Abstract Neurological symptoms are common in post-COVID-19 condition (PCC) and have been linked to underlying brain alterations. However, in individuals with PCC following a mild infection without hospitalization, such alterations are rarely detected using conventional neuroimaging techniques. This study aims to investigate brain connectivity in patients with PCC with cognitive symptoms after mild COVID-19 infection, using resting-state functional magnetic resonance imaging (rs-fMRI). Additional aims were to explore associations between brain connectivity, neuropsychological performance, and self-reported fatigue and emotional status. Patients with PCC (n = 22) and lasting cognitive symptoms and fatigue were consecutively recruited from a regional rehabilitation unit and compared with a convenience sample of non-symptomatic controls (n = 19). The assessments were conducted on average 32 months post-infection and included 3 Tesla rs-fMRI, neuropsychological testing, and self-report measures of fatigue (MFI-20), anxiety, and depression (HADS). Patients with PCC had elevated functional connectivity in brain regions associated with the default mode network (DMN) compared to controls. No significant correlations were found between functional connectivity, neuropsychological test performance, fatigue, anxiety, or depression. Our findings suggest persistent alterations in DMN connectivity in PCC with cognitive symptoms and fatigue, underscoring the need for continued larger studies on brain functioning in this patient group. Clinical trial registration: No. NCT06042530.

BACKGROUND/OBJECTIVES:Characterizing spinal cord multiple sclerosis (MS) lesions in MRI is critical for diagnosis, monitoring, and treatment evaluation. However, current automated approaches for lesion detection and segmentation are typically designed for specific MRI contrasts or acquisition sites, limiting their generalizability in real-world clinical settings where imaging protocols vary widely. This work proposes a robust multi-site, multi-contrast segmentation framework for spinal cord lesions. METHODS:The segmentation model was trained and evaluated on a large-scale dataset comprising 4428 annotated images from 1849 persons with MS across 23 imaging centers, encompassing six MRI contrasts (T1w, T2w, T2*w, PSIR, STIR, and UNIT1) acquired at 1.5 tesla (T), 3 T, and 7 T. RESULTS:Likert-type assessment performed by neuroradiologist ratings demonstrated superior generalization of the model compared to existing contrast-specific pipelines (p < 0.01). Additional experiments evaluated robustness across spinal levels, acquisition resolutions, binarization thresholds, and quantitative evaluation on external labeled datasets. CONCLUSIONS:The proposed model can achieve accurate and reliable spinal cord MS lesion segmentation across heterogeneous MRI data, addressing a key barrier to clinical translation. The model is available in the Spinal Cord Toolbox v7.2 and higher.Code repository: https://github.com/ivadomed/seg-sc-ms-lesion-multicontrast.

Background Multiple sclerosis (MS) is the most common disabling neurological disease of young adults, with many approved disease-modifying treatments (DMTs). We developed and validated a Bayesian modelling framework to predict individual-level treatment response.Methods We used two prospective cohorts of people with relapsing–remitting MS (pwMS) in the UK (training) and Sweden (external validation), with clinical and MRI data assessed within 6 months of DMT initiation, and outcomes (no-evidence-of-disease-activity (NEDA) over 2 years. Bayesian models of increasing complexity (up to 12 baseline predictors) predicted NEDA (no-evidence-of-disease-activity: absence of relapse, disability progression, and new MRI lesions) at 1 and 2 years and were externally validated. Internal performance used ELPD-LOO (expected leave-one-out log posterior density), AUROC (area-under-the-receiver-operating-characteristic curve), R² (explained variance), and calibration plots; external validation used AUROC and R².Findings The training and external cohorts comprised 1,873 and 808 pwMS, respectively, with differing baseline characteristics; NEDA occurred in 65.6% and 70.3% over 2 years. Greater model complexity improved internal NEDA prediction; the most complex model reached AUROC=0.71 (95% Bayesian Credible Interval 0.70–0.72) at year 1 and 0.66 (0.65–0.67) at year 2. External performance was modest: AUROC=0.59 (0.57–0.61) at year 1 and 0.57 (0.55–0.58) at year 2. At year 2, explained variance was low but higher for complex than DMT-only models (R²=0.08 [0.06–0.10] vs 0.03 [0.01–0.05]). Complex models showed consistent calibration across patient subgroups, and individual predictions with uncertainty were illustrated.Interpretation A Bayesian framework using routine clinical and MRI data achieved modest-to-moderate individual-level outcome prediction, but discrimination decreased on geographic validation, highlighting challenges in transporting prediction models across settings with different case-mix, monitoring and treatment practices. Further multicenter training and local model updating may improve prediction.Funding National Institute for Health and Care Research (NIHR).

