Advanced Imaging Research Articles

Artificial Intelligence and Machine Learning in Hepatocellular Carcinoma Screening, Diagnosis and Treatment - A Comprehensive Systematic Review

Background: Medical image analysis plays a crucial role in the screening, monitoring, diagnosis, and prognosis of diseases. Hepatocellular Carcinoma (HCC) often remain asymptomatic in their early stages. Timely identification and intervention are crucial to prevent the progression to decompensated liver diseases and advanced-stage HCC, minimizing morbidity and mortality. Methodology: This study examined twenty relevant research articles that met the inclusion criteria. Utilizing the PRISMA criteria, a systematic search was conducted on PubMed/MEDLINE and Google Scholar for studies on HCC screening employing Artificial Intelligence (AI) and Machine Learning (ML). The search focused on the keywords "artificial intelligence" and "hepatocellular carcinoma," with inclusion criteria specifying studies in the English language published in and after 2020. Exclusions were made for histology, animal research, and investigations conducted before 2020. Titles and abstracts were thoroughly reviewed, and any discrepancies were discussed. Results: The comprehensive review reveals the transformative impact of AI and ML on HCC screening, diagnosis, and therapy. ML models demonstrated effectiveness in early HCC diagnosis, distinguishing hepatic lesions, predicting treatment responses, and assessing recurrence risks across various techniques. Integration of mass spectrometry-based technologies, advanced imaging, and real-world data significantly improved diagnostic accuracy and clinical decision support. AI models exhibited diagnostic expertise with potential applications in therapy suggestions, personalized surveillance, and prognostic evaluations. However, further validation and seamless integration into clinical practice are essential for realizing their full potential. Conclusion: This systematic study underscores the progress made in the application of AI and ML in HCC screening, diagnosis, and treatment. Numerous studies highlight the capability of AI and ML systems to enhance hepatocellular carcinoma diagnosis, predict outcomes, and optimize therapy. The enduring and versatile nature of these technologies points towards a revolutionary future in personalized and efficient HCC management.

Production of carbon-11 for PET preclinical imaging using a high-repetition rate laser-driven proton source

Most advanced medical imaging techniques, such as positron-emission tomography (PET), require tracers that are produced in conventional particle accelerators. This paper focuses on the evaluation of a potential alternative technology based on laser-driven ion acceleration for the production of radioisotopes for PET imaging. We report for the first time the use of a high-repetition rate, ultra-intense laser system for the production of carbon-11 in multi-shot operation. Proton bunches with energies up to 10–14 MeV were systematically accelerated in long series at pulse rates between 0.1 and 1 Hz using a PW-class laser. These protons were used to activate a boron target via the 11\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{11}$$\\end{document}B(p,n)11\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{11}$$\\end{document}C nuclear reaction. A peak activity of 234 kBq was obtained in multi-shot operation with laser pulses with an energy of 25 J. Significant carbon-11 production was also achieved for lower pulse energies. The experimental carbon-11 activities measured in this work are comparable to the levels required for preclinical PET, which would be feasible by operating at the repetition rate of current state-of-the-art technology (10 Hz). The scalability of next-generation laser-driven accelerators in terms of this parameter for sustained operation over time could increase these overall levels into the clinical PET range.

Advanced Imaging of Gout and Other Inflammatory Diseases Around the Knee.

The knee is one of the most commonly affected joints in the course of inflammatory arthropathies, such as crystal-induced and autoimmune inflammatory arthritis. The latter group includes systemic connective tissue diseases and spondyloarthropathies. The different pathogenesis of these entities results in their varied radiologic images. Some lead quickly to joint destruction, others only after many years, and in the remaining, destruction will not be a distinguishing radiologic feature.Radiography, ultrasonography, and magnetic resonance imaging have traditionally been the primary modalities in the diagnosis of noninflammatory and inflammatory arthropathies. In the case of crystallopathies, dual-energy computed tomography has been introduced. Hybrid techniques also offer new diagnostic opportunities. In this article, we discuss the pathologic findings and imaging correlations for crystallopathies and inflammatory diseases of the knee, with an emphasis on recent advances in their imaging diagnosis.

Neoplastic and Non-neoplastic Bone Lesions of the Knee.

