200a;=&#x200a;0.970, P&#x200a;<&#x200a;0.001). The correlation between measured and calculated temperatures in the CSF was poor (r&#x200a;=&#x200a;0.522, P&#x200a;=&#x200a;0.122). The calculation of the temperature based on the ADC of the vitreous body is feasible and reliable for postmortem fetal imaging.

Brain metastases (BMs) are the most common intracranial malignancy, often arising from lung, breast, and melanoma cancers. Receptor tyrosine kinases, such as EGFR and HER2, drive tumor progression and resistance to therapy. Noninvasive detection of these biomarkers, especially in brain metastases, is crucial due to challenges with traditional biopsy methods. This systematic review and meta-analysis assess machine learning (ML)-based models for detecting EGFR mutations and HER2 overexpression in metastatic brain adenocarcinoma using MRI-derived radiomic features. A systematic review and meta-analysis were conducted following PRISMA 2020 guidelines. Studies were identified via PubMed, Scopus, and Web of Science, focusing on ML applications to MRI radiomics for detecting EGFR and HER2 in brain metastases. Data on study design, imaging modality, model type, sample size, and performance metrics were extracted. Subgroup analyses were performed by model type (deep learning vs. classical ML) and sample size (<150 vs. &#x2265;150 participants). A random-effects model was used to pool performance metrics, and risk of bias was assessed using the RoB 2 tool. STATA version 18 and Python 3.10 were used for analyses and visualizations. Of 383 identified studies, 31 (7925 participants) met the inclusion criteria. The pooled analysis showed strong diagnostic performance: AUC = 0.84, accuracy = 0.86, and sensitivity = 0.83. Subgroup analysis revealed higher AUC and accuracy in deep learning models compared with classical ML. Sensitivity analysis also indicated improved AUC in studies with larger sample sizes (&#x2265;150), though variability remained. No evidence of heterogeneity or publication bias was detected. ML models demonstrate strong diagnostic performance for detecting EGFR and HER2 in metastatic brain adenocarcinoma, supporting their potential as noninvasive diagnostic tools. However, these findings should be interpreted considering methodological heterogeneity and the limited use of external validation. Further prospective, multicenter studies are warranted to confirm their clinical applicability and generalizability.

Longitudinal associations of noninvasive 2-dimensional phase-contrast magnetic resonance imaging (2D-PC-MRI) velocity markers of the superficial femoral artery (SFA) were analyzed along with the characteristics of peripheral artery disease (PAD). We hypothesized that the 2-year differences in MRI-based measures of SFA velocity were associated with longitudinal changes in markers of PAD. A total of 33 (11 diabetic, 22 nondiabetic) patients with PAD with baseline and 2-year follow-up MRI scans were included in this secondary analysis of the Effect of Lipid Modification on Peripheral Artery Disease after Endovascular Intervention Trial (ELIMIT). Electrocardiographically gated 2D-PC-MRI was performed at a proximal and a distal location of the distal SFA territory. SFA lumen, wall, and total vessel volumes and the normalized wall index (NWI) were analyzed. Baseline characteristics did not differ between diabetic and nondiabetic PAD patients. Maximum proximal and distal SFA velocity measures did not differ between baseline and 2 years (41.98 interquartile range (IQR) (23.58-72.6) cm/s vs. 40.31 IQR (26.69-61.29) cm/s; P = 0.30). Pooled analysis (N = 33) showed that the 24-month change in the NWI was inversely associated with the 24-month change in the proximal maximal SFA velocity (beta = -168.36, R2 = 0.150, P value = 0.03). The 24-month change of the maximum velocity differences between the proximal and distal SFA locations was inversely associated with the 24-month changes in peak walking distance (beta = -0.003, R2 = 0.360, P value = 0.011). The 2-year change of SFA plaque burden is inversely associated with the 2-year change of proximal peak SFA blood flow velocity. 2D-PC-MRI measured SFA velocity may be of interest in assessing PAD longitudinally.

