Deep intravital brain tumor imaging enabled by tailored three-photon microscopy and analysis.

Intravital 2P-microscopy enables the longitudinal study of brain tumor biology in superficial mouse cortex layers. Intravital microscopy of the white matter, an important route of glioblastoma invasion and recurrence, has not been feasible, due to low signal-to-noise ratios and insufficient spatiotemporal resolution. Here, we present an intravital microscopy and artificial intelligence-based analysis workflow (Deep3P) that enables longitudinal deep imaging of glioblastoma up to a depth of 1.2 mm. We find that perivascular invasion is the preferred invasion route into the corpus callosum and uncover two vascular mechanisms of glioblastoma migration in the white matter. Furthermore, we observe morphological changes after white matter infiltration, a potential basis of an imaging biomarker during early glioblastoma colonization. Taken together, Deep3P allows for a non-invasive intravital investigation of brain tumor biology and its tumor microenvironment at subcortical depths explored, opening up opportunities for studying the neuroscience of brain tumors and other model systems.

T2-relaxometry in a large cohort of hereditary transthyretin amyloidosis with polyneuropathy.

BACKGROUND: Previously, T2-relaxation time (T2app) and proton spin density (ρ) detected nerve injury in a small group of ATTRv amyloidosis. Here, we aim to quantify peripheral nerve impairment in a large cohort of symptomatic and asymptomatic ATTRv amyloidosis and correlate T2-relaxometry markers with clinical parameters and nerve conduction studies (NCS). METHODS: Eighty participants with pathologic variants of the transthyretin gene (TTRv) and 40 controls prospectively underwent magnetic resonance neurography. T2-relaxometry was performed, allowing to calculate tibial ρ, T2app and cross-sectional-area (CSA). Detailed clinical examinations and NCS of tibial and peroneal nerves were performed. RESULTS: Forty participants were classified as asymptomatic TTRv-carriers, 40 as symptomatic patients with polyneuropathy. ρ, T2app and CSA were significantly higher in symptomatic ATTRv amyloidosis (484.2 ± 14.8 a.u.; 70.6 ± 1.8 ms; 25.7 ± 0.9 mm2) versus TTRv-carriers (413.1 ± 9.4 a.u., p < 0.0001; 62.3 ± 1.3 ms, p = 0.0002; 19.0 ± 0.8 mm2, p < 0.0001) and versus controls (362.6 ± 7.5 a.u., p < 0.0001; 59.5 ± 1.0 ms, p < 0.0001; 15.4 ± 0.5 mm2, p < 0.0001). Only ρ and CSA differentiated TTRv-carriers from controls. ρ and CSA correlated with NCS in TTRv-carriers, while T2app correlated with NCS in symptomatic ATTRv amyloidosis. Both ρ and T2app correlated with clinical score. CONCLUSION: ρ and CSA can detect early nerve injury and correlate with electrophysiology in asymptomatic TTRv-carriers. T2app increases only in symptomatic ATTRv amyloidosis in whom it correlates with clinical scores and electrophysiology. Our results suggest that T2-relaxometry can provide biomarkers for disease- and therapy-monitoring in the future.

Shape matters: unsupervised exploration of IDH-wildtype glioma imaging survival predictors.