Numerous anatomical variants are described around the knee, many of which look like bony lesions, so it is important to know them to avoid unnecessary complementary tests and inadequate management. Likewise, several alterations in relation to normal development can also simulate bone lesions.However, numerous pathologic processes frequently affect the knee, including traumatic, inflammatory, infectious, and tumor pathology. Many of these entities show typical radiologic features that facilitate their diagnosis. In other cases, a correct differential diagnosis is necessary for proper clinical management.Despite the availability of increasingly advanced imaging techniques, plain radiography is still the technique of choice in the initial study of many of these pathologies. This article reviews the radiologic characteristics of tumor and nontumor lesions that may appear around the knee to make a correct diagnosis and avoid unnecessary complementary radiologic examinations and inadequate clinical management.

Advanced Imaging of Total Knee Arthroplasty.

The prevalence of total knee arthroplasty (TKA) is increasing with the aging population. Although long-term results are satisfactory, suspected postoperative complications often require imaging with the implant in place. Advancements in computed tomography (CT), such as tin prefiltration, metal artifact reduction algorithms, dual-energy CT with virtual monoenergetic imaging postprocessing, and the application of cone-beam CT and photon-counting detector CT, allow a better depiction of the tissues adjacent to the metal. For magnetic resonance imaging (MRI), high bandwidth (BW) optimization, the combination of view angle tilting and high BW, as well as multispectral imaging techniques with multiacquisition variable-resonance image combination or slice encoding metal artifact correction, have significantly improved imaging around metal implants, turning MRI into a useful clinical tool for patients with suspected TKA complications.

Habitat-specific allocations of elements in Atriplex lentiformis seeds hint at adaptation to metal toxicity.

Self-sustaining vegetation in metal-contaminated areas is essential for rebuilding the ecological resilience and community stability in degraded lands. Metal-tolerant plants originating from contaminated post-mining areas may hold the key to successful plant establishment and growth. Yet, little is known about the impact of metal toxicity on reproductive strategies, metal accumulation and allocation patterns at the seed stage. Our research focused on metal tolerant Atriplex lentiformis, examining the effects of toxic metal(loid) concentration in soils on variability in its reproductive strategies, including germination patterns, elemental uptake, and allocation within the seeds. We employed advanced imaging techniques like synchrotron X-ray Fluorescence Microscopy (XFM; 2D scans and 3D tomograms) combined with ICP-MS to reveal significant differences in metal(loid) concentration and distribution within the seed structures of A. lentiformis from contrasting habitats. Exclusive Zn hotspots of high concentrations were found in the seeds of the metallicolous accession, primarily in the sensitive tissues of shoot apical meristems and root zones of the seed embryos. The findings of this study offer novel insights into phenotypic variability, metal tolerance and accumulation in plants from extreme environments. This knowledge can be applied to enhance plant survival and performance in land restoration efforts.

Noninvasive assessment of Ki-67 labeling index in glioma patients based on multi-parameters derived from advanced MR imaging

PurposeTo investigate the predictive value of multi-parameters derived from advanced MR imaging for Ki-67 labeling index (LI) in glioma patients.Materials and MethodsOne hundred and nine patients with histologically confirmed gliomas were evaluated retrospectively. These patients underwent advanced MR imaging, including dynamic susceptibility-weighted contrast enhanced MR imaging (DSC), MR spectroscopy imaging (MRS), diffusion-weighted imaging (DWI) and diffusion-tensor imaging (DTI), before treatment. Twenty-one parameters were extracted, including the maximum, minimum and mean values of relative cerebral blood flow (rCBF), relative cerebral blood volume (rCBV), relative mean transit time (rMTT), relative apparent diffusion coefficient (rADC), relative fractional anisotropy (rFA) and relative mean diffusivity (rMD) respectively, and ration of choline (Cho)/creatine (Cr), Cho/N-acetylaspartate (NAA) and NAA/Cr. Stepwise multivariate regression was performed to build multivariate models to predict Ki-67 LI. Pearson correlation analysis was used to investigate the correlation between imaging parameters and the grade of glioma. One-way analysis of variance (ANOVA) was used to explore the differences of the imaging parameters among the gliomas of grade II, III, and IV.ResultsThe multivariate regression showed that the model of five parameters, including rCBVmax (RC=0.282), rCBFmax (RC=0.151), rADCmin (RC= -0.14), rFAmax (RC=0.325) and Cho/Cr ratio (RC=0.157) predicted the Ki-67 LI with a root mean square (RMS) error of 0. 0679 (R2 = 0.8025).The regression check of this model showed that there were no multicollinearity problem (variance inflation factor: rCBVmax, 3.22; rCBFmax, 3.14; rADCmin, 1.96; rFAmax, 2.51; Cho/Cr ratio, 1.64), and the functional form of this model was appropriate (F test: p=0.682). The results of Pearson correlation analysis showed that the rCBVmax, rCBFmax, rFAmax, the ratio of Cho/Cr and Cho/NAA were positively correlated with Ki-67 LI and the grade of glioma, while the rADCmin and rMDmin were negatively correlated with Ki-67 LI and the grade of glioma.ConclusionCombining multiple parameters derived from DSC, DTI, DWI and MRS can precisely predict the Ki-67 LI in glioma patients.

