Adrian Korbecki

Objectives: To examine the association between optic nerve lesion volume (ONLV) on double inversion recovery (DIR) MRI and other imaging biomarkers of disease burden in multiple sclerosis (MS), including white matter hyperintensities (WMHs), T1‐weighted hypointensities, and brain volumetrics. The study explores whether ONLV reflects more severe neurodegenerative MS profile. Materials and Methods: In this cross‐sectional study, 212 MS patients underwent 3 T MRI, including 3D T1W, FLAIR, and DIR sequences. Optic nerve lesions were segmented on DIR and quantified volumetrically. Patients were categorized by optic nerve involvement: none ( n = 59), unilateral ( n = 60), bilateral ( n = 93). WMHs were analyzed using AI‐based tool. T1W hypointensities and brain volumetrics were extracted using FreeSurfer and AI algorithm applied to 3D T1W and FLAIR images. Multivariate models were adjusted for multiple covariates. Results: ONLV positively correlated with periventricular ( r = 0.365, p < 0.001), deep ( r = 0.165, p = 0.005), and juxtacortical ( r = 0.163, p = 0.007) WMHs. Bilateral involvement was associated with higher WMH burden, increased T1W hypointensities ( β = 10.91, p < 0.001). ONLV was associated with greater white matter atrophy ( β = −107.02, p = 0.016); after adjustment for age, this association was attenuated, while bilateral optic nerve involvement emerged as predictor of gray matter atrophy. Periventricular WMHs also correlated with global cortical and subcortical gray matter loss. Conclusions: ONLV is associated with greater lesion load and neurodegeneration in MS. Longitudinal studies are needed to confirm prognostic value of ONLV as a candidate biomarker of disease severity.

Background/Objectives: To evaluate whether incorporating both diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) in pituitary MRI examinations improves differential diagnosis by providing additional diagnostic value. Methods: A retrospective analysis was performed on 88 patients with histologically confirmed sellar or parasellar tumors who underwent 1.5T MRI with DWI and dynamic susceptibility contrast PWI (DSC-PWI) between October 2007 and April 2023. DWI parameters included minimum apparent diffusion coefficient (ADCmin) and relative ADCmin (rADCmin). PWI parameters included mean and maximum relative cerebral blood volume (rCBV, rCBVmax) and relative peak height (rPH, rPHmax), normalized to white matter. Tumor regions of interest were manually segmented, excluding calcified or hemorrhagic areas. Group comparisons and ROC analyses assessed diagnostic performance of individual and combined parameters. Results: Significant differences in diffusion and perfusion metrics were observed among the five tumor types. The combined analysis of DWI and PWI improved diagnostic accuracy in selected comparisons. The greatest benefit occurred in distinguishing meningiomas from solid non-functional pituitary adenomas (pituitary neuroendocrine tumors-PitNET), where the combination of ADCmin and rPHmax yielded an AUC of 0.818, sensitivity of 88%, and specificity of 76%, exceeding the performance of either parameter alone. In other comparisons, including meningiomas versus invasive PitNETs and adamantinomatous craniopharyngiomas, combined analysis did not substantially improve accuracy when single parameters, particularly rCBVmax (AUC = 0.995), already demonstrated excellent performance. Conclusions: Integration of DWI and PWI into pituitary MRI protocols enhances diagnostic performance in selected tumor groups. The additive value is context-dependent, supporting the tailored application of these sequences in the evaluation of sellar and parasellar tumors.

Background and Objectives: Deep venous thrombosis (DVT) is associated with pulmonary embolism and long-term complications such as post-thrombotic syndrome (PTS). Anticoagulation prevents thrombus extension but does not actively remove clot. Interventional techniques, including catheter-directed thrombolysis, mechanical and pharmacomechanical thrombectomy, and venous stenting, have been introduced to restore venous patency and reduce complications. This systematic review summarizes current evidence on outcomes, safety, and patient selection for these procedures. Materials and Methods: A systematic search of PubMed, EMBASE, Cochrane Library, and Web of Science was conducted for studies published between January 2000 and February 2024. Eligible studies included randomized controlled trials, systematic reviews, meta-analyses, and observational studies with ≥20 patients. Extracted outcomes were technical success, thrombus clearance, venous patency, PTS, quality of life, and complications. Risk of bias was assessed using the Cochrane Risk of Bias Tool, Newcastle–Ottawa Scale, and AMSTAR-2. Results: Of 456 records screened, 35 studies were included. Randomized trials (CaVenT, ATTRACT, CAVA) showed that catheter-directed and pharmacomechanical approaches improved venous patency and reduced moderate-to-severe PTS in selected patients with iliofemoral DVT, though overall benefit was variable. Mechanical thrombectomy devices (e.g., AngioJet, ClotTriever, FlowTriever) achieved high thrombus clearance and shorter procedural times, with device-specific complication profiles. Observational data demonstrated venous stenting patency rates of 74–89% at 12 months. Study heterogeneity limited direct comparisons. Conclusions: Interventional procedures can reduce PTS and improve outcomes in carefully selected patients, particularly those with acute iliofemoral DVT. Modern mechanical and pharmacomechanical techniques enhance efficiency and safety, while venous stenting addresses underlying obstructions. Further high-quality trials with long-term follow-up are needed to define optimal patient selection and comparative effectiveness.

