Search Results (59)

MRI perfusion is used to diagnose and monitor neurological conditions such as brain tumors, stroke, dementia, and traumatic brain injury. Dynamic Susceptibility Contrast (DSC) is the most widely available quantitative MRI technique for perfusion imaging. Even in its most basic implementation, DSC MRI provides critical hemodynamic metrics like cerebral blood flow (CBF), blood volume (CBV), mean transit time (MTT), and time between the peak of arterial input and residue function (Tmax), through the dynamic tracking of a gadolinium-based contrast agent. Notwithstanding its high clinical importance and widespread use, the reproducibility and diagnostic reliability are impeded by a lack of standardized pre-processing protocols and quality controls. A comprehensive literature review and the authors’ aggregated experience identified common DSC MRI artefacts and corresponding pre-processing methods. Pre-processing methods to correct for artefacts were evaluated for their practical applicability and validation status. A consensus on the pre-processing was established by a multidisciplinary team of experts. Acquisition-related artefacts include geometric distortions, slice timing misalignment, and physiological noise. Intrinsic artefacts include motion, B₁ inhomogeneities, Gibbs ringing, and noise. Motion can be mitigated using rigid-body alignment, but methods for addressing B₁ inhomogeneities, Gibbs ringing, and noise remain underexplored for DSC MRI. Pre-processing of DSC MRI is critical for reliable diagnostics and research. While robust methods exist for correcting geometric distortions, motion, and slice timing issues, further validation is needed for methods addressing B₁ inhomogeneities, Gibbs ringing, and noise. Implementing adequate mitigation methods for these artefacts could enhance reproducibility and diagnostic accuracy, supporting the growing reliance on DSC MRI in neurological imaging. Finally, we emphasize the crucial importance of pre-scan quality assurance with phantom scans. Full article

Background: Distinguishing tumor recurrence from radiation necrosis after radiotherapy for brain metastases remains a major diagnostic challenge. Perfusion MRI, particularly the measurement of relative cerebral blood volume (rCBV), is a commonly used technique to differentiate between these two entities. However, variations in the placement of regions of interest (ROIs) affect diagnostic accuracy. This study compares the diagnostic performance of different cerebral perfusion methods, including a novel volumetric 3D ROI method and automatic thresholding, to differentiate tumor recurrence from radiation necrosis. Methods: We retrospectively analyzed data from 23 patients, including 25 brain metastases treated with stereotactic radiotherapy, who were suspected of local recurrence and had histological confirmation via biopsy or surgical resection. Each patient underwent perfusion MRI before surgery. The diagnostic performance of the different ROI methods (manual and 3D) was evaluated using the area under the ROC curve (AUC), as well as sensitivity and specificity measures. An automatic thresholding method was also applied, generating tumor sub-volumes with predefined cut-off values to determine the rCBV threshold most specific for differentiating relapse from necrosis. Results: The 3D ROI method, considering the whole lesion and a healthy ROI in the head of the caudate nucleus, demonstrated superior diagnostic performance (AUC = 0.65), outperforming manual methods (AUC = 0.53). Robustness was moderate, with an intraclass correlation coefficient of 0.60 between Syngo.via and IntelliSpace. Conclusions: The 3D ROI method shows promise in improving diagnostic accuracy in distinguishing tumor recurrence from radiation necrosis. Further studies with standardized protocols and larger populations are needed to validate these results. Full article

Background/Objectives: This study evaluates intratumoral susceptibility signals (ITSS) as imaging markers for glioma grade prediction and their association with molecular and histopathologic features, in the context of the fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS5). Methods: We retrospectively analyzed patients with adult diffuse gliomas who underwent pretreatment magnetic resonance imaging. ITSS were semiquantitatively graded by two radiologists: grade 0 (no signal), grade 1 (1–5), grade 2 (6–10), and grade 3 (≥11). Relative cerebral blood volume (rCBV) and tumor volume were also obtained. Histopathologic features included tumor grade, Ki-67, mitotic count, necrosis, microvascular proliferation, and molecular alterations (isocitrate dehydrogenase [IDH], 1p/19q, cyclin-dependent kinase inhibitors 2A and 2B [CDKN2A/B], and p53). Regression models predicted tumor grade (low: 1–2, high: 3–4) using ITSS, tumor volume, and rCBV. ROC curves and diagnostic performance metrics were analyzed. Results: 99 patients were included. ITSS grading correlated with rCBV, tumor volume, mitotic count, Ki-67, and tumor grade (p < 0.001). ITSS grades 0–1 were associated with oligodendrogliomas and astrocytomas (p < 0.001), IDH mutations (p < 0.001), and 1p/19q co-deletions (p = 0.01). ITSS grades 2–3 were linked to glioblastomas (p < 0.001), necrosis (p < 0.001), microvascular proliferation (p < 0.001), and CDKN2A/B homozygous deletions (p = 0.02). Models combining ITSS with rCBV and volume showed AUC of 0.94 and 0.96 (p < 0.001), outperforming univariate models. Conclusions: Semiquantitative ITSS grading correlates with key histopathologic and molecular glioma features. Combined with perfusion and volumetric parameters, ITSS enhance non-invasive glioma grading, in alignment with WHO CNS5. Full article