BACKGROUND AND PURPOSE:Recent MRI developments have allowed for in vivo myelin imaging in clinically feasible time frames. This retrospective study aimed to evaluate the ability of the Rapid Estimation of Myelin for Diagnostic Imaging (REMyDI) technique in monitoring longitudinal myelin changes and brain atrophy in persons with multiple sclerosis (pwMS) undergoing treatment with rituximab or autologous hematopoietic stem cell transplantation (aHSCT). METHODS:Between May 2017 and January 2022, 62 pwMS treated with either rituximab (n = 25) or aHSCT (n = 37) underwent brain MRI scans at three time points. A 3 Tesla brain MRI was performed, including 3D T1-weighted imaging, 3D T2-weighted fluid-attenuated inversion recovery imaging, and 2D multi-dynamic multi-echo imaging for REMyDI and brain volumetrics. Longitudinal changes in imaging parameters and associations with the Expanded Disability Status Scale and Symbol Digit Modalities Test were analyzed using mixed-effects models. RESULTS:The rituximab group exhibited increases in whole-brain myelin (+0.25 mL per year), cortical myelin (+0.11 mL per year), and myelin in normal-appearing deep gray matter (NADGM) (+0.02 mL per year). In contrast, these measures were stable or declined in the aHSCT group. Brain parenchymal fraction showed a larger reduction in the rituximab group (-0.68% per year) compared to the aHSCT group (-0.24% per year). Myelin-related imaging measures showed positive but nonsignificant associations with clinical parameters. CONCLUSIONS:REMyDI enables longitudinal assessment of myelin-related metrics in vivo, which complements conventional brain volumetrics and is suitable for monitoring treatment responses in MS.

Brain imaging is essential in the diagnostic workup of cognitive disorders. Computed tomography (CT) is usually the first-line method due to accessibility and patient comfort, whereas magnetic resonance imaging (MRI) offers higher diagnostic precision and is required before anti-amyloid therapy. MRI adds value by detecting microvascular pathology, enabling volumetric analysis, and ensuring safe monitoring of amyloid-related imaging abnormalities (ARIA). National Swedish MRI protocols and structured reporting templates support harmonized diagnostics and follow-up. Nuclear medicine methods are useful for complex cases to assess glucose metabolism, amyloid burden or dopamine transport function. With emerging treatment options for Alzheimer's disease, standardized imaging and close collaboration across specialties are essential for upscaling diagnostic routines and for safe and efficient care.

Abstract Background and aims Fast magnetic resonance imaging (MRI) sequences have shown potential for accurate diagnosis and improved patient tolerability in stroke evaluation. This study aimed to compare ultra-fast echo-planar image mix (EPIMix) with conventional MRI as gold standard (GS) in diagnosing suspected minor stroke. Methods In this prospective study, 101 persons with suspected stroke and negative brain computed tomography (CT) were examined with GS (acquisition time eleven minutes) and EPIMix (78 seconds) at 3 Tesla, including multiple weightings (T2, T2 Fluid-Attenuated Inversion Recovery [FLAIR], T1-FLAIR, diffusion [DWI], T2*). One neuroradiologist and one radiology resident independently evaluated images for acute infarcts, white matter hyperintensities (WMH) and cerebral microbleeds (CMB). Sensitivity and specificity were calculated for EPIMix compared to GS and interrater reliability using weighted Cohen’s kappa for diagnostic confidence quality. Results Thirty-four patients were diagnosed with ischemic stroke; mean National Institutes of Health Stroke Scale scores at inclusion were 1 (IQR 0-3). Diffusion restriction was detected in 28 persons when examined with GS vs 23 with EPIMix (sensitivity 0.82, specificity 1.00). Moderate to severe WMH (Fazekas scale grade 2-3) was seen in 41 vs 40 persons (sensitivity 0.88, specificity 0.93) and CMB in 26 vs 19 persons (sensitivity 0.58, specificity 0.94). Inter-rater reliability was greater for EPIMix (k=0.72) than GS (k=0.62). Conclusions In persons with minor stroke and negative brain CT, EPIMix had a good, but not perfect ability to detect ischemia. The high specificity allows for ruling in ischemia, but not for ruling out. EPIMix also had good specificity for detecting WMH and CMB. Conflict of interest Oskar Koivisto Taxén: nothing to disclose. Ida Gugler: nothing to disclose. Jenny Bäcklund: nothing to disclose. Tommy Sjöberg: nothing to disclose. Tobias Granberg: nothing to disclose. Anders von Heijne: nothing to disclose. Björn Hansen: nothing to disclose. Annika Lundström: nothing to disclose.

