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Archived Theses
Nükleer görüntülemede parsiyel hacim etkisi(PVE) düzeltmesi: özel fantom geliştiri̇lmesive kli̇ni̇k cihazlarda doğrulanmasi
Positron Emission Tomography (PET) imaging is a powerful and widely utilized modality in the field of Nuclear Medicine, particularly for the diagnosis and monitoring of cancer. This project aims to design and produce a unique anthropomorphic PET phantom that mimics human tissue properties; Utilize the developed phantom to calculate corrected Standard Uptake Values (SUVs) for oncological lesions by applying Recovery Coefficients (RCs) determined for Partial Volume Effect (PVE) correction. SUV is a widely utilized quantitative parameter in PET imaging, representing the concentration of radiotracer uptake within a region of interest (ROI) relative to its overall distribution in the body. However, the accuracy of SUV measurements can be compromised by the PVE, a phenomenon that arises when the spatial resolution of the imaging system is insufficient to clearly distinguish between adjacent tissues or structures within a single voxel, leading to underestimation or overestimation of radiotracer concentration. This dissertation is dedicated to exploring the critical area of PVE correction in F18-FDG PET imaging, focusing on the use of anthropomorphic phantoms as a fundamental platform for in-depth studies. By simulating anatomical conditions representative of the human body, anthropomorphic phantoms serve as important test phantoms for the evaluation of correction algorithms and provide insights into their efficacy and limitations. Through methodical experimentation and careful analysis, the research described here aims to make a significant contribution towards the refinement of PVE correction strategies, to increase the accuracy and reliability of F18- FDG PET imaging in the future.
Kombine kanser tedavisi için altın nanoçubukların ve kemoterapi ilaçlarının plga nanopartiküllerine ikili enkapsülasyonu
Due to its multifaceted nature, cancer often requires combination therapies for effective treatment. In this study, we report the development of a dual functional nanoplatform in which gold nanorods (AuNRs) and doxorubicin (DOX) are co encapsulated within poly(lactic coglycolic acid) (PLGA) nanoparticles (≈246 nm). The miniemulsion-based formulation produced nanoparticles with a narrow size distribution (PDI ≤ 0.1), a DOX loading of 32±4 µg/mL, and 47% AuNR encapsulation efficiency. AuNR encapsulated PLGA nanoparticles retained their structural integrity, shape, and longitudinal surface plasmon resonance (LSPR) properties during 110-day storage at 4 ◦C. Upon near-infrared laser irradiation (808 nm, 1W/cm2, 5 min), nanoparticles generated a temperature increase of approximately 25 ◦C, confirming preserved photothermal performance. 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT) assays in MCF-7 breast cancer cells revealed that cytotoxicity was primarily driven by the DOX payload, with no statistically significant synergy observed between chemotherapy and photothermal effects.
Dinlenme ile zihinsel stres sırasında kafeinin fMRG dinamiklerine katkısı
Caffeine is an adenosine receptor antagonist with known effects on arousal and cerebrovascular tone. Yet its rapid, systemic impact on fMRI signals and their cou pling to physiology remains understudied. We examined how caffeine modulates BOLD responses and systemic physiology across two intake delays (10 vs. 30 minutes; Caff 10, Caff-30), and two different scanning conditions, arithmetic equation solving and resting-state. GLM analysis revealed robust activation in task-positive networks. Es pecially, the IPS, one of the active regions, is crucial in number processing. Relative to Caff-10, Caff-30 showed modestly greater activation in IPS and insula, alongside pat terns near sulcal territories, suggesting a vascular contribution on task responses. Also, we conducted ICA, dual regression, and voxel-wise cross-correlations between BOLD RVT, and BOLD-PPG-Amp. Resting-state ICA prioritized a WM/ventricular pattern, whereas the analogous component emerged later during task, implying stronger vari ance attributable to systemic processes at rest. Dual regression did not yield signif icant results, which is reflecting limited power and network overlap. However, cross correlation analyses draw a consistent picture. Caffeine lengthened and broadened physiology-BOLD coupling. In rest, Caff-30 exhibited earlier negative and prolonged positive RVT-BOLD correlations. For PPG-Amp, spatial expression shifted by caffeine from more localized VIS effects (Caff-10) to widespread WM-CSF patterns (Caff-30). Together, these results indicate that on the timescale of minutes, caffeine reshapes both the temporal and spatial dimensions of systemic influences on BOLD, while modestly enhancing task-relevant activations. Recent caffeine intake can affect physiological vari ance and alter network engagement. Therefore, researchers must measure and control physiological signals when interpreting fMRI results.
