Diffusion MRI Tractography

We derive the Iterative Confidence Enhancement of Tractography (ICE-T) framework to address the problem of path-length dependency (PLD), the streamline dispersivity confound inherent to probabilistic tractography methods. We show that PLD can arise as a non-linear effect, compounded by tissue complexity, and therefore cannot be handled using linear correction methods. ICE-T is an easy-to-implement framework that acts as a wrapper around most probabilistic streamline tractography methods, iteratively growing the tractography seed regions. Tract networks segmented with ICE-T can subsequently be delineated with a global threshold, even from a single-voxel seed. We investigated ICE-T performance using ex vivo pig-brain datasets where true positives were known via in vivo tracers, and applied the derived ICE-T parameters to a human in vivo dataset. We examined the parameter space of ICE-T: the number of streamlines emitted per voxel, and a threshold applied at each iteration. As few as 20 streamlines per seed-voxel, and a robust range of ICE-T thresholds, were shown to sufficiently segment the desired tract network. Outside this range, the tract network either approximated the complete white-matter compartment (too low threshold) or failed to propagate through complex regions (too high threshold). The parameters were shown to be generalizable across seed regions. With ICE-T, the degree of both near-seed flare due to false positives, and of distal false negatives, are decreased when compared with thresholded probabilistic tractography without ICE-T. Since ICE-T only addresses PLD, the degree of remaining false-positives and falsenegatives will consequently be mainly attributable to the particular tractography method employed. Given the benefits offered by ICE-T, we would suggest that future studies consider this or a similar approach when using tractography to provide tract segmentations for tract based analysis, or for brain network analysis.

Computed tomography (CT) stands as an indispensable diagnostic modality, offering unparalleled anatomical precision that informs critical medical and surgical decision-making. However, its widespread utilization necessitates a rigorous, evidence-based understanding of the attendant iatrogenic risks, which span acute adverse events to stochastic long-term sequelae. This scholarly review provides a comprehensive examination of the two principal domains of CT-related patient safety: contrast media reactions and ionizing radiation exposure. We deconstruct the pathophysiology and risk stratification of contrast-induced nephropathy (CIN) and hypersensitivity reactions, critically addressing pervasive clinical misconceptions such as the purported link between shellfish allergy and iodinated contrast media. The review further delineates the biological basis of radiation-induced carcinogenesis, contextualizing the limitations of extrapolating data from atomic bomb survivor studies to modern diagnostic CT, while underscoring the heightened vulnerability of pediatric and fetal populations. Emphasizing a systems-based approach, this article articulates the imperative for interprofessional collaboration-among radiologists, referring clinicians, nursing staff, and allied health professionals-in implementing evidence-based protocols for preprocedural risk assessment, hydration, premedication, and post-procedural monitoring. The central thesis is that the safe and effective deployment of CT technology hinges not merely on technical proficiency but on a cultivated culture of shared accountability, wherein diagnostic efficacy is systematically balanced against a sophisticated understanding of individualized patient risk.

Extensive resection for malignant intraaxial intracranial tumors has been demonstrated to improve survival. This is not always possible due to potential tumor location in or next to eloquent brain regions, like primary motor cortex or speech areas. In this case, avoiding neurological defi cits is challenging. One of the tools for minimizing that risk is intraoperative cortical mapping (ICM). This report describes the ICM technique of eloquent brain regions, feasibility and complementariness with other methods for tumor localization. Methods: Seven patients with brain lesions near eloquent regions operated on were analyzed. Frameless stereotaxis (neuronavigation) and intraoperative cortical mapping (direct cortical stimulation and monitoring of somatosensory evoked potentials) were used. The location, size, type of lesion, amount of resection, pre and postoperative neurological status were studied. Results: ICM was effective in localizing primary motor cortex in all patients. In 6 patients the tumor area without functionality was removed, achieving complete or greater than 90% resection in 5 of them. In one patient the lesion was a deep arteriovenous malformation located below the primary motor cortex. In this case ICM made feasible an approach from non-eloquent cortex to achieve total resection. No new postoperative defi cit was found in a 12 month follow-up period. Conclusion: ICM is useful and localize functional cortical regions effectively, simply and reliably, making possible to perform extensive tumor resections in eloquent regions. This technique is complementary to other tools for anatomical or physiological localization and could contribute to a safer and more effective surgery.