Morphometric measures derived from spinal cord segmentations can serve as diagnostic and prognostic biomarkers in neurological diseases and injuries affecting the spinal cord. For instance, the spinal cord cross-sectional area can be used to monitor cord atrophy in multiple sclerosis and to characterize compression in degenerative cervical myelopathy. While robust, automatic segmentation methods to a wide variety of contrasts and pathologies have been developed over the past few years, whether their predictions are stable as the model is updated using new datasets has not been assessed. This is particularly important for deriving normative values from healthy participants. In this study, we present a spinal cord segmentation model trained on a multisite (n = 75 sites, 1,631 participants) dataset, including 9 different MRI contrasts and several spinal cord pathologies. We also introduce a lifelong learning framework to automatically monitor the morphometric drift as the model is updated using additional datasets. The framework is triggered by an automatic GitHub Actions workflow every time a new model is created, recording the morphometric values derived from the model’s predictions over time. As a real-world application of the proposed framework, we employed the spinal cord segmentation model to update a recently introduced normative database of healthy participants containing commonly used measures of spinal cord morphometry. Results showed that (i) our model performs well compared with its previous versions and existing pathology-specific models on the lumbar spinal cord, images with severe compression, and in the presence of intramedullary lesions and/or atrophy achieving an average Dice score of 0.95 ± 0.03; (ii) the automatic workflow for monitoring morphometric drift provides a quick feedback loop for developing future segmentation models; and (iii) the scaling factor required to update the database of morphometric measures is nearly constant among slices across the given vertebral levels, showing minimum drift between the current and previous versions of the model monitored by the framework. The code and model are open source and accessible via Spinal Cord Toolbox v7.0.

Brain atrophy subtypes are increasingly recognized in Alzheimer’s disease (AD) dementia. However, their relevance across the real-world memory clinic spectrum, from subjective cognitive impairment (SCI) and mild cognitive impairment (MCI) to AD and non-AD dementias, remains unclear. This cross-sectional study aimed to identify MRI-based atrophy subtypes in a relatively young memory clinic and examine associations with demographic, cerebrospinal fluid (CSF) biomarkers, and cerebrovascular burden to inform precision medicine approaches. We included all consecutive patients (SCI to dementia), evaluated at the Karolinska University-Hospital Memory Clinic (Stockholm, Sweden) between 2018 and 2023 with available clinical and 3T MRI data. Subtypes were defined using FreeSurfer-derived volumetric measures and a validated algorithm combining categorical classification (typical, limbic predominant, cortical predominant, minimal atrophy) with continuous indices of typicality (cortical predominant–limbic predominant) and severity (minimal atrophy–typical). Demographics, cognitive profiles, APOE ε4 status, CSF biomarkers (Aβ42, Aβ42/40, phosphorylated [p]-tau181, total tau, neurofilament light chain [NFL]), and cerebrovascular burden were compared across subtypes. Analyses were replicated in Aβ-positive individuals and those eligible for anti-Aβ therapy. Among 809 patients (median age 60.0 years [interquartile-range 56.0–63.0], 56.1