Eşzamanlı fMRI, EEG ve fizyoloji ölçümlerinin entegrasyonu ile uyanıklık durumunun belirlenmesi ve doğrulanması
Resting-state fMRI (rsfMRI) is a powerful tool for investigating the brain's functional organization in the absence of specific tasks. Approaches such as functional connectivity (FC) and global signal variance are commonly used to study cognitive functions and disease states. These measures, which reflect correlations in blood oxy- genation level-dependent (BOLD) signals across brain regions, are particularly appeal- ing in clinical and research settings due to their ability to capture brain activity without requiring active participant engagement. However, a major challenge in rsfMRI lies in its sensitivity to fluctuations in vigilance-the state of wakefulness and attention. Vigilance-driven variations can alter BOLD signal amplitudes and correlations between brain regions. These effects, often influenced by systemic physiological signals, may be mistakenly interpreted as purely neural in origin if not properly accounted for. This study aimed to demonstrate how vigilance states modulate rsfMRI metrics and physiological signals in a healthy young population. Using an EEG-based vigilance index, we delineated the effects of vigilance on BOLD signals and their links to physi- ological dynamics. Systemic markers, including cardiac and respiratory signals such as heart rate and respiratory volume, as well as the photoplethysmogram (PPG) signal amplitude-an indicator of vascular tone-were analyzed to understand their contribution to these dynamics. Our work advances the field through several key innovations: developing clas- sification methodologies, integrating cerebrospinal fluid (CSF) flow oscillations from the fourth ventricle as a systemic signal of interest, utilizing physiological biomarkers often overlooked in rsfMRI studies, and leveraging time-lagged correlation matrices tovii uncover spatio-temporal brain dynamics. Data from 17 participants across multiple short eyes-open resting-state scans were analyzed. Our findings reveal significant correlations between fMRI signals and physiolog- ical markers during transitions from wakefulness to drowsiness. Notably, we observed co-variations between CSF oscillations and respiratory volume, correlations between sensory regions and heart rate, and associations between the default mode network (DMN) and photoplethysmography amplitude (PPG-AMP). Additionally, we demon- strated reduced connectivity during drowsy states compared to wakefulness, even in short-duration scans, underscoring the influence of vigilance transitions on rsfMRI out- comes. These findings highlight the interplay between systemic physiology and neural activity across vigilance states. By addressing these vigilance-driven variations, our approach enhances the interpretative accuracy of rsfMRI and broadens its potential applications in both research and clinical domains.
Çoklu-değişkenli deneysel kip ayrıştırımı-tabanlı işlevsel bağlantısallık öznitellikleriyle motor imgeleme sınıflandırması
Electroencephalogram (EEG) motor imagery signals are widely used for the implementation of brain-computer interfaces (BCI). Recently, functional connectivity measures have attracted attention as they can be used to capture statistical dependencies among EEG channels. However, functional connectivity during motor imagery tasks have not been fully explored. This study utilizes Instrinsic Mode Function (IMF) level phase locking value (PLV), coherence, and imaginary part of coherency in lefthand/right-hand motor imagery classification. EEG signals are decomposed into IMFs via noise-assisted multidimensional empirical mode decomposition (NA-MEMD), and connectivity metrics over the selected four channels are calculated for each trial as raw data and as a function of time and frequency. Resulting features are used to train multiple classifiers and their accuracy scores are analyzed. Best results are obtained using features derived from time-frequency functions of imaginary part of frequency where the overall accuracy score of 0.77 is achieved. This study shows that the change of connectivity throughout the duration of the task provides a more effective feature than connectivity calculated using each trial as raw data. Achieved accuracy scores are comparable to similar studies with the additional advantage of using few number of channels.