Extensive resection for malignant intraaxial intracranial tumors has been demonstrated to improve survival. This is not always possible due to potential tumor location in or next to eloquent brain regions, like primary motor cortex or speech areas. In this case, avoiding neurological defi cits is challenging. One of the tools for minimizing that risk is intraoperative cortical mapping (ICM). This report describes the ICM technique of eloquent brain regions, feasibility and complementariness with other methods for tumor localization. Methods: Seven patients with brain lesions near eloquent regions operated on were analyzed. Frameless stereotaxis (neuronavigation) and intraoperative cortical mapping (direct cortical stimulation and monitoring of somatosensory evoked potentials) were used. The location, size, type of lesion, amount of resection, pre and postoperative neurological status were studied. Results: ICM was effective in localizing primary motor cortex in all patients. In 6 patients the tumor area without functionality was removed, achieving complete or greater than 90% resection in 5 of them. In one patient the lesion was a deep arteriovenous malformation located below the primary motor cortex. In this case ICM made feasible an approach from non-eloquent cortex to achieve total resection. No new postoperative defi cit was found in a 12 month follow-up period. Conclusion: ICM is useful and localize functional cortical regions effectively, simply and reliably, making possible to perform extensive tumor resections in eloquent regions. This technique is complementary to other tools for anatomical or physiological localization and could contribute to a safer and more effective surgery.

Extensive resection for malignant intraaxial intracranial tumors has been demonstrated to improve survival. This is not always possible due to potential tumor location in or next to eloquent brain regions, like primary motor cortex or speech areas. In this case, avoiding neurological defi cits is challenging. One of the tools for minimizing that risk is intraoperative cortical mapping (ICM). This report describes the ICM technique of eloquent brain regions, feasibility and complementariness with other methods for tumor localization. Methods: Seven patients with brain lesions near eloquent regions operated on were analyzed. Frameless stereotaxis (neuronavigation) and intraoperative cortical mapping (direct cortical stimulation and monitoring of somatosensory evoked potentials) were used. The location, size, type of lesion, amount of resection, pre and postoperative neurological status were studied. Results: ICM was effective in localizing primary motor cortex in all patients. In 6 patients the tumor area without functionality was removed, achieving complete or greater than 90% resection in 5 of them. In one patient the lesion was a deep arteriovenous malformation located below the primary motor cortex. In this case ICM made feasible an approach from non-eloquent cortex to achieve total resection. No new postoperative defi cit was found in a 12 month follow-up period. Conclusion: ICM is useful and localize functional cortical regions effectively, simply and reliably, making possible to perform extensive tumor resections in eloquent regions. This technique is complementary to other tools for anatomical or physiological localization and could contribute to a safer and more effective surgery.

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Background: The late myelinating superficial white matter at the juncture of the cortical gray and white matter comprising the intracortical myelin and short-range association fibers has not received attention in Huntington's disease. It is an area of the brain that is late myelinating and is sensitive to both normal aging and neurodegenerative disease effects. Therefore, it may be sensitive to Huntington's disease processes. Methods: Structural MRI data from 25 Pre-symptomatic subjects, 24 Huntington's disease patients and 49 healthy controls was run through a cortical pattern-matching program. The surface corresponding to the white matter directly below the cortical gray matter was then extracted. Individual subject's Diffusion Tensor Imaging (DTI) data was aligned to their structural MRI data. Diffusivity values along the white matter surface were then sampled at each vertex point. DTI measures with high spatial resolution across the superficial white matter surfa...

Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disease involving the cerebellum. The particular atrophy pattern results in some typical clinical features mainly including motor deficits. In addition, the presence of cognitive impairments, involving language, visuospatial and executive functions, has been also shown in SCA2 patients and it is now widely accepted as a feature of the disease. The aim of the study is to investigate the microstructural patterns and the anatomo-functional substrate that could account for the cognitive symptomatology observed in SCA2 patients. In the present study, diffusion tensor imaging (DTI) based-tractography was performed to map the main cerebellar white matter (WM) bundles, such as Middle and Superior Cerebellar Peduncles, connecting cerebellum with higher order cerebral regions. Damage-related diffusivity measures were used to determine the pattern of pathological changes of cerebellar WM microstructure in patients ...