BACKGROUND AND PURPOSE:Photon-counting CT offers higher spatial resolution, improved contrast-to-noise efficiency, and spectral imaging, but its performance against heterogeneous EID-CT systems in routine use remains incompletely characterized. We compared image quality, pathology conspicuity, and radiation dose between clinical PCCT and EID-CT in paired routine neuroradiological examinations, accounting for differences in scanner generation, protocol, and reconstruction. We hypothesized higher image-quality and pathology-conspicuity scores and dose efficiency, while treating the comparison as pragmatic rather than detector-isolating. MATERIALS AND METHODS:This retrospective study included consecutive adults undergoing routine clinical PCCT from December 2023 through February 2024 who had a corresponding same-region EID-CT within three months; when several EID-CT scans were eligible, the closest in time was selected. Examinations were grouped by protocol: non-contrast brain CT was the primary cohort; contrast-enhanced brain CT and arterial and venous CTA were exploratory. Four blinded readers assessed image quality and pathology conspicuity, with paired scans assigned to separate sessions at least four weeks apart. CNR, dose-normalized CNR (CNRD), and radiation dose were compared within subjects using the Wilcoxon signed-rank test. RESULTS:The 194 patients (mean age, 65±15 years; 100 males) contributed to 270 pairs: 225 non-contrast brain CTs, 14 contrast-enhanced brain CTs, 26 arterial CTAs, and 5 venous CTAs. In the primary cohort, PCCT was associated with higher overall image-quality scores (median 4 versus 3; p<.001), higher time-independent pathology conspicuity in 224/225 pairs (median 4 versus 3; p<.001), 102%-152% higher GM/WM CNR, 128%-180% higher CNRD, and 22% lower median dose (38.4 versus 49.2 mGy; all p<.001). All three primary endpoints remained significant in the ≤1-day subset (n=60). Artifact scores did not differ significantly. Exploratory contrast-enhanced and angiographic analyses favored PCCT for selected parenchymal and small-vessel metrics, including perforator visualization (median 3 versus 2; p<.001), but these small subgroups were preliminary and did not strengthen the primary conclusions. CONCLUSIONS:PCCT was associated with higher image quality, pathology conspicuity, GM/WM CNR and CNRD, and lower dose in routine non-contrast brain CT. Smaller protocol groups were exploratory. Because scanner generations, vendors, protocols, and reconstructions were unmatched, the differences cannot be attributed solely to detector technology.

Background Primary orthostatic tremor (POT) is a rare progressive neurological disorder with a high-frequency tremor while standing. The pathophysiology remains uncertain, and imaging studies have been inconclusive.Objectives The primary objective was to assess whether neurodegenerative changes are detectable in POT by quantitative susceptibility mapping (QSM) on brain MRI. A second objective was to explore volumetric changes in subcortical grey matter and cerebellum.Methods In this cross-sectional study, 20 participants with POT and 14 age- and sex-matched healthy controls (HCs) were examined using a 3 T Siemens Prisma scanner with a T2*-weighted multiecho gradient-echo sequence for QSM. Regions of interest (ROIs) were automatically and manually segmented. Selected ROIs were the red nucleus, globus pallidus (GP), thalamus, caudatus, putamen, substantia nigra, dentate nucleus and inferior and superior colliculus.Results There was a significant increase in susceptibility in the red nucleus (mean susceptibility 138.1 parts per billion (ppb) in POT and 113.3 ppb in HC, p=0.015) and GP (mean susceptibility 101.2 ppb in POT and 73.3 ppb in HC, p=0.0015) and a significantly lower volume of the GP in the POT group compared with HC (mean volume 3838 mm3 in POT and 4062 mm3 in HC, p=0.012).Conclusion Increased susceptibility and decrease of volume indicate the possibility of a neurodegenerative process affecting the red nucleus and GP in POT. The red nucleus and GP are involved in motor control, and a focal dysfunction in these networks may be a part of the cause of orthostatic tremor.

This scientific commentary refers to ‘Evidence of skull bone translocator protein overexpression linked to multiple sclerosis progression’ by Corazzolla et al. (https://doi.org/10.1093/brain/awag084).

Pathological CGG expansions in the FMR1 gene, encompassing premutations (55–200 repeats) and full range mutations (> 200 repeats), cause a spectrum of complex and incurable disorders. Premutation carriers (PMC), particularly men, face the risk of developing fragile X-associated tremor/ataxia syndrome (FXTAS). Other conditions associated with premutations in FMR1 include both Fragile X-associated primary ovarian insufficiency (FXPOI), and Fragile X-associated neuropsychiatric disorders (FXAND). Hyperintensities in the middle cerebellar peduncle (MCP), known as the MCP sign, have been considered a radiological hallmark for FXTAS. FMR1-related disorders have not been characterized in Scandinavia. Here, we delineate the clinical spectrum of FMR1 related disorders among PMC emphasizing neuroimaging abnormalities and assess the correlation between CGG repeat size and clinical parameters as well as neuroimaging abnormalities at a tertiary center in Sweden. In total 33 PMC were evaluated (19 women and 14 men) of which 21 received a FMR1 related disorder (64