Sıçanlarda yürüme parametreleri ve makine öğrenmesi ile omurilik yaralanmasının sınıflandırılması
Spinal cord injury (SCI) represents a critical neurological condition with high morbidity, significantly impacting sensory motor functions. This thesis introduces a novel approach to classify the time after SCI and rats which recieved neuromodulation therapy by using machine learning (ML) based analysis of gait parameters and locomotor scores. Utilizing data derived from video imaging of rat locomotion, this study evaluates the accuracy and feasibility of the approach compared to previous literature. Key parameters analyzed include stance duration, swing duration, stride distance, limb duty factor, and paw area, alongside traditional metrics like Basso, Beattie and Bresnahan (BBB) locomotor rating scale and Von Frey (vF) withdrawal thresholds. The integration of markerless pose estimation tool, DeepLabCut (DLC), allowed for detailed extraction of gait parameters, overcoming challenges associated with lateral plane-focused methodologies. By using images from lateral and bottom views, footsteps were associated with animals' body movement and the gait pattern was extracted by deep-learning methods. Gait parameters, BBB scores and vF thresholds were input as features to Ensemble Learning including various ML methods to predict time after injury and neuromodulation treatment. In animals with SCI, gait parameters and BBB scores from both fore-hindlimbs, allowed prediction of post injury class (6 end-points) with \%43 accuracy. The same feature set predicted treatment with \%71 accuracy. Results showed that the methodology can effectively differentiate between treatment groups and time after SCI. However, additional work is needed to improve accuracy. Parameter extraction by deep-learning also provides an accessible and cost-effective solution for SCI research.
Preklinik uygulamalara yönelik çok parametreli nicel MRG haritalama ve işleme hatti
Quantitative MRI (qMRI) enables direct measurement of tissue properties -such as relaxation times and magnetic susceptibility- that shape the MRI signal in predictable ways. Unlike qualitative contrasts, qMRI provides reproducible biomarkers for studying processes from healthy aging to neurodegenerative and psychiatric disorders, with multiparametric approaches offering complementary insights as each parameter reflects distinct biological features. This thesis presents a multiparametric qMRI pipeline for preclinical use, demonstrated on 21 female Wistar rats at 7T. The pipeline integrates relaxometry mapping (T$_1$, T$_2$, R$_2^*$) within a Python-based framework featuring a GUI for subject-level analysis, and ensures reproducibility through automated conversion of raw data into BIDS-compliant format. Relaxometry maps were benchmarked against existing toolboxes with Bland-Altman analysis, confirming strong agreement. The pipeline was further extended to quantitative susceptibility mapping (QSM), where a dedicated phantom enabled evaluation of reconstruction algorithms; STAR-QSM outperformed alternatives by visual and nRMSE assessment and was adopted for preprocessing phase data and generating susceptibility maps with the Sepia toolbox. Regional mean susceptibility and relaxometry measures were extracted, with ROI-level mean, standard deviation, SEM, and voxel counts reported for transparency. Overall, this work establishes a reproducible preclinical qMRI framework unifying relaxometry and QSM, demonstrating methodological rigor and translational potential while providing a foundation for future studies of microstructural and physiological changes in disease and aging models.