Purpose: This study aimed to optimise the dynamic coronary computed tomography perfusion (CTP) protocol, focusing on patient preparation, scanning parameters, and image acquisition, reconstruction, and interpretation. Future phases will evaluate the diagnostic accuracy of dynamic CTP in detecting haemodynamically significant coronary artery disease (CAD), using invasive coronary angiography (ICA) and fractional flow reserve (FFR) as reference standards.Material and methods: Thirty-six symptomatic patients with confirmed or suspected CAD underwent dynamic CTP using a whole-heart coverage CT scanner (Revolution Apex CT, GE Healthcare). Two patients were excluded due to non-diagnostic CTP results. Of the remaining 34 patients, 24 underwent both cardiac CT angiography (CCTA) and CTP, while 19 underwent CCTA, CTP, and ICA. Preliminary data were analysed by comparing CTP findings with CCTA and ICA/FFR when available.Results: Among 578 myocardial segments, 424 (73.3%) showed normal perfusion and 154 (26.6%) exhibited hypo­perfusion. Of the 17 cases with perfusion deficits, ICA confirmed significant stenosis in 10, resulting in 100% sensitivity and 22% specificity for detecting haemodynamically significant stenosis. FFR assessment in 10 patients demonstrated 60% concordance between CTP, ICA, and FFR. Incorporating CTP into the diagnostic pathway led to a 29.4% reclassification in management strategies.Conclusions: The low specificity observed for detecting significant CAD underscores the need for further refinement of the CTP protocol. Future research should aim to optimise myocardial blood flow thresholds to improve diagnostic specificity and clinical applicability.

Tractography using diffusion tensor imaging (DTI) and constrained spherical deconvolution (CSD) provides valuable insights into the structure of white matter pathways. However, different methodologies may produce divergent fractional anisotropy (FA) values due to fundamental differences in their underlying approaches. This study compares FA measurements obtained using a manual DTI deterministic tractography method and an automatic AI-based approach via the TractSeg framework. Thirty healthy adults underwent brain MRI, and nine major white matter tracts were reconstructed using DTI-based vendor software and CSD with the AI-driven TractSeg. FA measurements were analyzed using inter-rater reliability and agreement metrics, including intraclass correlation coefficients (ICCs). Results revealed substantial differences in FA between the two methods, with ICC values ranging from poor to moderate for most fibers. Normalization using FA values of the corpus callosum (CC) and comparison of relative values further highlighted impactful discrepancies for all of the fibers (p < 0.001). Manual DTI-based methods yielded higher FA values across most tracts, with the largest discrepancies observed in the CC and inferior fronto-occipital fasciculus. Conversely, AI-based TractSeg showed higher FA values for the uncinate fasciculus, demonstrating advantages for smaller, complex fibers. Additionally, tract volume analysis showed that AI-based methods consistently produced larger tract volumes; however, volume differences did not align with FA ICC patterns. This indicates that volumetric discrepancies alone do not explain FA variability between methods. Despite high inter-rater reliability for manual measurements, significant inter-method differences indicate that FA values from the two methods are not interchangeable. Standardization is needed for reliable cross-study comparisons. Highlights: Comparison of manual DTI and AI-based tractography in healthy adults. Manual DTI yielded higher FA values; TractSeg favored smaller, complex tracts. Tract volume differences did not explain FA variability across methods. Poor-to-moderate FA agreement highlights need for cross-method standardization. Study supports careful interpretation of tractography metrics across techniques.