Background/Objectives: The accurate diagnosis and classification of brain tumors are critical for appropriate treatment planning and patient management. We evaluated the effectiveness of perfusion in differentiating glial tumors from metastases using dynamic susceptibility-weighted contrast enhanced perfusion MRI (DSC-MRI) Methods: A total of 95 consecutive patients with pathological diagnoses of brain tumors who underwent perfusion MRI between July 2021 and March 2023 were retrospectively recruited. Conventional and perfusion MRI were evaluated, and tumoral and peritumoral relative cerebral blood volume (rCBV) values were measured. Mann–Whitney U and Kruskal–Wallis tests were performed for non-parametric comparisons of continuous data. The optimal cut-off value of rCBV in differentiating tumor types was evaluated with the receiver operating characteristic (ROC) curve analysis. Results: Tumoral rCBV (p < 0.001) and peritumoral rCBV values (p = 0.001) were significantly higher in glial tumors than metastases. Further subgroup analyses showed that tumoral and peritumoral rCBV values of glial tumors were higher than those of non-small-cell lung cancers (p < 0.001 and p = 0.003, respectively) and those of breast cancer (p = 0.311 and p = 0.053, respectively) in discriminating high-grade glial tumors and metastases. ROC analyses showed that area under the curve values for tumoral and peritumoral rCBV were 0.816 and 0.725, respectively, for the optimal cut-off points 1.339 and 1.238 (87.5% and 58.33% sensitivity; 73.85% and 90.77% specificity, respectively). Multivariate analysis showed that increased tumoral rCBV and peritumoral rCBV values were independent risk factors for glial tumor occurrence. Conclusions: DSC-MRI is an effective method to differentiate glial tumors and metastases. Higher rCBV values may serve as a determinant for the diagnosis of glial tumors and metastatic brain tumors. Full article

Background. Statins appear to be useful in patients with acute ischemic stroke. Our aim was to evaluate the association between premorbid statin treatment and CT perfusion characteristics of acute ischemic stroke. Methods. A retrospective analysis of patients with acute stroke secondary to occlusion of large vessels in the anterior circulation was performed to assess collateral flow, ischemic core volume, and ischemic penumbra using CT angiography and CT perfusion maps. Fisher’s exact test was used to compare baseline characteristics of patients in the two groups. The Wilcoxon rank-sum test for independent groups was used to compare all variables obtained for the two different groups with and without statin use. Results. We identified 61 patients, including 29 treated with statins and 32 not treated with statins before stroke onset matched by age, gender, and vascular risk factors except for hypercholesterolemia. The statin group showed lower National Institutes of health Stroke Scale scores at onset (14 ± 6.1 vs. 16 ± 4.5; p = 0.04) and lower volumes of brain tissue characterized by impaired cerebral blood flow (CBF), cerebral blood volume (CBV), and ${\mathrm{T}}_{\mathrm{m}\mathrm{a}\mathrm{x}}^{9.5-25\mathrm{s}}$; otherwise, no statistically significant difference was found in the volume of the ${\mathrm{T}}_{\mathrm{m}\mathrm{a}\mathrm{x}}^{16-25\mathrm{s}}$ between the two groups. Conclusions. Premorbid statin treatment is associated with a favorable imaging condition of acute ischemic stroke in terms of ischemic core and ischemic penumbra volume. Full article