CVG-Net: novel transfer learning based deep features for diagnosis of brain tumors using MRI scans

Brain tumors present a significant medical challenge, demanding accurate and timely diagnosis for effective treatment planning. These tumors disrupt normal brain functions in various ways, giving rise to a broad spectrum of physical, cognitive, and emotional challenges. The daily increase in mortality rates attributed to brain tumors underscores the urgency of this issue. In recent years, advanced medical imaging techniques, particularly magnetic resonance imaging (MRI), have emerged as indispensable tools for diagnosing brain tumors. Brain MRI scans provide high-resolution, non-invasive visualization of brain structures, facilitating the precise detection of abnormalities such as tumors. This study aims to propose an effective neural network approach for the timely diagnosis of brain tumors. Our experiments utilized a multi-class MRI image dataset comprising 21,672 images related to glioma tumors, meningioma tumors, and pituitary tumors. We introduced a novel neural network-based feature engineering approach, combining 2D convolutional neural network (2DCNN) and VGG16. The resulting 2DCNN-VGG16 network (CVG-Net) extracted spatial features from MRI images using 2DCNN and VGG16 without human intervention. The newly created hybrid feature set is then input into machine learning models to diagnose brain tumors. We have balanced the multi-class MRI image features data using the Synthetic Minority Over-sampling Technique (SMOTE) approach. Extensive research experiments demonstrate that utilizing the proposed CVG-Net, the k-neighbors classifier outperformed state-of-the-art studies with a k-fold accuracy performance score of 0.96. We also applied hyperparameter tuning to enhance performance for multi-class brain tumor diagnosis. Our novel proposed approach has the potential to revolutionize early brain tumor diagnosis, providing medical professionals with a cost-effective and timely diagnostic mechanism.

A Bibliometric Analysis of Publications on Cadavers: A Study Based on Web of Science Data From 1978 to 2023.

This study aimed to investigate the significance of publications examining the effectiveness of cadaver studies in the field of medicine with the method of bibliometric analysis, which emerged in the 1950s, offers the opportunity to conduct a detailed analysis of a specific subject, just like a systematic literature review or meta-analysis. Also, it aimed to enlighten the content of cadaver studies in the last half-century and to present a perspective for the future. In the study, an advanced search was conducted on the Web of Science Core Collection database on August 1, 2023, using the keywords "cadaver," "cadaver study," "cadaveric dissection." Review articles were excluded from the study. There were determined 34554 documents. The documents were transferred to the VOSviewer software program. In this way, the authors made detailed analyses of authors, keywords, journals, organizations, institutions, and countries and created scientific maps. The United States was one of the most important countries in terms of research. Generally, the use of cadaver terms in documents belongs to surgery, anatomy, and transplantation journals. Anatomy, cadaver, biomechanics, ultrasound, and computed tomography were the top 5 most frequently used keywords with cadaver term. The findings of our study showed that cadaver studies are used in many fields of medical science. However, clinical studies, including advanced imaging techniques, draw attention to the developing technology. Along with the powerful institutions of the United States, its great contributions to publications stand out.

