Objective This study evaluates pre-surgery white matter abnormalities in evaluating the association between pre-surgical white matter integrity and post-surgery outcomes at the group level using Diffusion Tensor Imaging (DTI) in patients with Mesial Temporal Lobe Epilepsy (MTLE), specifically examining the differences between seizure-free and non-seizure-free patients, by Regions of Interest (ROIs) and diffusion parameters (biomarkers). Methods A retrospective analysis was conducted on pre-surgical DTI data from MTLE patients who underwent amygdalohippocampectomy with the subject outcome evaluation. Key parameters, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusion (AD), and radial diffusion (RD), were extracted from regions of interest (ROIs) such as the hippocampus, thalamus, and temporal pole. Statistical analysis included ANOVA with Bonferroni post-hoc tests to compare surgical responders and non-responders on the ipsilateral and contralateral sides of the epileptogenic zone, as per clinical convention. Results FA values in the contralateral hippocampus and thalamus were significantly lower in the Not-SF group compared to the SF group ( p < 0.05). RD values in the contralateral hippocampus and temporal pole were significantly higher in the Not-SF group ( p < 0.05). In ipsilateral regions, MD and RD values in the hippocampus and temporal pole were significantly higher in the Not-SF group ( p < 0.01), while FA values were significantly lower ( p < 0.001). AD did not show significant differences. Overall, increased RD and MD and reduced FA in the Not-SF group indicate reduced microstructural integrity associated with poor seizure control. Discussion This study demonstrates that diffusion-based structural connectivity analysis can distinguish seizure-free from non–seizure-free TLE patients, highlighting the clinical relevance of hippocampus–temporal pole and hippocampus–thalamus pathways in postoperative outcomes. By extending diffusion evaluation beyond traditional tracts and integrating modern segmentation and tractography tools, our findings support the role of TLE as a network disorder and emphasize the potential of advanced DTI methods for understanding the network-level changes associated with surgical outcomes. Conclusion This study shows that diffusion-based structural connectivity metrics, particularly within hippocampus–temporal pole and hippocampus–thalamus pathways, showed exploratory associations with surgical outcomes in MTLE patients. While these metrics suggest differences between outcome groups, these findings are hypothesis-generating.
Introduction Gaussian noise is often added during the acquisition or transmission of medical images, which can blur important organs and reduce diagnostic accuracy. To address this issue, a hybrid denoising model that combines BayesShrink thresholding in the wavelet domain with Patch-Wise Local Principal Component Analysis (PLPCA) is proposed. Methods The framework initially utilises the concept of local patch redundancy by applying PLCA to effectively suppress noise while preserving finer details. The remaining noise is again decomposed using the wavelet transform, with adaptive BayesShrink thresholding applied to refine the coefficients. Subsequently, the reconstructed signal is obtained. The resultant denoised images are then combined to obtain the final enhanced image. HRCTs and MRI datasets were corrupted by Gaussian noise at σ = 10-40 and were validated. Results Quantitative analysis based on PSNR, entropy, BRISQUE, NIQE, and PIQE confirmed the consistent high impact of the proposed method across traditional, hybrid, and deep learning baselines. It is worth noting that the approach achieved PSNR values of 31.15 dB at HRCT and 33.00 dB at MRI when σ = 10, as well as at high noise levels (23.95 dB and 22.64 dB, respectively). Discussion The proposed method consistently excels over traditional, hybrid, and deep learning-based strategies, as shown by quantitative assessments using PSNR, entropy, BRISQUE, NIQE, and PIQE. Conclusion The suggested framework has the strength of suppressing Gaussian noise while retaining anatomical detail. Thus, it is an encouraging option for enhancing the quality of medical image diagnosis in clinical practice.