Background: Dynamic susceptibility contrast perfusion MRI (DSC-MRI) is a promising non-invasive examination to predict histological and molecular characteristics of brain gliomas. However, the diagnostic accuracy of relative cerebral blood volume (rCBV) is heterogeneously reported in the literature. This systematic review and meta-analysis aims to assess the diagnostic accuracy of mean rCBV derived from DSC-MRI in differentiating Isocitrate Dehydrogenase (IDH)-mutant from IDH-wildtype gliomas. Methods: A comprehensive literature search was conducted in PubMed, Web of Science, and EMBASE up to January 2025, following PRISMA guidelines. Eligible studies reported mean CBV values in treatment-naïve gliomas with histologically confirmed IDH status. Pooled estimates of standardized mean differences (SMDs), diagnostic odds ratios (DOR), and area under the receiver-operating characteristic curve (AUC) were computed using a random-effects model. Heterogeneity was assessed via I² statistic. Meta-regression analyses were also performed. Results: An analysis of 18 studies (n = 1733) showed that mean rCBV is significantly lower in IDH-mutant gliomas (SMD = −0.86; p < 0.0001). The pooled AUC was 0.80 (95% CI, 0.75–0.90), with moderate sensitivity and specificity. Meta-regression revealed no significant influence of DSC-MRI acquisition parameters, although a flip angle showed a trend toward significance (p = 0.055). Conclusions: Mean rCBV is a reliable imaging biomarker for IDH mutation status in gliomas, demonstrating good diagnostic performance. However, heterogeneity in acquisition parameters and post-processing methods limits generalizability of results. Future research should focus on standardizing DSC-MRI protocols. Full article

Background: Differentiating brain radionecrosis (RN) from tumor progression (TP) is a persistent clinical difficulty. Here, we compared the diagnostic accuracy of leakage-corrected relative cerebral blood volume (rCBV) and fluid-suppressed amide proton transfer-weighted (APTw) imaging in distinguishing between RN and TP in metastases. Methods: Subjects with enlarging lesions after stereotactic radiosurgery were prospectively examined at 3T. APTw data were acquired with a 3D snapshot-gradient echo sequence. B0 and B1 inhomogeneities were corrected using the WASAB1 protocol. rCBV was calculated according to established guidelines. Image analysis was performed using Olea Sphere 3.0 software. ΔAPTw and ΔrCBV were calculated as the average signal within the lesion normalized against the average signal in the contralateral white matter. A diagnosis of TP or RN was assessed by histology or imaging at follow-up. Independent samples t-tests of ΔAPTw and ΔrCBV and the areas under the curve (AUCs) were computed. Results: Twenty-one metastases (10 RN, 11 TP) were evaluated. APTw differentiated between RN and TP (U = 120, p < 0.001), in contrast to rCBV (U = 71, p = 0.174). The AUC was 0.991 (95% CI = 0.962–1.020) for ΔAPTw, and 0.636 (95% CI = 0.352–0.921) for ΔrCBV. The optimal cutoff points were 0.4 and 2.1 for ΔAPTw and ΔrCBV, respectively. The sensitivity and specificity for RN-TP were 100% and 90% for ΔAPTw and 63.6% and 36.4% for ΔrCBV. Conclusions: Fluid-suppressed APTw metrics enabled more accurate diagnostic performances than leakage-corrected rCBV metrics in distinguishing between RN and TP. These promising results suggest that APTw imaging could valuably complement current multiparametric MRI protocols in brain metastases follow-ups. Full article

Cerebral blood volume (CBV) is an essential metric that indicates and evaluates various healthy and pathologic conditions. Most methods of CBV measurement are cumbersome and have a poor temporal resolution. Recently, it has been proposed that signals and derived metrics from cerebral near-infrared spectroscopy (NIRS), a non-invasive sensor, can be used to estimate CBV. However, this association remains vastly unexplored. As such, this scoping review aimed to examine the literature on the relationship between cerebral NIRS signals and CBV. A search of six databases was conducted conforming to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to assess the following search question: What are the associations between various NIRS cerebral signals and CBV? The database search yielded 3350 unique results. Seven of these articles were included in this review based on the inclusion and exclusion criteria. An additional study was identified and included while examining the articles’ reference sections. Overall, the literature for this systematic scoping review shows extreme variation in the association between cerebral NIRS signals and CBV, with few sources objectively documenting a true statistical association between the two. This review highlights the current critical knowledge gap and emphasizes the need for further research in the area. Full article