ADVANCED CARDIAC IMAGING IN LIVER TRANSPLANTATION ECHOCARDIOGRAPHIC VENTRICULAR STRAIN AS PROGNOSTIC TOOLS

Abstract While cardiac involvement in the end–stage liver disease (ESLD) is a known condition, the characteristics of cardiac function in cirrhotic cardiomyopathy and their effects on candidates to liver transplant (LT) are still vague. The evaluation of right ventricular failure (RVF) in liver transplant candidates has been revolutionized by advanced cardiac imaging techniques, particularly echocardiography with global longitudinal strain (GLS) analysis. Aim of the present study is to evaluate RV function indexes in ESLD patients candidate to LT and the potential relationship with the liver disease status and cardiovascular major events on early post–LT phase Forty ESDL adult patients (age 46.8 ± 10 yrs, M/F 33/7) candidate to LT (19 for hepatic failure, 18 for hepatocarcinoma and 3 for other causes) were evaluate. All were submitted to transthoracic echocardiography and coronary CT angiography or coronary angiography. The Model for End–stage Liver Disease (MELD) score in the study population was 19±6.8. The higher values of the MELD score (i.e. the worst liver function condition) were directly correlated with the lower RV–GLS (p<0.02). Cardiac function indexes were LV–EF 62.8±4.5%, RV–GLS –22.9±4.5%, LV–GLS –19.5±4.6% and PAPs 31.8±6.6 mmHg. On 21 day follow–up, 4 patients died (2 with acute RV failure, 1 with Tako–Tsubo syndrome). On CT–angiography the value of calcium score was significantly different between the group of fatal events and the rest (0 vs 567±900), revealing that none of the died patient suffered from coronary atheromatic disease. Our findings identified that specific RV–GLS patterns on echocardiography, indicative of subclinical myocardial dysfunction, were strongly correlated with worst clinical hepatic conditions and reduced survival rates post–transplant. The study highlights the importance of advanced cardiac imaging in identifying liver transplant candidates at higher risk of post–transplant mortality due to RHF.

Cortico-limbic interactions and carotid atherosclerotic burden during chronic stress exposure.

Chronic stress associates with cardiovascular disease, but mechanisms remain incompletely defined. Advanced imaging was used to identify stress-related neural imaging phenotypes associated with atherosclerosis. Twenty-seven individuals with post-traumatic stress disorder (PTSD), 45 trauma-exposed controls without PTSD, and 22 healthy controls underwent 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI). Atherosclerotic inflammation and burden were assessed using 18F-FDG PET (as maximal target-to-background ratio, TBR max) and MRI, respectively. Inflammation was assessed using high-sensitivity C-reactive protein (hsCRP) and leucopoietic imaging (18F-FDG PET uptake in spleen and bone marrow). Stress-associated neural network activity (SNA) was assessed on 18F-FDG PET as amygdala relative to ventromedial prefrontal cortex (vmPFC) activity. MRI diffusion tensor imaging assessed the axonal integrity (AI) of the uncinate fasciculus (major white matter tract connecting vmPFC and amygdala). Median age was 37 years old and 54% of participants were female. There were no significant differences in atherosclerotic inflammation between participants with PTSD and controls; adjusted mean difference in TBR max (95% confidence interval) of the aorta 0.020 (-0.098, 0.138), and of the carotids 0.014 (-0.091, 0.119). Participants with PTSD had higher hsCRP, spleen activity, and aorta atherosclerotic burden (normalized wall index). Participants with PTSD also had higher SNA and lower AI. Across the cohort, carotid atherosclerotic burden (standard deviation of wall thickness) associated positively with SNA and negatively with AI independent of Framingham risk score. In this study of limited size, participants with PTSD did not have higher atherosclerotic inflammation than controls. Notably, impaired cortico-limbic interactions (higher amygdala relative to vmPFC activity or disruption of their intercommunication) associated with carotid atherosclerotic burden. Larger studies are needed to refine these findings.

Magnetic resonance imaging for acute appendicitis in pregnancy: can clinical scores predict when imaging is needed?

Advanced imaging may augment the diagnostic milieux for presumed acute appendicitis (AA) during pregnancy, however it is not clear when such imaging modalities are indicated. The aim of this study was to assess the sensitivity and specificity of clinical scoring systems with the findings on magnetic resonance imaging (MRI) of AA in pregnant patients. A retrospective cohort study between 2019 and 2021 was performed in two tertiary level centers. Pregnant patients presenting with suspected AA and non-diagnostic trans-abdominal ultrasound who underwent MRI as part of their evaluation were identified. Patient demographics, parity, gestation, presenting signs, and symptoms were documented. The Alvarado and Appendicitis Inflammatory Response (AIR) score for each patient were calculated and correlated with clinical and MRI findings. Univariate analysis was used to identify factors associated with AA on MRI. Of the 255 pregnant patients who underwent MRI, 33 (13%) had findings of AA. On univariate analysis, presentation during the second/third trimester, migration of pain, vomiting and RLQ tenderness correlated with MRI findings of AA. Whilst 5/77 (6.5%) of patients with an Alvarado score ≤4 had signs of AA on MRI, a score of ≥5 had a sensitivity, specificity, negative and positive predictive value of 84.8%, 36.6%, 94.0% and 17.2%. For an AIR score ≥ 5, this was 78.8%, 41.5%, 93.0%, and 16.7%, respectively. Whilst clinical scoring systems may be useful in identifying which pregnant patients require MRI to be performed when AA is suspected, the low sensitivity implies further research is needed to refine the use of this valuable resource.