Objectives: To investigate the relationship between optic nerve lesion volume (ONLV), measured on double inversion recovery (DIR) MRI, and other radiological biomarkers of disease burden in multiple sclerosis (MS), including white matter hyperintensities (WMHs), T1-weighted hypointensities (“black holes”), and brain volumetrics. This study aims to determine whether ONLV correlates with a more severe neurodegenerative profile and could serve as a potential biomarker for disease severity. Materials and Methods: In this cross-sectional study, 212 MS patients underwent 3T MRI including 3D T1W, FLAIR, and DIR sequences. Optic nerve lesions were manually segmented on DIR images and quantified volumetrically. Patients were grouped by optic nerve involvement: none (n = 59), unilateral (n = 60), or bilateral (n = 93). WMHs were segmented and anatomically categorized using an AI-based tool. T1W hypointensities were extracted via FreeSurfer, and brain volumetrics were assessed using a machine learning–based segmentation algorithm on both 3D T1W and FLAIR images. Statistical comparisons and correlation analyses were performed using multivariate models adjusted for total intracranial volume. Results: ONLV was positively correlated with periventricular (r = 0.365, p < 0.001), deep white matter (r = 0.165, p = 0.005), and juxtacortical (r = 0.163, p = 0.007) WMHs. Patients with bilateral optic nerve involvement exhibited significantly higher WMH burden, greater T1W hypointensities (β = 10.91, p < 0.001), and more pronounced cerebral white matter atrophy (β = –107.02, p = 0.016). Regional brain atrophy was most evident in structures along the visual pathway (e.g., cuneus, fusiform gyrus, pericalcarine cortex, and thalamus). Additionally, periventricular WMH volume was significantly associated with global brain atrophy, including cortical and subcortical gray matter loss. Conclusions: This study demonstrates that ONLV, as quantified on DIR MRI, is associated with increased lesion burden, irreversible white matter damage, and widespread brain atrophy in MS. These findings suggest that ONLV may serve as a potential imaging biomarker of disease severity and could be integrated into standard MRI protocols for MS assessment. Future longitudinal studies are warranted to validate ONLV as a marker of disease progression and treatment response.

Purpose: This study aimed to assess the role of Diffusion-Weighted Imaging (DWI) in routine pituitary Magnetic Resonance Imaging (MRI) protocols for distinguishing sellar and parasellar tumors, addressing the lack of clear guidelines in contemporary literature. Methods: A retrospective analysis of 242 pituitary MRI scans with DWI sequences was conducted in a single-center study using a 1.5 T scanner and standard DWI sequence parameters. Measurements of both absolute and relative mean apparent diffusion coefficient (ADC) values, along with minimal ADC values within tumors, were performed. The adopted region of interest (ROI) based method used for these measurements was validated. Results: Invasive pituitary adenomas exhibited significantly lower min ADC and min rADC than meningiomas, with optimal cut-off points of 0.64 (sensitivity 73%, specificity 82%) and 0.78 (sensitivity 73%, specificity 89%), respectively. Post-hemorrhagic pituitary adenomas demonstrated lower ADC values than adamantinomatous craniopharyngiomas, with an AUC of 0.893 for min rADC = 1.07, and Rathke’s Cleft Cysts with mucous content, AUC 0.8 for min rADC = 1.01. Specific differentiation with high sensitivity and specificity based on diffusion parameters was observed for these tumor groups. Cystic pituitary non-functional adenomas obtained significantly lower ADC values compared to the adamantinomatous type of craniopharyngiomas and serous Rathke’s Cleft Cysts (AUC up to 0.942). Conclusions: The study concludes that integrating DWI into routine pituitary MRI protocols enhances diagnostic accuracy in distinguishing sellar and parasellar tumors. The short scan time of one minute makes DWI a valuable and precise tool, supporting its recommendation as a standard component of pituitary MRI examinations.

Background: Major Depressive Disorder (MDD) is a significant challenge in modern medicine due to its unclear underlying causes. Brain network dysfunction is believed to play a key role in its pathophysiology. Resting-state functional MRI (rs-fMRI), a neuroimaging technique, enables the in vivo assessment of functional connectivity (FC) between brain regions, offering insights into these network dysfunctions. The aim of this study was to evaluate abnormalities in FC within major brain networks in patients with drug-resistant MDD. Methods: The study group consisted of 26 patients with drug-resistant MDD and an age-matched control group (CG) of 26 healthy subjects. The rs-fMRI studies were performed on a 3T MR scanner (Philips, Ingenia) using a 32-channel head and neck coil. Imaging data were statistically analyzed, focusing on the intra- and inter-network FC of the following networks: default mode (DMN), sensorimotor (SMN), visual (VN), salience (SN), cerebellar (CN), dorsal attention (DAN), language (LN), and frontoparietal (FPN). Results: In patients with MDD, the intra-network analysis showed significantly decreased FC between nodes within VN compared to CG. In contrast, the inter-network analysis showed significantly increased FC between nodes from VN and SN or VN and DAN compared to CG. Decreased FC was found between SN and CN or SN and FPN as well as VN and DAN nodes compared to CG. Conclusions: Patients with MDD showed significant abnormalities in resting-state cortical activity, mainly regarding inter-network functional connectivity. These results contribute to the knowledge on the pathomechanism of MDD and may also be useful for developing new treatments.