The rapid expansion of off plan property marketing in the United Arab Emirates has exposed critical limitations
in human driven decision making, particularly in the areas of buyer qualification, demand forecasting, and lead
conversion prediction. Traditional marketing judgments shaped by cognitive bias, nationality stereotyping,
heuristic shortcuts, and fragmented cross channel data interpretation are increasingly inadequate in environments
where buyers generate complex digital footprints across portals, social media, call centres, CRM systems, and
WhatsApp interactions. This study introduces a multimodal deep learning architecture designed to surpass these
human biases by integrating heterogeneous data modalities into a unified predictive marketing framework. The
proposed system employs transformer-based text encoders, convolutional neural representations for visual
property engagement patterns, LSTM sequence modelling for lead journey timelines, and cross channel
behavioural embeddings constructed through contrastive representation learning. A dataset of 280,000 historical
off plan leads from major UAE developers was used to train and evaluate the model. Structural equation modelling
was applied in parallel to compare AI derived intent predictions with human marketing judgments provided by
sales agents and marketing managers. Results indicate that the multimodal deep learning model achieves a
significant improvement in predictive accuracy, yielding a 32 percent increase in true positive buyer intent
detection and reducing false positives by 41 percent compared with human evaluations. Furthermore, the model
demonstrates strong robustness under market volatility scenarios, such as payment plan changes, launch date
shifts, and macroeconomic fluctuations. This research provides empirical evidence that multimodal AI
frameworks can fundamentally restructure performance marketing strategies in UAE off plan real estate by
eliminating cognitive bias, optimizing audience selection, and enabling demand forecasting with significantly
higher precision than human cantered decision processes. The findings establish a new pathway for AI enhanced
marketing science within fast moving real estate sectors and contribute a scalable architecture for future predictive
applications.

Huntington's disease (HD) is characterized by progressive motor impairment. Therefore, the connectivity of the corticospinal tract (CST), which is the main white matter (WM) pathway that conducts motor impulses from the primary motor cortex to the spinal cord, merits particular attention. WM abnormalities have already been shown in presymptomatic (Pre-HD) and symptomatic HD subjects using magnetic resonance imaging (MRI). In the present study, we examined CST microstructure using diffusion tensor imaging (DTI)based tractography in 30-direction DTI data collected from 100 subjects: Pre-HD subjects (n = 25), HD patients (n = 25) and control subjects (n = 50), and T 2 *-weighted (iron sensitive) imaging. Results show decreased fractional anisotropy (FA) and increased axial (AD), and radial diffusivity (RD) in the bilateral CST of HD patients. Pre-HD subjects had elevated iron in the left CST, regionally localized between the brainstem and thalamus. CAG repeat length in conjunction with age, as well as motor (UHDRS) assessment were correlated with CST FA, AD, and RD both in Pre-HD and HD. In the presymptomatic phase, increased iron in the inferior portion supports the "dying back" hypothesis that axonal damage advances in a retrograde fashion. Furthermore, early iron alteration may cause a high level of toxicity, which may contribute to further damage.

Diffusion weighted (DW) MRI facilitates non-invasive quantification of tissue microstructure and, in combination with appropriate signal processing, three-dimensional estimates of fibrous orientation. In recent years, attention has shifted from the diffusion tensor model, which assumes a unimodal Gaussian diffusion displacement profile to recover fibre orientation (with various well-documented limitations), towards more complex high angular resolution diffusion imaging (HARDI) analysis techniques. Spherical deconvolution (SD) approaches assume that the fibre orientation density function (fODF) within a voxel can be obtained by deconvolving a 'common' single fibre response function from the observed set of DW signals. In practice, this common response function is not known a priori and thus an estimated fibre response must be used. Here the establishment of this single-fibre response function is referred to as 'calibration'. This work examines the vulnerability of two different SD approaches to inappropriate response function calibration: (1) constrained spherical harmonic deconvolution (CSHD)-a technique that exploits spherical harmonic basis sets and (2) damped Richardson-Lucy (dRL) deconvolution-a technique based on the standard Richardson-Lucy deconvolution. Through simulations, the impact of a discrepancy between the calibrated diffusion profiles and the observed ('Target') DW-signals in both single and crossing-fibre configurations was investigated. The results show that CSHD produces spurious fODF peaks (consistent with well known ringing artefacts) as the discrepancy between calibration and target response increases, while dRL demonstrates a lower over-all sensitivity to miscalibration (with a calibration response function for a highly anisotropic fibre being optimal). However, dRL demonstrates a reduced ability to resolve low anisotropy crossing-fibres compared to CSHD. It is concluded that the range and spatial-distribution of expected single-fibre anisotropies within an image must be carefully considered to ensure selection of the appropriate algorithm, parameters and calibration. Failure to choose the calibration response function carefully may severely impact the quality of any resultant tractography.