OBJECTIVES: This study examines clustering based on shape radiomic features and tumor volume to identify IDH-wildtype glioma phenotypes and assess their impact on overall survival (OS). MATERIALS AND METHODS: This retrospective study included 436 consecutive patients diagnosed with IDH-wt glioma who underwent preoperative MR imaging. Alongside the total tumor volume, nine distinct shape radiomic features were extracted using the PyRadiomics framework. Different imaging phenotypes were identified using partition around medoids (PAM) clustering on the training dataset (348/436). The prognostic efficacy of these phenotypes in predicting OS was evaluated on the test dataset (88/436). External validation was performed using the public UCSF glioma dataset (n = 397). A decision-tree algorithm was employed to determine the relevance of features associated with cluster affiliation. RESULTS: PAM clustering identified two clusters in the training dataset: Cluster 1 (n = 233) had a higher proportion of patients with higher sphericity and elongation, while Cluster 2 (n = 115) had a higher proportion of patients with higher maximum 3D diameter, surface area, axis lengths, and tumor volume (p < 0.001 for each). OS differed significantly between clusters: Cluster 1 showed a median OS of 23.8 compared to 11.4 months of Cluster 2 in the holdout test dataset (p = 0.002). Multivariate Cox regression showed improved performance with cluster affiliation over clinical data alone (C index 0.67 vs 0.59, p = 0.003). Cluster-based models outperformed the models with tumor volume alone (evidence ratio: 5.16-5.37). CONCLUSION: Data-driven clustering reveals imaging phenotypes, highlighting the improved prognostic power of combining shape-radiomics with tumor volume, thereby outperforming predictions based on tumor volume alone in high-grade glioma survival outcomes. CLINICAL RELEVANCE STATEMENT: Shape-radiomics and volume-based cluster analyses of preoperative MRI scans can reveal imaging phenotypes that improve the prediction of OS in patients with IDH-wild type gliomas, outperforming currently known models based on tumor size alone or clinical parameters. KEY POINTS: Shape radiomics and tumor volume clustering in IDH-wildtype gliomas are investigated for enhanced prognostic accuracy. Two distinct phenotypic clusters were identified with different median OSs. Integrating shape radiomics and volume-based clustering enhances OS prediction in IDH-wildtype glioma patients.

Assessment of Tumor Cell Invasion and Radiotherapy Response in Experimental Glioma by Magnetic Resonance Elastography.

BACKGROUND: Gliomas are highly invasive brain neoplasms. MRI is the most important tool to diagnose and monitor glioma but has shortcomings. In particular, the assessment of tumor cell invasion is insufficient. This is a clinical dilemma, as recurrence can arise from MRI-occult glioma cell invasion. HYPOTHESIS: Tumor cell invasion, tumor growth and radiotherapy alter the brain parenchymal microstructure and thus are assessable by diffusion tensor imaging (DTI) and MR elastography (MRE). STUDY TYPE: Experimental, animal model. ANIMAL MODEL: Twenty-three male NMRI nude mice orthotopically implanted with S24 patient-derived glioma cells (experimental mice) and 9 NMRI nude mice stereotactically injected with 1 µL PBS (sham-injected mice). FIELD STRENGTH/SEQUENCE: 2D and 3D T2-weighted rapid acquisition with refocused echoes (RARE), 2D echo planar imaging (EPI) DTI, 2D multi-slice multi-echo (MSME) T2 relaxometry, 3D MSME MRE at 900 Hz acquired at 9.4 T (675 mT/m gradient strength). ASSESSMENT: Longitudinal 4-weekly imaging was performed for up to 4 months. Tumor volume was assessed in experimental mice (n = 10 treatment-control, n = 13 radiotherapy). The radiotherapy subgroup and 5 sham-injected mice underwent irradiation (3 × 6 Gy) 9 weeks post-implantation/sham injection. MRI-/MRE-parameters were assessed in the corpus callosum and tumor core/injection tract. Imaging data were correlated to light sheet microscopy (LSM) and histology. STATISTICAL TESTS: Paired and unpaired t-tests, a P-value ≤0.05 was considered significant. RESULTS: From week 4 to 8, a significant callosal stiffening (4.44 ± 0.22 vs. 5.31 ± 0.29 kPa) was detected correlating with LSM-proven tumor cell invasion. This was occult to all other imaging metrics. Histologically proven tissue destruction in the tumor core led to an increased T2 relaxation time (41.65 ± 0.34 vs. 44.83 ± 0.66 msec) and ADC (610.2 ± 12.27 vs. 711.2 ± 13.42 × 10-6 mm2/s) and a softening (5.51 ± 0.30 vs. 4.24 ± 0.29 kPa) from week 8 to 12. Radiotherapy slowed tumor progression. DATA CONCLUSION: MRE is promising for the assessment of key glioma characteristics. EVIDENCE LEVEL: NA TECHNICAL EFFICACY: Stage 2.

Tumor biomechanics as a novel imaging biomarker to assess response to immunotherapy in a murine glioma model.