Functional 1H magnetic resonance spectroscopy (fMRS) is a derivative of dynamic MRS imaging. This modality links physiologic metabolic responses with available activity and measures absolute or relative concentrations of various metabolites. According to clinical evidence, the mitochondrial glycolysis pathway is disrupted in many nervous system disorders, especially Alzheimer disease, resulting in the activation of anaerobic glycolysis and an increased rate of lactate production. Our study evaluates fMRS with J-editing as a cutting-edge technique to detect lactate in Alzheimer disease. In this modality, functional activation is highlighted by signal subtractions of lipids and macromolecules, which yields a much higher signal-to-noise ratio and enables better detection of trace levels of lactate compared with other modalities. However, until now, clinical evidence is not conclusive regarding the widespread use of this diagnostic method. The complex machinery of cellular and noncellular modulators in lactate metabolism has obscured the potential roles fMRS imaging can have in dementia diagnosis. Recent developments in MRI imaging such as the advent of 7 Tesla machines and new image reconstruction methods, coupled with a renewed interest in the molecular and cellular basis of Alzheimer disease, have reinvigorated the drive to establish new clinical options for the early detection of Alzheimer disease. Based on the latter, lactate has the potential to be investigated as a novel diagnostic and prognostic marker for Alzheimer disease.

Sinonasal tumors are relatively rare and radiographically challenging to evaluate due to their wide variety of pathologies and imaging features. However, sinonasal tumors possessing somatostatin receptor overexpression have the benefit of utilizing a multimodality anatomic and functional imaging for a more comprehensive evaluation. This is particularly evident with esthesioneuroblastoma, with computed tomography and magnetic resonance imaging defining the anatomic extent of the tumor, whereas somatostatin receptor imaging, particularly with gallium-68 DOTATATE positron emission tomography/computed tomography, is used to assess the presence of metastatic disease for staging purposes as well as in the surveillance for tumor recurrence. In addition, areas which accumulate gallium-68 DOTATATE are potentially amenable to treatment with peptide receptor radionuclide therapy. In this manner, a combined approach of anatomic and functional imaging is critical for optimal imaging evaluation and treatment strategy of patients with sinonasal tumors.

Introduction:Breast cancer is a global issue impacting women's well-being, highlighting the importance of early detection to improve treatment outcomes and decrease mortality rates. This study aimed to assess various AI methodologies to classify breast images into normal, benign, and malignant. A hybrid of 4 CNN-pertained networks-Res-Net18, Mobile-Net, Shuffle-Net, and Inception-V3-were applied on 269 mammograms and 267 dynamic contrast-enhanced MRI examinations. Transfer learning was used, adapting the last fully connected layers to classify 3 classes (normal, benign, and malignant) resulting in 12 features. Support vector machine was employed to categorize images. Classifier performances were evaluated using the confusion matrix, accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating curve (AUC-ROC). Res-Net model achieved the highest accuracy, sensitivity, and specificity of 90.89%, 90.93%, and 95.39%, respectively. Whereas Shuffle-Net displayed the lowest accuracy of 84.76%. The AUC ranged between 0.95 and 0.97 among pretrained networks while it was higher (0.99) for the hybrid model. For MRI image classification, the Mobile-net network recorded the highest accuracy, sensitivity, and specificity of 88.55%, 88.49%, and 94.22%, respectively, while the Res-Net exhibited the lowest accuracy of 84.35%. The AUC ranged between 0.94 and 0.96 among pretrained networks while it was higher (0.98) for the Hybrid model. ResNet-18 showed the most optimal model for extracting features from mammograms compared with other CNN networks (Mobile-Net, Shuffle-Net, and Inception-V3) while Mobile-Net model was the most suitable in MRI. The effectiveness of deep learning in accurately classifying mammograms and MRI images can be improved by using a hybrid model.

Recent advances in technology, particularly in the field of magnetic resonance imaging, have brought forth new sequences, including vessel wall imaging (VWI). Traditionally, the workup for intracranial vascular pathology has always turned to luminal imaging using computed tomography angiography, magnetic resonance angiography, or digital subtraction angiography. Since its introduction, VWI has enabled researchers and practicing clinicians to better understand disease processes and manage patients to the best standard of care possible. Spontaneous recanalization in acute ischemic stroke (AIS) is a known but understudied phenomenon. Available literature has looked at this phenomenon and postulated the occurrence based on conventional cross-sectional imaging and angiography; however, objective evidence pointing to the occurrence of this phenomenon is scarce. We would like to share our experience using VWI in a patient who was clinically suspected to have a middle cerebral artery syndrome at onset, with resolution of the symptoms 3 hours after initial presentation. VWI showed vessel wall enhancement at the suspected vessel involved, with evidence of acute infarcts at the vascular territory supplied. A presumptive diagnosis of AIS with spontaneous recanalization was made. Our experience could potentially aid in the understanding of spontaneous recanalization in patients with AIS, particularly in the postulation of the pathophysiology.