"AI-Based Diabetes Prediction System," a research work that describes the creation of a novel web application for
diabetes risk assessment and early identification. In order to enable prompt intervention, the system looks at a wide
range of patient health data in order to identify those who may already have diabetes or are at risk of getting it. A
number of machine learning algorithms were applied to bulk amount of data. The various algorithms are Logistic
regression, Decision Tree, Random Forest, SVM and ANN are adopted in the study to build an efficient model. The
Artificial Neural Network (ANN) classifier with accuracy 85%, precision 84%, recalls 86%, f1-score 85% and AUC
0.89. The model has ability in predicting the patient is diabetic or non-diabetic. A web application that allows user
to enter physiological information in real time and dynamically view prediction results was created to improve
accessibility and interpretability. The system integrates SHAP-based explainability and feature importance
visualizations, providing transparency in model decisions and enabling healthcare professionals to better understand
contributing factors.

Wine quality is traditionally judged by human tasters, which makes the process subjective and inconsistent. This research presents 'Wine IQ: An Exceptional Beverage Selection,' a machine learning-based system that classifies wines into three categories-excellent, good, and poor-using physicochemical properties. The project leverages a Random Forest classifier to achieve accurate quality prediction and introduces a rulebased suggestion engine that provides actionable recommendations for improving lower-quality wines. This dual functionality not only enhances quality prediction but also supports producers in maintaining consistent standards. The system has been validated using accuracy, precision, recall, confusion matrices, and ROC curves, demonstrating its effectiveness in bridging data science and winemaking practices.

Deep understanding of myocardial structure of the heart would unravel crucial knowledge for clinical and medical procedures. Although the muscular architecture of the heart has been debated by countless researchers, the controversy is still alive. Diffusion Tensor MRI, DT-MRI, is a unique imaging technique for computational validation of the muscular structure of the heart. By the complex arrangement of myocites, existing techniques can not provide comprehensive descriptions of the global muscular architecture. In this paper we introduce a multiresolution reconstruction technique based on DT-MRI streamlining for simplified global myocardial model generation. Our reconstructions can restore the most complex myocardial structures and indicate a global helical organization.

Alzheimer’s Disease is a progressive neurological disorder that severely impacts memory, thinking, and
behavior, especially in the elderly population. Early diagnosis is essential to slow its progression and
improve the patient’s quality of life. In this study, a deep learning-based approach using the MobileNetV2
architecture is proposed to classify brain MRI images into different stages of Alzheimer’s Disease. The
model is trained on a preprocessed and augmented dataset to improve generalization and robustness. After
training for 40 epochs, the model achieved a classification accuracy of 93.1%, indicating strong potential for
clinical applications. A web-based interface is also developed using Flask, allowing users to upload MRI
scans and receive real-time predictions. This research highlights the effectiveness of lightweight
convolutional neural networks in medical image analysis, especially in settings with limited computational

Recent studies have highlighted the potential role of basic numerical processing in the acquisition of numerical and mathematical competences. However, it is debated whether high-level numerical skills and mathematics depends specifically on basic numerical representations. In this study mathematicians and nonmathematicians performed a basic number line task, which required mapping positive and negative numbers on a physical horizontal line, and has been shown to correlate with more advanced numerical abilities and mathematical achievement. We found that mathematicians were more accurate compared with nonmathematicians when mapping positive, but not negative numbers, which are considered numerical primitives and cultural artifacts, respectively. Moreover, performance on positive number mapping could predict whether one is a mathematician or not, and was mediated by more advanced mathematical skills. This finding might suggest a link between basic and advanced mathematical skills. However, when we included visuospatial skills, as measured by block design subtest, the mediation analysis revealed that the relation between the performance in the number line task and the group membership was explained by non-numerical visuospatial skills. These results demonstrate that relation between basic, even specific, numerical skills and advanced mathematical achievement can be artifactual and explained by visuospatial processing.