Multiple sclerosis (MS) shows a highly heterogeneous course, with some patients accumulating severe disability early while others remain relatively preserved even after decades. A key driver of disability progression is smoldering inflammation, a chronic, compartmentalized immune process at the edge of chronic active lesions. However, the factors driving smoldering inflammation in MS remain incompletely understood. We investigated the role of genetic variation in smoldering inflammation–related genes across two independent MS cohorts, using a discovery-replication design in a total of 2,817 patients. We identified a locus in the HIF1A (Hypoxia-Inducible Factor 1-alpha) gene that is associated with a more favorable disease course at over 20 years from disease onset. Using additional independent cohorts, we found that carriers of the HIF1A protective allele exhibited lower paramagnetic rim lesion volume on MRI, lower plasma and cerebrospinal fluid neurofilament levels, and reduced microglial/macrophage inflammation with less axonal injury in post-mortem progressive MS tissue. By integrating single-nucleus RNA sequencing and spatial transcriptomics, we showed that the HIF1A variant dynamically modulates gene expression in a cell-type specific and context-dependent manner in the MS brain. Collectively, these findings highlight a protective HIF1A variant associated with a more favourable long-term disease course and reduced smoldering inflammation, opening new avenues to translate this genetic discovery into new potential strategies to tackle disease progression.

Time-resolved three-dimensional phase-contrast MRI (4D Flow MRI) enables non-invasive quantification of blood flow and derivation of hemodynamic parameters. However, its clinical application is limited by low spatial resolution and noise, particularly affecting velocity measurements near vessel walls. Machine learning-based super-resolution has shown promise in addressing these limitations, but challenges remain, not least in recovering near-wall velocities. Generative adversarial networks (GANs) offer a compelling solution, having demonstrated strong capabilities in restoring sharp boundaries in non-medical super-resolution settings. Yet, their application in 4D Flow MRI remains unexplored, with implementation challenged by known issues such as training instability and non-convergence. In this study, we investigate GAN-based super-resolution and denoising in 4D Flow MRI. Training and validation were conducted using patient-specific cerebrovascular in-silico models, converted into synthetic images via an MR-true reconstruction pipeline, with complementary validation on in-vivo acquisitions. A dedicated GAN architecture was implemented and evaluated across three adversarial loss functions: Vanilla, Relativistic, and Wasserstein. Our results demonstrate that the proposed GAN improved near-wall velocity recovery compared to a non-adversarial reference (vector Normalized Root Mean Square Error (vNRMSE): 6.9% vs. 9.6%); however, implementation specifics are critical for stable network training. While Vanilla and Relativistic GANs proved unstable compared to generator-only training (vNRMSE: 8.1% and 7.8% vs. 7.2%), a Wasserstein GAN demonstrated optimal stability and incremental improvement (vNRMSE: 6.9% vs. 7.2%). Moreover, strong in-vivo performance supports clinical translation. Together, these findings highlight the potential of GAN-based super-resolution in enhancing 4D Flow MRI, particularly in challenging cerebrovascular regions, while emphasizing the importance of carefully selecting adversarial training strategies.

Introduction: Early identification of the etiology of ischemic stroke is crucial for secondary prevention. Recent publications on dedicated ECG-gated cardiac CT showcase the potential to detect sources of cardiac embolism. Photon-counting CT (PCCT) uses semiconductors to convert X-ray photons into electrical signals directly. This allows for improved image quality and lower radiation dose compared to conventional CT. Exploiting PCCT technology with a high-pitch dual-source image acquisition may render an exceptionally high temporal resolution. We hypothesized that non-ECG-gated high-pitch dual-source PCCT angiography from the diaphragm to the brain might provide added clinical value in detecting cardiac stroke sources during initial stroke imaging while maintaining optimal brain and neck image quality. Methods: Consecutive patients with a clinical suspicion of acute stroke imaged with a PCCT system between October 4 th , 2023 and April 13 th , 2024 at a Swedish comprehensive stroke center were included. Diaphragm to brain coverage was obtained in an acquisition time of 1.3 seconds; on average, yielding a dose-length product of 360 mGy*cm for the study participants (2.34 mSv). Image quality was graded using a 4-point Likert scale. Images were assessed for cardiac stroke sources. Where available, reference standard echocardiography results were collected. Results: Of 249 consecutive stroke investigations, 193 included cardiothoracic imaging, which constituted the study population. 126 scans (65.3%) were cases with imaging confirmed ischemic stroke. The median age was 74 (IQR, 61-81) years, 99 were male (51.3%). In total, 39.4% underwent follow-up echocardiography. Image quality of the heart was excellent in 19 scans (9.8%) good in 97 (50.3%), moderate in 72 (37.3%), and poor in 5 scans (2.6%). Fourteen (7.3%) certain or probable interatrial septal defects were found in the study population. In the imaging-confirmed ischemic stroke subgroup, six certain or probable cardiac thrombi were found (4.8%). Additionally, a possible cardiac thrombus was found in 31 instances (24.6%). Three aortic valve vegetations were found (2.4%), all confirmed by echocardiography. Conclusions: Non-ECG-gated high-pitch dual-source PCCT angiography typically provides images of high quality, at virtually no time expense while maintaining a reasonably low radiation exposure. Non-gated PCCT angiography is a promising technique to be used as a primary screening method for cardioembolic stroke.