Remnant cholesterol is associated with unstable carotid plaque in a neurologically healthy population

BackgroundRemnant cholesterol (RC) is considered to be one of the most significant and important risk factors for atherosclerotic cardiovascular disease (ASCVD). Nonetheless, the association between RC and unstable carotid plaque...

The Role of CT scan in the Diagnosis of Ileosigmoid Knot: A Case Report and Literature Review

The ileosigmoid knot, although rare, represents a complex surgical emergency requiring rapid and precise man agement. Imaging, particularly computed tomography (CT), plays a crucial role in diagnosis by highlighting specific features such as the "spiral sign" and signs of necrosis. Early detection through these imaging modalities allows for prompt surgical intervention, minimizing the risks of severe complications associated with intestinal torsion. The speed of surgical intervention is crucial to prevent progression to ischemic necrosis, reduce the risks of intestinal perforation, peritonitis, and other serious complications. Various surgical modalities, such as colonic diversion, detorsion, segmental resection, or extended resection in case of necrosis, must be tailored to the clinical presentation and intraoperative findings. Ultimately, the combination of advanced radiological imaging and swift surgical intervention is the key to successful management of the ileosigmoid knot. Emphasizing awareness among healthcare professionals of this rare condition and educating patients to recognize symptoms promptly will contribute to the early and effective management of this infrequent surgical l emergency.

Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model.

Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF). To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model. Thirty mice with carbon tetrachloride (CCl4)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000s/mm2) diffusion-weighted imaging (DWI) on a 3T scanner. DWI data were processed using RSI model (2-5 compartments) with the Bayesian Information Criterion (BIC) determining the optimal model. Conventional ADC value and signal fraction of each compartment in the optimal RSI model were compared across groups. Receiver operating characteristics (ROC) curve analysis was performed to determine the diagnosis performances of different parameters, while Spearman correlation analysis was employed to investigate the correlation between different tissue compartments and the stage of LF. According to BIC results, a 4-compartment RSI model (RSI4) with optimal ADCs of 0.471×10-3, 1.653×10-3, 9.487×10-3, and>30×10-3, was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C1 and C3 compartments were observed between LF and control groups (P=0.018 and 0.003, respectively). ROC analysis showed that RSI4-C3 was the most effective single diffusion parameter for characterizing LF (AUC=0.876, P=0.003). Furthermore, the combination of ADC values and RSI4-C3 value increased the diagnosis performance significantly (AUC=0.894, P=0.002). The 4-compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI4-C3 showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI4-C3 values further improved the discrimination performance.

Point based weakly semi-supervised biomarker detection with cross-scale and label assignment in retinal OCT images

Background and ObjectiveOptical coherence tomography (OCT) is currently one of the most advanced retinal imaging methods. Retinal biomarkers in OCT images are of clinical significance and can assist ophthalmologists in diagnosing lesions. Compared with fundus images, OCT can provide higher resolution segmentation. However, image annotation at the bounding box level needs to be performed by ophthalmologists carefully and is difficult to obtain. In addition, the large variation in shape of different retinal markers and the inconspicuous appearance of biomarkers make it difficult for existing deep learning-based methods to effectively detect them. To overcome the above challenges, we propose a novel network for the detection of retinal biomarkers in OCT images. MethodsWe first address the issue of labeling cost using a novel weakly semi-supervised object detection method with point annotations which can reduce bounding box-level annotation efforts. To extend the method to the detection of biomarkers in OCT images, we propose multiple consistent regularizations for point-to-box regression network to deal with the shortage of supervision, which aims to learn more accurate regression mappings. Furthermore, in the subsequent fully supervised detection, we propose a cross-scale feature enhancement module to alleviate the detection problems caused by the large-scale variation of biomarkers. We also propose a dynamic label assignment strategy to distinguish samples of different importance more flexibly, thereby reducing detection errors due to the indistinguishable appearance of the biomarkers. ResultsWhen using our detection network, our regressor also achieves an AP value of 20.83 s when utilizing a 5 % fully labeled dataset partition, surpassing the performance of other comparative methods at 5 % and 10 %. Even coming close to the 20.87 % result achieved by Point DETR under 20 % full labeling conditions. When using Group R-CNN as the point-to-box regressor, our detector achieves 27.21 % AP in the 50 % fully labeled dataset experiment. 7.42 % AP improvement is achieved compared to our detection network baseline Faster R-CNN. ConclusionsThe experimental findings not only demonstrate the effectiveness of our approach with minimal bounding box annotations but also highlight the enhanced biomarker detection performance of the proposed module. We have included a detailed algorithmic flow in the supplementary material.