Background: Accurate delineation of lesions in acute ischemic stroke is important for determining the extent of tissue damage and the identification of potentially salvageable brain tissues. Automatic segmentation on CT images is challenging due to the poor contrast-to-noise ratio. Quantitative CT perfusion images improve the estimation of the perfusion deficit regions; however, they are limited by a poor signal-to-noise ratio. The study aims to investigate the potential of deep learning (DL) algorithms for the improved segmentation of ischemic lesions. Methods: This study proposes a novel DL architecture, DenseResU-NetCTPSS, for stroke segmentation using multiparametric CT perfusion images. The proposed network is benchmarked against state-of-the-art DL models. Its performance is assessed using the ISLES-2018 challenge dataset, a widely recognized dataset for stroke segmentation in CT images. The proposed network was evaluated on both training and test datasets. Results: The final optimized network takes three image sequences, namely CT, cerebral blood volume (CBV), and time to max (Tmax), as input to perform segmentation. The network achieved a dice score of 0.65 ± 0.19 and 0.45 ± 0.32 on the training and testing datasets. The model demonstrated a notable improvement over existing state-of-the-art DL models. Conclusions: The optimized model combines CT, CBV, and T_max images, enabling automatic lesion identification with reasonable accuracy and aiding radiologists in faster, more objective assessments. Full article

Background: The accurate and early distinction of glioblastomas (GBMs) from single brain metastases (BMs) provides a window of opportunity for reframing treatment strategies enabling optimal and timely therapeutic interventions. We sought to leverage physiologically sensitive parameters derived from diffusion tensor imaging (DTI) and dynamic susceptibility contrast (DSC)–perfusion-weighted imaging (PWI) along with machine learning-based methods to distinguish GBMs from single BMs. Methods: Patients with histopathology-confirmed GBMs (n = 62) and BMs (n = 26) and exhibiting contrast-enhancing regions (CERs) underwent 3T anatomical imaging, DTI and DSC-PWI prior to treatment. Median values of mean diffusivity (MD), fractional anisotropy, linear, planar and spheric anisotropic coefficients, and relative cerebral blood volume (rCBV) and maximum rCBV values were measured from CERs and immediate peritumor regions. Data normalization and scaling were performed. In the next step, most relevant features were extracted (non-interacting features), which were subsequently used to generate a set of new, innovative, high-order features (interacting features) using a feature engineering method. Finally, 10 machine learning classifiers were employed in distinguishing GBMs and BMs. Cross-validation and receiver operating characteristic (ROC) curve analyses were performed to determine the diagnostic performance. Results: A random forest classifier with ANOVA F-value feature selection algorithm using both interacting and non-interacting features provided the best diagnostic performance in distinguishing GBMs from BMs with an area under the ROC curve of 92.67%, a classification accuracy of 87.8%, a sensitivity of 73.64% and a specificity of 97.5%. Conclusions: A machine learning based approach involving the combined use of interacting and non-interacting physiological MRI parameters shows promise to differentiate between GBMs and BMs with high accuracy. Full article

Pretreatment CT Perfusion (CTP) parameter rCBV < 42% lesion volume has recently been shown to predict 90-day mRS. In this study, we aim to assess the relationship between rCBV < 42% and a radiographic follow-up infarct volume delineated on FLAIR images. In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 9 January 2017 and 10 January 2023. Follow-up FLAIR imaging was used to determine the final infarct volume. Student t, Mann-Whitney-U, and Chi-Square tests were used to assess differences. Spearman’s rank correlation and linear regression analysis were used to assess associations between rCBV < 42% and follow-up infarct volume on FLAIR. In total, 158 patients (median age: 68 years, 52.5% female) met our inclusion criteria. rCBV < 42% (ρ = 0.56, p < 0.001) significantly correlated with follow-up-FLAIR infarct volume. On multivariable linear regression analysis, rCBV < 42% lesion volume (beta = 0.60, p < 0.001), ASPECTS (beta = −0.214, p < 0.01), mTICI (beta = −0.277, p < 0.001), and diabetes (beta = 0.16, p < 0.05) were independently associated with follow-up infarct volume. The rCBV < 42% lesion volume is independently associated with FLAIR follow-up infarct volume. Full article