Monte Carlo simulasyonu kullanarak I123 SPECT kantifikasyonunun dogrulanmasi
The accurate quantification of I-123 Single Photon Emission Computed Tomography (SPECT) is essential for the diagnosis and ongoing observation of neurological diseases. This work compares the performance of two gamma cameras (A and B) with the goal of validating the quantification accuracy of I-123 SPECT through Monte Carlo simulations with the Zubal brain phantom, using the SIMIND. The OSEM technique was utilized to rebuild the SPECT images, which allowed for measurements of the mean activity concentration and standard deviation in specific brain regions. The quantification accuracy was evaluated by calculating the recovery coefficient (RC) and computing the uptake in the regions of the cerebellum, temporal lobe, and parietal lobe. By comparing results with known activity concentrations and taking into account the impacts of scatter correction, PVE and collimator settings, the accuracy of the simulation was evaluated. The results show that quantification errors are acceptable for both cameras. Both cameras are suitable for I-123 imaging, but the choice may depend on the specific requirements of the imaging study. Further, the study explores into the effects of different imaging parameters on quantification accuracy, providing useful insights for enhancing SPECT protocols in the context of neurological imaging. Keywords: I-123 SPECT, Monte Carlo, Zubal phantom, SIMIND, OSEM, LEHR, Quantification Accuracy.
Eklem kıkırdağı tedavisi için 3b metilselüloz - Jelatin doku iskelelerinin üretimi ve karakterizasyonu
Articular cartilage is a highly specialized tissue that plays a crucial role in movement. However, its avascular nature severely limits its regenerative capacity, often resulting in degenerative diseases. Current treatment methods face challenges such as prolonged recovery times, inconsistent outcomes, and high costs. This study aims to develop scaffolds that mimic the properties of natural cartilage, as to be an alternative. For this purpose, Methylcellulose(MC)-Gelatinbased tissue scaffolds were fabricated with varying MC(10%, 12.5%, and 15%) and constant gelatin(20%) concentrations using a 3D printer. Characterization studies were performed to evaluate scaffold performance. Fourier Transform Infrared (FTIR) spectroscopy was employed to analyze the chemical interactions between MC and gelatin, particularly the effects of MC concentration. Contact Angle Measurement (CAM) results indicated that increasing MC concentration enhanced hydrophobicity due to the amphiphilic nature of MC. Mechanical tests revealed that increasing MC concentrations led to higher elasticity but reduced stiffness. Swelling and degradation tests revealed that higher MC concentrations reduced degradation rates and enhanced scaffold durability. In conclusion, the produced tissue scaffolds were able to mimic natural cartilage tissue closely, especially 10MC scaffolds stand out as a promising candidate to be a treatment alternative.
Omurilik yaralanması olan sıçanlarda periferik transkütanöz elektriksel sinir stimülasyonu ve yürüme analizi
Electrical stimulation strategies are investigated in the literature to recover sensorimotor impairments in spinal cord injury. In this study, transcutaneous electrical nerve stimulation (TENS) (pulse width: 0.3 ms, pulse frequency: 2 Hz, amplitude: 2 × motor threshold, duration: 30 min. at 3 days/week) was applied bilaterally to the tibial nerve rats with spinal cord injury (SCI). The study included sham (n=10), SCI (n=15), NS-sham (n=6), and NS-SCI (n=18) with endpoints at day 1 (D1), day 7 (D7), month 1 (M1), month 2 (M2). After T8-T9 laminectomy, contusion-type SCI was induced by using a computer-controlled custom-made impactor device (contactor diameter: 2.3 mm, peak force: 0.9-2 N, displacement: 1.75 mm, duration: 0.5 s). The severity (moderate-to-severe) of the injury and recovery was measured Basso, Beattie ve Bresnahan (BBB) locomotor rating scale. The mechanical withdrawal threshold was measured by von Frey filaments. DeepLabCut system was used to track each paw from high-speed (120 fps) video recordings and gait parameters (stance/swing/stride durations, limb duty factor, stride length, footprint area) were extracted in Matlab before detrending for speed. Average BBB scores were significantly decreased after the injury (D1: 0). The recovery was higher at NS-SCI (BBB: 17.5) than SCI (BBB: 8) (p = 0.003) at M2. vF scores also improved but were not statistically different between SCI and NS-SCI (p = 0.495). In gait parameters, the facilitative effect of TENS was observed up until D14; however, there was a reversal trend in limb duty factor and footprint area after D14. Overall, TENS had positive effects on locomotor function and stepping after SCI. However, synchronized application of TENS with stepping may have better results in terms of gait parameters in future studies.