'Brain age' is a numerical estimate of the biological age of the brain and an overall effort to measure neurodegeneration, regardless of disease type. In multiple sclerosis, accelerated brain ageing has been linked to disability accrual. Artificial intelligence has emerged as a promising tool for the assessment and quantification of the impact of neurodegenerative diseases. Despite the existence of numerous AI models, there is a noticeable lack of comparative imaging data for traditional machine learning versus deep learning in conditions such as multiple sclerosis. A retrospective observational study was initiated to analyse clinical and MRI data (4584 MRIs) from various scanners in a large longitudinal cohort (n = 1516) of people with multiple sclerosis collected from two institutions (Karolinska Institute and Oslo University Hospital) using a uniform data post-processing pipeline. We conducted a comparative assessment of brain age using a deep learning simple fully convolutional network and a well-established traditional machine learning model. This study was primarily aimed to validate the deep learning brain age model in multiple sclerosis. The correlation between estimated brain age and chronological age was stronger for the deep learning estimates (r = 0.90, P < 0.001) than the traditional machine learning estimates (r = 0.75, P < 0.001). An increase in brain age was significantly associated with higher expanded disability status scale scores (traditional machine learning: t = 5.3, P < 0.001; deep learning: t = 3.7, P < 0.001) and longer disease duration (traditional machine learning: t = 6.5, P < 0.001; deep learning: t = 5.8, P < 0.001). No significant inter-model difference in clinical correlation or effect measure was found, but significant differences for traditional machine learning-derived brain age estimates were found between several scanners. Our study suggests that the deep learning-derived brain age is significantly associated with clinical disability, performed equally well to the traditional machine learning-derived brain age measures, and may counteract scanner variability.

PURPOSE:Early diagnosis of cauda equina syndrome is essential to prevent irreversible neurological damage, but MRI can be unavailable or contraindicated. This study aimed to evaluate whether photon-counting CT (PCCT) can be a reliable alternative to reference standard MRI for diagnosing cauda equina syndrome. METHODS:In this prospective study, participants with different conditions, including degenerative spinal canal stenosis, disk herniation, vertebral compression fracture, and intraspinal extradural tumor underwent PCCT and MRI between November 2022 and March 2024 at a university hospital. Three radiologists independently evaluated images for compression of cauda equina and/or spinal cord, level and cause of compression, and spinal cord visibility. Intrarater sensitivity and specificity in diagnosing compression of cauda equina and/or spinal cord on PCCT versus MRI were calculated. Pearson correlation between PCCT and MRI was assessed for dural sac areas at all lumbar disk levels. Point-biserial correlation was calculated for body mass index (BMI) versus spinal cord visibility. RESULTS:A total of 14 participants [mean age 76±6 y (SD); 8 women] were examined. PCCT demonstrated 100% sensitivity and 60% to 83% specificity for diagnosing compression of cauda equina and/or spinal cord across 3 raters, compared with MRI. Axial area measurements showed an almost perfect correlation between modalities (r >0.9), with PCCT slightly underestimating areas in 70% of measurements. PCCT visualized the spinal cord in all participants, but in 19% of the assessments it was barely visible. No correlation was found between BMI and spinal cord visibility ( P >.05). CONCLUSIONS:Photon-counting CT demonstrated its usefulness as a rapid alternative in selected patients with suspected cauda equina syndrome (excluding spinal hematomas and spondylodiscitis), when MRI is unavailable or contraindicated. There were strong correlations in spinal canal stenosis measurements with MRI, showing its potential as an alternative to MRI in nonacute conditions of the lumbar spine, such as degenerative disk disease.