Glioblastoma is the most common and aggressive primary malignant brain tumor with poor prognosis. Novel immunotherapeutic approaches are currently under investigation. Even though magnetic resonance imaging (MRI) is the most important imaging tool for treatment monitoring, response assessment is often hampered by therapy-related tissue changes. As tumor and therapy-associated tissue reactions differ structurally, we hypothesize that biomechanics could be a pertinent imaging proxy for differentiation. Longitudinal MRI and magnetic resonance elastography (MRE) were performed to monitor response to immunotherapy with a toll-like receptor 7/8 agonist in orthotopic syngeneic experimental glioma. Imaging results were correlated to histology and light sheet microscopy data. Here, we identify MRE as a promising non-invasive imaging method for immunotherapy-monitoring by quantifying changes in response-related tumor mechanics. Specifically, we show that a relative softening of treated compared to untreated tumors is linked to the inflammatory processes following therapy-induced re-education of tumor-associated myeloid cells. Mechanistically, combined effects of myeloid influx and inflammation including extracellular matrix degradation following immunotherapy form the basis of treated tumors being softer than untreated glioma. This is a very early indicator of therapy response outperforming established imaging metrics such as tumor volume. The overall anti-tumor inflammatory processes likely have similar effects on human brain tissue biomechanics, making MRE a promising tool for gauging response to immunotherapy in glioma patients early, thereby strongly impacting patient pathway.

Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities.

BACKGROUND: While the association between diffusion and perfusion magnetic resonance imaging (MRI) and survival in glioblastoma is established, prognostic models for patients are lacking. This study employed clustering of functional imaging to identify distinct functional phenotypes in untreated glioblastomas, assessing their prognostic significance for overall survival. METHODS: A total of 289 patients with glioblastoma who underwent preoperative multimodal MR imaging were included. Mean values of apparent diffusion coefficient normalized relative cerebral blood volume and relative cerebral blood flow were calculated for different tumor compartments and the entire tumor. Distinct imaging patterns were identified using partition around medoids (PAM) clustering on the training dataset, and their ability to predict overall survival was assessed. Additionally, tree-based machine-learning models were trained to ascertain the significance of features pertaining to cluster membership. RESULTS: Using the training dataset (231/289) we identified 2 stable imaging phenotypes through PAM clustering with significantly different overall survival (OS). Validation in an independent test set revealed a high-risk group with a median OS of 10.2 months and a low-risk group with a median OS of 26.6 months (P = 0.012). Patients in the low-risk cluster had high diffusion and low perfusion values throughout, while the high-risk cluster displayed the reverse pattern. Including cluster membership in all multivariate Cox regression analyses improved performance (P ≤ 0.004 each). CONCLUSIONS: Our research demonstrates that data-driven clustering can identify clinically relevant, distinct imaging phenotypes, highlighting the potential role of diffusion, and perfusion MRI in predicting survival rates of glioblastoma patients.

An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids.

Cerebral organoids recapitulate the structure and function of the developing human brain in vitro, offering a large potential for personalized therapeutic strategies. The enormous growth of this research area over the past decade with its capability for clinical translation makes a non-invasive, automated analysis pipeline of organoids highly desirable. This work presents a novel non-invasive approach to monitor and analyze cerebral organoids over time using high-field magnetic resonance imaging and state-of-the-art tools for automated image analysis. Three specific objectives are addressed, (I) organoid segmentation to investigate organoid development over time, (II) global cysticity classification and (III) local cyst segmentation for organoid quality assessment. We show that organoid growth can be monitored reliably over time and cystic and non-cystic organoids can be separated with high accuracy, with on par or better performance compared to state-of-the-art tools applied to brightfield imaging. Local cyst segmentation is feasible but could be further improved in the future. Overall, these results highlight the potential of the pipeline for clinical application to larger-scale comparative organoid analysis.

In vivo nanoparticle-based T cell imaging can predict therapy response towards adoptive T cell therapy in experimental glioma.