Introduction Magnetic Resonance Imaging (MRI) and High-Resolution Computed Tomography (HRCT) are crucial for comprehensive diagnosis and treatment planning, as they provide detailed anatomical information. However, noise introduced during image acquisition often degrades the quality of these images, obscuring key anatomical features and complicating accurate diagnoses. Methods This study compared the performance of eight denoising algorithms: BM3D, EPLL, FoE, WNNM, Bilateral, Guided, NLM, and DnCNN. Both objective metrics, including Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR), as well as perceptual quality metrics, such as NIQE, BRISQUE, and PIQE, were employed to assess their effectiveness. Results BM3D consistently outperformed other algorithms at low and moderate noise levels, achieving the highest PSNR and SSIM values while preserving structural integrity and perceptual quality. For high noise levels, conventional algorithms, such as EPLL and WNNM, demonstrated competitive performance in homogeneous areas, preserving fine texture, but were limited by computational complexity. Discussion One of the challenges in image denoising is preserving the finer detail structures of images while efficiently removing noise. Finding a balance between the reduction of noise and preservation of image integrity can be a lifesaving challenge, especially in cases where the images are in high detail, such as in the medical world. Conclusion This study highlights the trade-offs between denoising quality and computational efficiency among various algorithms for MRI and HRCT images. While BM3D remains a dependable choice for moderate noise levels, advanced deep learning-based methods, such as DnCNN, are better suited for handling significant noise variations without compromising critical diagnostic features.
Introduction Differentiating brain tumors through neuroimaging is challenging due to overlapping radiological features, requiring advanced techniques and clinical correlation for accurate diagnosis. The aim of this retrospective observational monocentric study is to determine the diagnostic performance of combining perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI), and magnetic resonance spectroscopy (MRS) for MRI-based differential diagnosis of the three major classes of adult malignant intra-axial brain tumors. Principal component analysis (PCA) is applied to identify relevant imaging features, with the goal of supporting preoperative diagnosis beyond conventional MRI alone. Methods We selected 72 adult patients who underwent MRI examination, including DWI, PWI, and MRS imaging before surgery, for suspected malignant intra-axial expansive lesions (namely glioblastoma, metastasis, or primary non-Hodgkin lymphoma). The definitive histological diagnosis was obtained on post-operative specimens. Quantitative variables derived from DWI, PWI, and MRS acquisition were identified and processed using principal component analysis. The differences between groups for the most relevant parameters identified by PCA were then tested by the Kruskal-Wallis test. Results Finally, a total of 11 specimens of non-Hodgkin lymphomas, 18 specimens of single metastases, and 43 specimens of wild-type glioblastomas were gathered. CBF, CBV, MTT, ADC, and lipid-lactate (Lip-Lac) at MRS were found to be the most relevant variables for differential diagnostic purposes through PCA analysis. In particular, ADC and Lip-Lac were more strongly associated with differentiating lymphoma from the other two disease classes, while CBF, CBV, and MTT contributed more to differentiating glioblastoma from metastasis. Discussion In this study, ADC and Lip-Lac differentiated CNS lymphoma, while CBV, CBF, and MTT distinguished GBM from metastases, supporting PCA’s clinical value beyond diagnostic workflows. Conclusion The combined use of PWI, DWI, and MRS can assist the radiologist in accurate preoperative differential diagnosis of the three main classes of adult malignant intra-axial brain neoplasms, enhancing diagnostic performance beyond that of conventional MRI alone.
Introduction Ischemic stroke remains a leading cause of disability and mortality, making a rapid and reliable diagnosis essential. Dynamic Susceptibility Contrast Magnetic Resonance Imaging (DSC-MRI) is widely used to assess cerebral perfusion, yet its diagnostic accuracy strongly depends on the computational model applied. This study investigates how model selection influences the reliability of CBV-based ischemic stroke detection under varying noise conditions and tissue types. Methods Simulated tissue signal curves were generated from clinical reference data and modified to reflect ischemic alterations across multiple noise levels. Cerebral Blood Volume (CBV) was estimated using two established approaches: the modified gamma variate function and a compartmental (triple-exponential) model. Diagnostic performance was evaluated by comparing the accuracy and robustness of CBV estimation. Results The compartmental model consistently outperformed the gamma variate function, providing more accurate and stable CBV estimates, particularly under high-noise conditions. In contrast, the gamma variate function demonstrated reduced robustness and greater sensitivity to noise. Discussion These findings underscore the importance of computational model selection in DSC-MRI analysis. The performance of the compartmental model suggests its potential for integration into clinical workflows, particularly in acute stroke care, where reliability under challenging conditions is crucial. However, this study has several limitations. Most importantly, the analysis was based on simulated tissue signal curves derived from clinical reference data rather than on in vivo measurements, which may not fully capture the complexity of real patient physiology. Conclusion Computational modeling influences the diagnostic value of DSC-MRI in ischemic stroke assessment. The compartmental model offers greater robustness and accuracy, supporting its use in diagnostic systems.