Purpose: The accuracy of target delineation in radiation treatment planning of high-grade gliomas (HGGs) is crucial to achieve high tumor control, while minimizing treatment-related toxicity. Magnetic resonance imaging (MRI) represents the standard imaging modality for delineation of gliomas with inherent limitations in accurately determining the microscopic extent of tumors. The purpose of this study was to assess the survival impact of multi-observer delineation variability of multiparametric MRI (mpMRI) and [¹⁸F]-FET PET/CT. Materials and Methods: Thirty prospectively included patients with histologically confirmed HGGs underwent a PET/CT and mpMRI including diffusion-weighted imaging (DWI: b0, b1000, ADC), contrast-enhanced T1-weighted imaging (T1-Gado), T2-weighted fluid-attenuated inversion recovery (T2Flair), and perfusion-weighted imaging with computation of relative cerebral blood volume (rCBV) and K2 maps. Nine radiation oncologists delineated the PET/CT and MRI sequences. Spatial similarity (Dice similarity coefficient: DSC) was calculated between the readers for each sequence. Impact of the DSC on progression-free survival (PFS) and overall survival (OS) was assessed using Kaplan–Meier curves and the log-rank test. Results: The highest DSC mean values were reached for morphological sequences, ranging from 0.71 +/− 0.18 to 0.84 +/− 0.09 for T2Flair and T1Gado, respectively, while metabolic volumes defined by PET/CT achieved a mean DSC of 0.75 +/− 0.11. rCBV variability (mean DSC0.32 +/− 0.20) significantly impacted PFS (p = 0.02) and OS (p = 0.002). Conclusions: Our data suggest that the T1-Gado and T2Flair sequences were the most reproducible sequences, followed by PET/CT. Reproducibility for functional sequences was low, but rCBV inter-reader similarity significantly impacted PFS and OS. Full article

Differentiating recurrent cerebral metastasis (CM) from brain radiation necrosis (BRN) is pivotal for guiding appropriate treatment and prognostication. Despite advances in imaging techniques, however, accurately distinguishing these conditions non-invasively is still challenging. This single-center retrospective study reviewed 32 cases (28 patients) with confirmed cerebral metastases who underwent surgical excision of lesions initially diagnosed by MRI and/or MR perfusion scans from 1 January 2015 to 30 September 2020. Diagnostic accuracy was assessed by comparing imaging findings with postoperative histopathology. Conventional MRI accurately identified recurrent CM in 75% of cases. MR perfusion scans showed significantly higher mean maximum relative cerebral blood volume (max. rCBV) in metastasis cases, indicating its potential as a discriminative biomarker. No single imaging modality could definitively distinguish CM from BRN. Survival analysis revealed gender as the only significant factor affecting overall survival, with no significant survival difference observed between patients with CM and BRN after controlling for confounding factors. This study underscores the limitations of both conventional MRI and MR perfusion scans in differentiating recurrent CM from BRN. Histopathological examination remains essential for accurate diagnosis. Further research is needed to improve the reliability of non-invasive imaging and to guide the management of patients with these post-radiation events. Full article

Background: The pretreatment CT perfusion (CTP) marker the relative cerebral blood volume (rCBV) < 42% lesion volume has recently been shown to predict 90-day functional outcomes; however, studies assessing correlations of the rCBV < 42% lesion volume with other outcomes remain sparse. Here, we aim to assess the relationship between the rCBV < 42% lesion volume and the reference standard digital subtraction angiography (DSA)-derived American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN) collateral score, hereby referred as the DSA CS. Methods: In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 1 September 2017 and 1 October 2023. Group differences were assessed using the Student’s t test, Mann–Whitney U test and Chi-Square test. Spearman’s rank correlation and logistic regression analyses were used to assess associations between rCBV < 42% and DSA CS. Results: In total, 222 patients (median age: 69 years, 56.3% female) met our inclusion criteria. In the multivariable logistic regression analysis, taking into account age, sex, race, hypertension, hyperlipidemia, diabetes, atrial fibrillation, prior stroke or transient ischemic attack, the admission National Institute of Health stroke scale, the premorbid modified Rankin score, the Alberta stroke program early CT score (ASPECTS), and segment occlusion, the rCBV < 42% lesion volume (adjusted OR: 0.98, p < 0.05) was independently associated with the DSA CS. Conclusion: The rCBV < 42% lesion volume is independently associated with the DSA CS. Full article

Functional ultrasound (fUS) flow imaging provides a non-invasive method for the in vivo study of cerebral blood flow and neural activity. This study used functional flow imaging to investigate rat brain’s response to ultrasound and colored-light stimuli. Male Long-Evan rats were exposed to direct full-field strobe flashes light and ultrasound stimulation to their retinas, while brain activity was measured using high-frequency ultrasound imaging. Our study found that light stimuli, particularly blue light, elicited strong responses in the visual cortex and lateral geniculate nucleus (LGN), as evidenced by changes in cerebral blood volume (CBV). In contrast, ultrasound stimulation elicited responses undetectable with fUS flow imaging, although these were observable when directly measuring the brain’s electrical signals. These findings suggest that fUS flow imaging can effectively differentiate neural responses to visual stimuli, with potential applications in understanding visual processing and developing new diagnostic tools. Full article