Advanced Imaging Guidance for Ductus Venosus Stenting in Obstructed Total Anomalous Pulmonary Venous Return.

Obstructed infracardiac total anomalous pulmonary venous return is nearly always a surgical emergency in which infants present in severe cardiopulmonary distress. Ductal venosus stenting can provide a temporizing option for premature, low birth weight infants with high risk for surgical complications. In challenging anatomic cases, virtual reality, 3D-printed models, and fusion image guidance can aid in procedural planning and provide support for successful intervention.

Incidental Detection of Second Primary Thyroid Malignancy in Patients of Metastatic Castration-Resistant Prostate Carcinoma: Documentation on 68Ga-PSMA-11 and 64CuCl2 PET/CT.

Second primary tumors are being increasingly detected owing to and in proportion to the use of advanced imaging modalities including PET/CT. Patients suffering from prostate cancer have been reported to have increased second primary cancers of gastrointestinal tract, urinary bladder, and thyroid. We herein describe incidental detection of thyroid carcinoma, in 2 patients of mCRPC (metastatic castration-resistant prostate carcinoma) undergoing preradioligand therapy workup, on 68Ga-prostate-specific membrane antigen PET/CT initially, subsequently also observed on multitracer PET/CT (64CuCl2 and 18F-FDG). Thus, the potential of PET/CT for early in vivo second primary detection in mCRPC setting is illustrated in the aforementioned 2 patients.

A comprehensive review on role of magnetic resonance imaging in the precise measurement of corpus callosum

Magnetic Resonance Imaging (MRI) has emerged as a powerful and non-invasive imaging technique, playing a pivotal role in neuroimaging research and clinical diagnosis. This comprehensive review explores the significance of MRI in the precise measurement of the corpus callosum, a key structure connecting the two hemispheres of the brain. The corpus callosum plays a crucial role in inter hemispheric communication and is implicated in various neurological and psychiatric disorders. The review begins by elucidating the anatomy and functions of the corpus callosum, emphasizing its importance in facilitating seamless communication between the cerebral hemispheres. Subsequently, the paper delves into the historical evolution of MRI techniques and how advancements have enhanced the visualization and measurement capabilities of the corpus callosum. A detailed examination of various MRI modalities, including structural MRI, diffusion tensor imaging (DTI), and functional MRI, is presented in the context of corpus callosum analysis. Structural MRI provides high-resolution images for accurate morphological assessments, while DTI offers insights into white matter microstructure and fiber tractography, allowing for a more comprehensive understanding of the corpus callosum's integrity. The review also highlights the role of MRI in investigating alterations in the corpus callosum associated with neurological disorders such as multiple sclerosis, epilepsy, and neurodevelopmental disorders. Additionally, the paper explores how functional MRI contributes to understanding the functional connectivity and inter hemispheric interactions mediated by the corpus callosum. Challenges and limitations in the measurement of the corpus callosum using MRI are discussed, including methodological considerations, image artifacts, and potential sources of variability. The review concludes by outlining future directions in corpus callosum imaging research, including the integration of advanced imaging techniques, artificial intelligence, and large-scale multi-center studies to enhance the precision and reliability of measurements.In summary, this comprehensive review underscores the pivotal role of MRI in advancing our understanding of the corpus callosum, providing valuable insights into both normal brain function and pathological conditions. The continual evolution of MRI techniques holds promise for further refining the assessment of the corpus callosum, ultimately contributing to improved diagnostic accuracy and therapeutic interventions in neurological and psychiatric disorders.

Accurate Range Modeling for High-Resolution Spaceborne Synthetic Aperture Radar

Accurate Range Modeling for High-Resolution Spaceborne Synthetic Aperture Radar