Magnetic Resonance Imaging (MRI) is an advanced, non-invasive diagnostic modality that revolutionized medical imaging by providing unparalleled soft tissue contrast without the risks of ionizing radiation. This paper comprehensively explores the foundational principles, equipment, personnel qualifications, procedural techniques, potential complications, and clinical significance of MRI, particularly in the context of gadolinium-based contrast agents. MRI utilizes a powerful static magnetic field, gradient fields, and radiofrequency pulses to manipulate the nuclear spin of hydrogen protons, generating detailed cross-sectional images. The equipment consists of a high-field magnet, gradient coils, radiofrequency coils, and sophisticated computer systems that reconstruct signals into diagnostic images. Personnel working in MRI environments are stratified into levels based on safety training, with the MR technologist playing a pivotal role in patient care and image acquisition. Preparation involves meticulous screening for metallic implants, insertion of intravenous lines for contrast administration, and patient education to mitigate anxiety. The scanning technique emphasizes patient immobility, controlled use of contrast agents, and adherence to safety protocols. Although MRI is generally safe, potential complications include claustrophobia, nephrogenic systemic fibrosis in at-risk populations, and rare hypersensitivity reactions. Clinically, MRI is indispensable for diagnosing neurological, musculoskeletal, cardiovascular, and oncologic diseases, offering unique insights into both anatomy and pathology. This paper also underscores the importance of interdisciplinary collaboration in optimizing MRI outcomes and enhancing patient care.

Object In this study simple and computationally efficient random-walk probabilistic algorithms are presented for white matter fibre tracking using diffusion MR images. We examine how fibre tracts can be modelled globally by means of randomwalk algorithms which capture the uncertainties in local fibre orientations due to noise and partial volume effects. Materials and Methods Using Monte Carlo methods, the algorithms generate multiple pathways from a single seed location by randomly perturbing the direction of propagation according to the algorithm model at each step. We analyze the random-walk models quantitatively using a synthetic data set and estimate the optimal parameter values of the models. These algorithms are then applied to a multi-tensor model to compute the probabilities of connections between regions with complex fibre configurations. Voxels are classified based on tensor morphologies and a two-tensor model is estimated for planar voxels and a single-tensor model for the remaining voxels. The proposed algorithms are validated and compared to alternative methods on synthetic data, a physical phantom and real-world brain MRI datasets. The results confirm the effectiveness of the proposed approach, which gives comparable results to other probabilistic methods. Our approach is however significantly faster and requires less memory. The results of multi-tensor randomwalk algorithms demonstrate that our algorithms can accurately identify fibre bundles in complex fibre regions.

The direction of axons in white matter can be estimated using a deterministic fibre tracking algorithms and diffusion weighted imaging. The aim of this work was to evaluate the data, obtained from pig spines phantom measurements with relatively low b-value, using two types of reconstructions: diffusion tensor imaging (DTI) and q-ball approach. Pigs spines submerged in agar gel were used to prepare a phantom with two crossing populations of fibres. The phantoms were measured in 3T MR scanned for b-value of 1000 and 2000 s/mm2 for q-ball and 200-2000s/mm2 for DTI reconstruction. Analysis of crossing and single fibre population regions in the scanners showed that the median dispersions from the reference directions in case of single fibre population were c.a. 4° and for crossing area c.a. 12° and 6.5° for b-value of 1000 s/mm2 and 2000 s/mm2 respectively. The q-ball approach was able to resolve crossing problem for both low b-values. It was shown here that coherent results can be achie...