Rationale: Intrinsic brain tumors, such as gliomas are largely resistant to immunotherapies including immune checkpoint blockade. Adoptive cell therapies (ACT) including chimeric antigen receptor (CAR) or T cell receptor (TCR)-transgenic T cell therapy targeting glioma-associated antigens are an emerging field in glioma immunotherapy. However, imaging techniques for non-invasive monitoring of adoptively transferred T cells homing to the glioma microenvironment are currently lacking. Methods: Ultrasmall iron oxide nanoparticles (NP) can be visualized non-invasively by magnetic resonance imaging (MRI) and dedicated MRI sequences such as T2* mapping. Here, we develop a protocol for efficient ex vivo labeling of murine and human TCR-transgenic and CAR T cells with iron oxide NPs. We assess labeling efficiency and T cell functionality by flow cytometry and transmission electron microscopy (TEM). NP labeled T cells are visualized by MRI at 9.4 T in vivo after adoptive T cell transfer and correlated with 3D models of cleared brains obtained by light sheet microscopy (LSM). Results: NP are incorporated into T cells in subcellular cytoplasmic vesicles with high labeling efficiency without interfering with T cell viability, proliferation and effector function as assessed by cytokine secretion and antigen-specific killing assays in vitro. We further demonstrate that adoptively transferred T cells can be longitudinally monitored intratumorally by high field MRI at 9.4 Tesla in a murine glioma model with high sensitivity. We find that T cell influx and homogenous spatial distribution of T cells within the TME as assessed by T2* imaging predicts tumor response to ACT whereas incomplete T cell coverage results in treatment resistance. Conclusion: This study showcases a rational for monitoring adoptive T cell therapies non-invasively by iron oxide NP in gliomas to track intratumoral T cell influx and ultimately predict treatment outcome.

Iatrogenic air embolism: influence of air bubble size on cerebral infarctions in an experimental in vivo and numerical simulation model.

BACKGROUND: Cerebral infarctions resulting from iatrogenic air embolism (AE), mainly caused by small air bubbles, are a well-known and often overlooked event in endovascular interventions. Despite their significance, the underlying pathophysiology remains largely unclear. METHODS: In 24 rats, AEs were induced using a microcatheter, positioned in the carotid artery via femoral access. Rats were divided into two study groups, based on the size of the bubbles (85 and 120 µm) and two sub-groups, differing in air volume (0.39 and 0.64 µl). Ultra-high-field magnetic resonance imaging (MRI) was performed 1.5 hours after intervention. MRI findings including the number, single volume and total volume of the infarctions were assessed. A software-based numerical simulation was performed to qualitatively assess the microvascular pathomechanisms. RESULTS: In the study groups 22 of 24 rats (92%) revealed cerebral infarctions. The number of infarctions per rat was higher for the smaller bubbles, for the lower (medians: 5 vs 3; p=0.049) and higher air volume sub-groups (medians: 6 vs 4; p=0.012). Correspondingly, total infarction volume was higher for the smaller bubbles (1.67 vs 0.5 mm³; p=0.042). Simulations confirmed the results of the experiments and suggested that fusion of microbubbles to larger bubbles is the underlying pathomechanism of vascular occlusions. CONCLUSION: In iatrogenic AE, the size of the bubbles can have a major impact on the number and total volume of cerebral infarctions. These findings can help to better understand the pathophysiology of this frequent, often underestimated adverse event in endovascular interventions.

MR microscopy to assess clot composition following mechanical thrombectomy predicts recanalization and clinical outcome.

BACKGROUND: Mechanical thrombectomy (MT) is the standard of care for patients with a stroke and large vessel occlusion. Clot composition is not routinely assessed in clinical practice as no specific diagnostic value is attributed to it, and MT is performed in a standardized 'non-personalized' approach. Whether different clot compositions are associated with intrinsic likelihoods of recanalization success or treatment outcome is unknown. METHODS: We performed a prospective, non-randomized, single-center study and analyzed the clot composition in 60 consecutive patients with ischemic stroke undergoing MT. Clots were assessed by ex vivo multiparametric MRI at 9.4 T (MR microscopy), cone beam CT, and histopathology. Clot imaging was correlated with preinterventional CT and clinical data. RESULTS: MR microscopy showed red blood cell (RBC)-rich (21.7%), platelet-rich (white,38.3%) or mixed clots (40.0%) as distinct morphological entities, and MR microscopy had high accuracy of 95.4% to differentiate clots. Clot composition could be further stratified on preinterventional non-contrast head CT by quantification of the hyperdense artery sign. During MT, white clots required more passes to achieve final recanalization and were not amenable to contact aspiration compared with mixed and RBC-rich clots (maneuvers: 4.7 vs 3.1 and 1.2 passes, P<0.05 and P<0.001, respectively), whereas RBC-rich clots showed higher probability of first pass recanalization (76.9%) compared with white clots (17.4%). White clots were associated with poorer clinical outcome at discharge and 90 days after MT. CONCLUSION: Our study introduces MR microscopy to show that the hyperdense artery sign or MR relaxometry could guide interventional strategy. This could enable a personalized treatment approach to improve outcome of patients undergoing MT.