Magnetic resonance imaging (MRI) is essential for diagnosis but often induces anxiety, especially in claustrophobic patients, potentially affecting image quality. This study compared oxygen saturation, heart rate, and anxiety levels between claustrophobic and non-claustrophobic patients undergoing closed and open MRI in Erbil, Iraq. The comparative study was conducted from October 2024 to April 2025 in the Radiology Departments of Consultant Medical City and Top Med Medical Complex Centers in Erbil using purposive sampling. The questionnaire contained 3 sections: sociodemographic variables, the Claustrophobia Questionnaire, and the State-Trait Anxiety Inventory-State Subscale. Physiological measures (oxygen saturation and heart rate) were recorded at 3 timepoints: pre-, mid-, and post-MRI. Statistical analyses included one-way ANOVA, repeated measures ANOVA, post hoc tests, and both univariate and multiple linear regression, using SPSS version 26. A total of 125 participants were involved in the study. The mean anxiety score was moderate, with higher levels in claustrophobic patients. Claustrophobia scores also fell within the moderate range, indicating psychological discomfort during the MRI procedure. Physiological measurements showed that claustrophobic patients, particularly those undergoing closed MRI, experienced elevated heart rates and reduced oxygen saturation compared to non-claustrophobic individuals. Statistical analysis indicated a strong positive association between anxiety and claustrophobia, with scan entry direction, age, and sex also being significant predictors of claustrophobic responses. Claustrophobic patients undergoing closed MRI experience increased anxiety and physiological distress. Open MRI systems and pre-scan anxiety screening are recommended to enhance patient comfort and diagnostic outcomes.

To develop and evaluate a deep learning technique for the differentiation of hepatocellular carcinoma (HCC) using "simplified intravoxel incoherent motion (IVIM) parameters" derived from only 3 b-value images. Ninety-eight retrospective magnetic resonance imaging data were collected (68 men, 30 women; mean age 59 &#xb1; 14 years), including T2-weighted imaging with fat suppression, in-phase, out-of-phase, and diffusion-weighted imaging (b = 0, 100, 800 s/mm2). Ninety percent of data were used for stratified 10-fold cross-validation. After data preprocessing, diffusion-weighted imaging images were used to compute simplified IVIM and apparent diffusion coefficient (ADC) maps. A 17-layer 3D convolutional neural network (3D-CNN) was implemented, and the input channels were modified for different strategies of input images. The 3D-CNN with IVIM maps (ADC, f, and D*) demonstrated superior performance compared with other strategies, achieving an accuracy of 83.25 &#xb1; 6.24% and area under the receiver-operating characteristic curve of 92.70 &#xb1; 8.24%, significantly surpassing the baseline of 50% (P < 0.05) and outperforming other strategies in all evaluation metrics. This success underscores the effectiveness of simplified IVIM parameters in combination with a 3D-CNN architecture for enhancing HCC differentiation accuracy. Simplified IVIM parameters derived from 3 b-values, when integrated with a 3D-CNN architecture, offer a robust framework for HCC differentiation.

In this review article, we present the latest developments in quantitative imaging biomarkers based on magnetic resonance imaging (MRI), applied to the diagnosis, assessment of response to therapy, and assessment of prognosis of Crohn disease. We also discuss the biomarkers' limitations and future prospects. We performed a literature search of clinical and translational research in Crohn disease using diffusion-weighted MRI (DWI-MRI), dynamic contrast-enhanced MRI (DCE-MRI), motility MRI, and magnetization transfer MRI, as well as emerging topics such as T1 mapping, radiomics, and artificial intelligence. These techniques are integrated in and combined with qualitative image assessment of magnetic resonance enterography (MRE) examinations. Quantitative MRI biomarkers add value to MRE qualitative assessment, achieving substantial diagnostic performance (area under receiver-operating curve = 0.8-0.95). The studies reviewed show that the combination of multiple MRI sequences in a multiparametric quantitative fashion provides rich information that may help for better diagnosis, assessment of severity, prognostication, and assessment of response to biological treatment. However, the addition of quantitative sequences to MRE examinations has potential drawbacks, including increased scan time and the need for further validation before being used in therapeutic drug trials as well as the clinic.