© 2024 by Ali Mohammadioun Otikandi, Hamed Aghazadeh, Parvin Karimi, Sobhan Aboulhassanzadeh, Reyhaneh Nasr Azadani, Tahereh Sangchooli, Samin Aboulhassanzadeh, Parastoo Ghadermarzi, Pourya Mahdavi Abdar.
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The diagnosis and treatment of liver cancer are complex processes that require precision and accuracy. A multidisciplinary approach involving oncologists, surgeons, and radiologists is often necessary to develop a comprehensive treatment plan tailored to each patient's specific needs. Additionally, ongoing monitoring and follow-up care are essential to track the progress of treatment and address any potential complications that may arise. Regular imaging tests, blood work, and physical exams are typically conducted to ensure the effectiveness of the treatment plan and make any necessary adjustments. It is important for patients to communicate openly with their healthcare team and adhere to all recommended follow-up appointments to optimize their chances of successful outcomes. In recent years, artificial intelligence (AI) has emerged as a valuable tool in the medical field, particularly in the field of oncology. AI has shown great promise in improving early detection of cancer, personalizing treatment plans, and predicting patient outcomes. Its ability to analyze vast amounts of data quickly and accurately has the potential to revolutionize how cancer is diagnosed and treated. AI has also been used to develop innovative methods for drug discovery and development, leading to more effective and targeted therapies for cancer patients. Additionally, AI can assist in identifying patterns and trends in patient data that may have previously gone unnoticed by human healthcare providers.

Liver cancer remains one of the most lethal malignancies globally due to late diagnosis and limited treatment
options. Magnetic Resonance Imaging (MRI) has emerged as a powerful non-invasive diagnostic tool, offering
superior soft tissue contrast and functional imaging capabilities for liver tumor detection and characterization.
This study proposes a unique approach that leverages MRI scans integrated with artificial intelligence (AI) based
algorithms to enhance the early diagnosis and accurate classification of liver cancer. The methodology involves
pre-processing MRI images to reduce noise, followed by segmentation using advanced deep learning models like
U-Net and ResNet. These models are trained on annotated datasets to identify and differentiate benign from
malignant lesions with high precision. Furthermore, radiomic features extracted from segmented images are fed
into machine learning classifiers such as Random Forest and Support Vector Machine (SVM) to improve
diagnostic accuracy. The proposed system demonstrates exceptional performance in identifying liver tumors,
outperforming traditional diagnostic methods in sensitivity, specificity, and overall accuracy. By combining
imaging data with AI, the framework not only supports radiologists in clinical decision-making but also reduces
inter-observer variability. This novel integration aims to revolutionize liver cancer detection, enabling early
intervention and improved patient outcomes. Future directions include expanding the dataset diversity,
incorporating multimodal imaging, and real-time deployment in clinical settings. The findings of this research
highlight the transformative potential of AI-enhanced MRI analysis in liver oncology, pushing the boundaries of
personalized medicine and precision diagnostics.

Real-time image processing plays a crucial role in various domains, including medical imaging, surveillance, and autonomous systems, where the demand for efficient hardware acceleration is paramount. Field-Programmable Gate Arrays (FPGAs) emerge as a viable solution owing to their ability to perform parallel processing and their low-latency characteristics. This study introduces an FPGA-based implementation of both linear and non-linear image filters that are specifically optimized for real-time applications. The methodology employs a coefficient file (.coe) generation technique to facilitate efficient sum-ofproduct calculations and swift pixel ordering within a 3×3 window, all executed within a single clock cycle. The design, implemented on a Basys-3 FPGA using Verilog HDL and synthesized in Xilinx Vivado, achieves a processing latency of 0.04 ms at a clock frequency of 464 MHz while deliberately avoiding the utilization of FPGA DSP blocks. The paper provides a comprehensive account of the methodology to ensure reproducibility, detailing preprocessing steps, data management, and the experimental framework. The results indicate that the proposed architecture enhances computational efficiency without compromising image quality, thereby making it highly suitable for real-time applications in FPGA-based image processing.