A Cellular Ground Truth to Develop MRI Signatures in Glioma Models by Correlative Light Sheet Microscopy and Atlas-Based Coregistration.

Glioblastoma is the most common malignant primary brain tumor with poor overall survival. Magnetic resonance imaging (MRI) is the main imaging modality for glioblastoma but has inherent shortcomings. The molecular and cellular basis of MR signals is incompletely understood. We established a ground truth-based image analysis platform to coregister MRI and light sheet microscopy (LSM) data to each other and to an anatomic reference atlas for quantification of 20 predefined anatomic subregions. Our pipeline also includes a segmentation and quantification approach for single myeloid cells in entire LSM datasets. This method was applied to three preclinical glioma models in male and female mice (GL261, U87MG, and S24), which exhibit different key features of the human glioma. Multiparametric MR data including T2-weighted sequences, diffusion tensor imaging, T2 and T2* relaxometry were acquired. Following tissue clearing, LSM focused on the analysis of tumor cell density, microvasculature, and innate immune cell infiltration. Correlated analysis revealed differences in quantitative MRI metrics between the tumor-bearing and the contralateral hemisphere. LSM identified tumor subregions that differed in their MRI characteristics, indicating tumor heterogeneity. Interestingly, MRI signatures, defined as unique combinations of different MRI parameters, differed greatly between the models. The direct correlation of MRI and LSM allows an in-depth characterization of preclinical glioma and can be used to decipher the structural, cellular, and, likely, molecular basis of tumoral MRI biomarkers. Our approach may be applied in other preclinical brain tumor or neurologic disease models, and the derived MRI signatures could ultimately inform image interpretation in a clinical setting.SIGNIFICANCE STATEMENT We established a histologic ground truth-based approach for MR image analyses and tested this method in three preclinical glioma models exhibiting different features of glioblastoma. Coregistration of light sheet microscopy to MRI allowed for an evaluation of quantitative MRI data in histologically distinct tumor subregions. Coregistration to a mouse brain atlas enabled a regional comparison of MRI parameters with a histologically informed interpretation of the results. Our approach is transferable to other preclinical models of brain tumors and further neurologic disorders. The method can be used to decipher the structural, cellular, and molecular basis of MRI signal characteristics. Ultimately, information derived from such analyses could strengthen the neuroradiological evaluation of glioblastoma as they enhance the interpretation of MRI data.

MR-Class: A Python Tool for Brain MR Image Classification Utilizing One-vs-All DCNNs to Deal with the Open-Set Recognition Problem.

BACKGROUND: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. METHODS: Deep convolutional neural networks (DCNN) were trained as one-vs-all classifiers to differentiate between six classes: T1 weighted (w), contrast-enhanced T1w, T2w, T2w-FLAIR, ADC, and SWI. Each classifier yields a probability, allowing threshold-based and relative probability assignment while excluding images with low probability (label: unknown, open-set recognition problem). Data from three high-grade glioma (HGG) cohorts was assessed; C1 (320 patients, 20,101 MRI images) was used for training, while C2 (197, 11,333) and C3 (256, 3522) were for testing. Two raters manually checked images through an interactive labeling tool. Finally, MR-Class' added value was evaluated via radiomics model performance for progression-free survival (PFS) prediction in C2, utilizing the concordance index (C-I). RESULTS: Approximately 10% of annotation errors were observed in each cohort between the DICOM series descriptions and the derived labels. MR-Class accuracy was 96.7% [95% Cl: 95.8, 97.3] for C2 and 94.4% [93.6, 96.1] for C3. A total of 620 images were misclassified; manual assessment of those frequently showed motion artifacts or alterations of anatomy by large tumors. Implementation of MR-Class increased the PFS model C-I by 14.6% on average, compared to a model trained without MR-Class. CONCLUSIONS: We provide a DCNN-based method for the sequence classification of brain MR images and demonstrate its usability in two independent HGG datasets.

MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma.

PURPOSE: This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models' performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). METHODS: MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods' performance. RESULTS: Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13); however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman's rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. CONCLUSION: The IN method impacted the predictive power of survival models; thus, performance is sequence-dependent.

T cell-independent eradication of experimental glioma by intravenous TLR7/8-agonist-loaded nanoparticles.

Glioblastoma, the most common and aggressive primary brain tumor type, is considered an immunologically "cold" tumor with sparse infiltration by adaptive immune cells. Immunosuppressive tumor-associated myeloid cells are drivers of tumor progression. Therefore, targeting and reprogramming intratumoral myeloid cells is an appealing therapeutic strategy. Here, we investigate a ß-cyclodextrin nanoparticle (CDNP) formulation encapsulating the Toll-like receptor 7 and 8 (TLR7/8) agonist R848 (CDNP-R848) to reprogram myeloid cells in the glioma microenvironment. We show that intravenous monotherapy with CDNP-R848 induces regression of established syngeneic experimental glioma, resulting in increased survival rates compared with unloaded CDNP controls. Mechanistically, CDNP-R848 treatment reshapes the immunosuppressive tumor microenvironment and orchestrates tumor clearing by pro-inflammatory tumor-associated myeloid cells, independently of T cells and NK cells. Using serial magnetic resonance imaging, we identify a radiomic signature in response to CDNP-R848 treatment and ultrasmall superparamagnetic iron oxide (USPIO) imaging reveals that immunosuppressive macrophage recruitment is reduced by CDNP-R848. In conclusion, CDNP-R848 induces tumor regression in experimental glioma by targeting blood-borne macrophages without requiring adaptive immunity.

Spatial Distribution and Long-Term Alterations of Peripheral Nerve Lesions in Schwannomatosis.

Purpose To examine the spatial distribution and long-term alterations of peripheral nerve lesions in patients with schwannomatosis by in vivo high-resolution magnetic resonance neurography (MRN). Methods In this prospective study, the lumbosacral plexus as well as the right sciatic, tibial, and peroneal nerves were examined in 15 patients diagnosed with schwannomatosis by a standardized MRN protocol at 3 Tesla. Micro-, intermediate- and macrolesions were assessed according to their number, diameter and spatial distribution. Moreover, in nine patients, peripheral nerve lesions were compared to follow-up examinations after 39 to 71 months. Results In comparison to intermediate and macrolesions, microlesions were the predominant lesion entity at the level of the proximal (p < 0.001), mid- (p < 0.001), and distal thigh (p < 0.01). Compared to the proximal calf level, the lesion number was increased at the proximal (p < 0.05), mid- (p < 0.01), and distal thigh level (p < 0.01), while between the different thigh levels, no differences in lesion numbers were found. In the follow-up examinations, the lesion number was unchanged for micro-, intermediate and macrolesions. The diameter of lesions in the follow-up examination was decreased for microlesions (p < 0.01), not different for intermediate lesions, and increased for macrolesions (p < 0.01). Conclusion Microlesions represent the predominant type of peripheral nerve lesion in schwannomatosis and show a rather consistent distribution pattern in long-term follow-up. In contrast to the accumulation of nerve lesions, primarily in the distal nerve segments in NF2, the lesion numbers in schwannomatosis peak at the mid-thigh level. Towards more distal portions, the lesion number markedly decreases, which is considered as a general feature of other types of small fiber neuropathy.

Brain tumor classification of virtual NMR voxels based on realistic blood vessel-induced spin dephasing using support vector machines.