Subcortical structures, such as the hippocampus, amygdala, and nucleus accumbens (NAcc), play crucial roles in human cognitive, memory, and emotional processing, chronic pain pathophysiology, and are implicated in various psychiatric and neurological diseases. Interventions modulating the activities of these deep brain structures hold promise for improving clinical outcomes. Recently, non-invasive brain stimulation (NIBS) has been applied to modulate brain activity and has demonstrated its potential for treating psychiatric and neurological disorders. However, modulating the above deep brain structures using NIBS may be challenging due to the nature of these stimulations. This study attempts to identify brain surface regions as source targets for NIBS to reach these deep brain structures by integrating functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). We used resting-state functional connectivity (rsFC) and probabilistic tractography (PTG) analysis to ...

This paper describes implementation of image processing algorithms on FPGA. Currently, implementation of image processing algorithms using software approach is slower due to limited speed of the processor. So a dedicated processor for implementation of image processing algorithms is required which is possible with VLSI technology. FPGA’s have the advantages of speed and re-configurability which is required for image processing applications. This paper deals with image enhancement algorithm. This paper presents three techniques of image enhancement algorithms of Negative transformation, Thresholding & Contrast stretching. The aim of this paper is to simulate and implement these algorithms using Matlab and VHDL. The device selected here for implementation is Sparten 3E500K from Xilinx.

Background: Deep brain stimulation of the anterior nucleus of the thalamus (ANT) is emerging EU/US-approved
form of therapy for drug-resistant focal epilepsy. Its mechanism of action is not yet fully understood, and the
patient outcomes in epilepsy appear less consistent compared to for instance movement disorders. Furthermore,
very little anatomy-based information, such as tractography of relevant fiber systems, exists guiding DBS therapy
at present.
Objective: To demonstrate ANT-related fiber systems based on histology in vivo employing multi-shell/multi-
tissue constrained spherical deconvolution (MSMT-CSD) based deterministic and probabilistic tractography
using a sophisticated scanning protocol of 3T high angular resolution diffusion-weighted imaging (HARDI) data
in healthy volunteers.
Method: HARDI data was acquired from five healthy volunteers in a 3T Siemens MAGNETOM Skyra Magnetic
resonance imaging (MRI) machine using multiple b-values (1000, 2000, and 3000), 64 directions, and further
preprocessed for tractography. MSMT-CSD-based deterministic and probabilistic tractography was performed
from selected fiber systems based on existing literature.
Results: Multiple fiber systems were identified: The anterior thalamic radiation (ANT), the thalamo-cingulate
tract, the inferior thalamic peduncle (with remote termination areas in the amygdala, the ventral tegmental
area, and the occipital cortex), and the mammillothalamic tract. In addition, we observed three parallel connections
to the hippocampus (via the cingulum bundle, the fornix, and the temporo-pulvinar pathway). Interestingly,
different seed areas in ANT complex mimicking DBS contact locations resulted in visualization of
different fiber systems.
Discussion: The connections of the ANT are complex and different stimulation sites are likely to affect different
networks depending on lead locations and the selection of the active contact.
Conclusions: In-depth understanding of the network of anatomical structures related to the ANT is likely to influence
therapy outcomes. A hypothetical model of neuronal networks affected by different DBS lead contact
locations is proposed. A more profound understanding of neuroanatomic characteristics may guide stereotactic
implantation and subsequent programming to optimize outcomes.

-Ao Professor Dr. Jaderson Costa da Costa, que me acolheu como aluno e permitiu meu crescimento pessoal e científico. Com ele aprendi a importância de uma "pergunta" correta e o valor de um "brainstorming". É contagioso o seu entusiasmo e gosto pelo saber. É um orientador dedicado, um neurocientista exemplar, uma pessoa admirável. Nele, pude testemunhar que o brilhantismo não se opõe à simplicidade. -Aos neurorradiologistas, físicos, residentes e chefia do Serviço de Diagnóstico por Imagem do Hospital Moinhos de Vento, sem os quais este trabalho não teria se concretizado. No período em que frequentei esse Serviço, pude encontrar mais do que colaboradores na pesquisa, encontrei amigos.

Background: The human reward system has been extensively studied using neuroimaging. This bibliometric analysis aimed to determine the global trend in diffusion magnetic resonance imaging (dMRI) and human reward research in terms of the number of documents, the most active countries and their collaborating countries, the top journals and institutions, the most prominent authors and most cited articles, and research hotspots. Methods: The research datasets were acquired from the Scopus database. The search terms used were 'reward' AND 'human' AND 'diffusion imaging' OR 'diffusion tensor imaging' OR 'diffusion MRI' OR 'diffusion-weighted imaging' OR 'tractography' in the abstract, article title and keywords. A total of 336 publications were analysed using Harzing's Publish or Perish and VOSviewer software. Results: The results revealed an upward trend in the number of publications with the highest number of articles in 2020 and 2022. Most publications were limited to countries, authors, and institutions in the USA, China and Europe. Bracht, Coenen, Wiest, Federspiel and Feng were among the top authors from Switzerland, Germany and the UK. Neuroimage, Neuroimage Clinical, Frontiers in Human Neuroscience, Human Brain Mapping, and the Journal of Neuroscience were the top journals. Among the top articles, six were reviews and four were original articles, while

The next paragraphs are meant as a quick summary of some definitions and results in linear algebra necessary to remove the exponential of a matrix from Lagrange's formula and express the solution of a linear system of ordinary differential equations in a more meaningful and manageable form. The topics we need to recap are three: diagonalization of a matrix, the Jordan canonical form, and the exponential of a square matrix.

In vivo tractography based on diffusion magnetic resonance imaging (dMRI) has opened new doors to study structure-function relationships in the human brain. Initially developed to map the trajectory of major white matter tracts, dMRI is used increasingly to infer long-range anatomical connections of the cortex. Because axonal projections originate and terminate in the gray matter but travel mainly through the deep white matter, the success of tractography hinges on the capacity to follow fibers across this transition. Here we demonstrate that the complex arrangement of white matter fibers residing just under the cortical sheet poses severe challenges for long-range tractography over roughly half of the brain. We investigate this issue by comparing dMRI from very-high-resolution ex vivo macaque brain specimens with histological analysis of the same tissue. Using probabilistic tracking from pure gray and white matter seeds, we found that ∼50% of the cortical surface was effectively in...

Background:
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, often linked to chronic liver disease or cirrhosis. Early detection is vital for improving outcomes, yet diagnosing HCC, particularly in its early stages, remains difficult. The Liver Imaging Reporting and Data System (LI-RADS) classifies liver lesions based on imaging, with LI-RADS 3 (LR-3) indicating intermediate probability and LI-RADS 4 (LR-4) indicating probable HCC. Combining serum biomarkers and liquid biopsy markers with imaging may enhance diagnostic accuracy.
Objective:
This study evaluates the combined use of serum biomarkers, liquid biopsy (circulating tumor cells [CTCs] and circulating tumor DNA [ctDNA]), and imaging for assessing LI-RADS 3 and 4 lesions, with a focus on detecting early-stage HCC.
Methods:
A cohort of 70 patients with LI-RADS 3 (n=42) and LI-RADS 4 (n=28) lesions confirmed via MRI or contrast-enhanced CT was analyzed. Serum biomarkers such as alpha-fetoprotein (AFP), AFP-L3, gamma-glutamyl transferase (GGT), hepatocyte growth factor (HGF), and interleukin-6 (IL-6) were measured. CTCs and ctDNA were assessed via immunocytochemistry and next-generation sequencing. Biomarker and liquid biopsy data were correlated with imaging findings to assess malignancy risk.
Results:
LI-RADS 4 patients had higher elevations in AFP (211 ng/mL), AFP-L3 (48%), HGF (90%), and IL-6 (47%) compared to LI-RADS 3 patients. AFP was elevated in 47.61% of LI-RADS 3 cases (119 ng/mL), with ctDNA detected in 16.6%. CTCs were found in 17.85% of LI-RADS 4 patients, indicating a higher malignancy risk. Liquid biopsy provided additional insights when imaging was inconclusive.
Conclusion:
Combining serum biomarkers, liquid biopsy, and imaging enhances diagnostic accuracy in LI-RADS 3 and 4 lesions, particularly for early-stage HCC detection and monitoring. Further research is needed to validate these findings in routine clinical practice.
Keywords: LI-RADS, hepatocellular carcinoma, serum biomarkers, liquid biopsy, circulating tumor cells, circulating tumor DNA, early detection, liver cancer