Remodeling of tissue microvasculature commonly promotes neoplastic growth; however, there is no imaging modality in oncology yet that noninvasively quantifies microvascular changes in clinical routine. Although blood capillaries cannot be resolved in typical magnetic resonance imaging (MRI) measurements, their geometry and distribution influence the integral nuclear magnetic resonance (NMR) signal from each macroscopic MRI voxel. We have numerically simulated the expected transverse relaxation in NMR voxels with different dimensions based on the realistic microvasculature in healthy and tumor-bearing mouse brains (U87 and GL261 glioblastoma). The 3D capillary structure in entire, undissected brains was acquired using light sheet fluorescence microscopy to produce large datasets of the highly resolved cerebrovasculature. Using this data, we trained support vector machines to classify virtual NMR voxels with different dimensions based on the simulated spin dephasing accountable to field inhomogeneities caused by the underlying vasculature. In prediction tests with previously blinded virtual voxels from healthy brain tissue and GL261 tumors, stable classification accuracies above 95% were reached. Our results indicate that high classification accuracies can be stably attained with achievable training set sizes and that larger MRI voxels facilitated increasingly successful classifications, even with small training datasets. We were able to prove that, theoretically, the transverse relaxation process can be harnessed to learn endogenous contrasts for single voxel tissue type classifications on tailored MRI acquisitions. If translatable to experimental MRI, this may augment diagnostic imaging in oncology with automated voxel-by-voxel signal interpretation to detect vascular pathologies.

Dual-Echo Turbo Spin Echo and 12-Echo Multi Spin Echo Sequences as Equivalent Techniques for Obtaining T2-Relaxometry Data: Application in Symptomatic and Asymptomatic Hereditary Transthyretin Amyloidosis as a Surrogate Disease.

OBJECTIVES: Multi spin echo (MSE) sequences are often used for obtaining T2-relaxometry data as they provide defined echo times (TEs). Due to their time-consuming acquisition, they are frequently replaced by turbo spin echo (TSE) sequences that in turn bear the risk of systematic errors when analyzing small structures or lesions. With this study, we aim to test whether T2-relaxometry data derived from either dual-echo TSE or 12-echo MSE sequences are equivalent for quantifying peripheral nerve lesions. Hereditary transthyretin (ATTRv) amyloidosis was chosen as a surrogate disease, as it allows the inclusion of both asymptomatic carriers of the underlying variant transthyretin gene (varTTR) and symptomatic ATTRv amyloidosis patients. MATERIALS AND METHODS: Overall, 50 participants with genetically confirmed varTTR (20 clinically symptomatic ATTRv amyloidosis; 4 females, 16 males; mean age, 61.8 years; range, 33-76 years; and 30 asymptomatic varTTR-carriers; 18 females, 12 males; mean age, 43.1 years; range, 21-62 years), and 30 healthy volunteers (13 females, 17 males, mean age 41.3 years, range 22-73) were prospectively included and underwent magnetic resonance neurography at 3 T. T2-relaxometry was performed by acquiring an axial 2-dimensional dual-echo TSE sequence with spectral fat saturation (TE1/TE2, 12/73 milliseconds; TR, 5210 milliseconds; acquisition time, 7 minutes, 30 seconds), and an axial 2-dimensional MSE sequence with spectral fat saturation and with 12 different TE (TE1, 10 milliseconds to TE12, 120 milliseconds; ΔTE, 10 milliseconds; TR, 3000 milliseconds; acquisition time, 11 minutes, 23 seconds) at the right mid to lower thigh. Sciatic nerve regions of interest were manually drawn in ImageJ on 10 central slices per participant and sequence, and the apparent T2-relaxation time (T2app) and proton spin density (ρ) were calculated individually from TSE and MSE relaxometry data. RESULTS: Linear regression showed that T2app values obtained from the dual-echo TSE (T2appTSE), and those calculated from the 12-echo MSE (T2appMSE) were mathematically connected by a factor of 1.3 throughout all groups (controls: 1.26 ± 0.02; varTTR-carriers: 1.25 ± 0.02; symptomatic ATTRv amyloidosis: 1.28 ± 0.02), whereas a factor of 0.5 was identified between respective ρ values (controls: 0.47 ± 0.01; varTTR-carriers: 0.47 ± 0.01; symptomatic ATTRv amyloidosis: 0.50 ± 0.02). T2app calculated from both TSE and MSE, distinguished between symptomatic ATTRv (T2appTSE 66.38 ± 2.6; T2appMSE 84.6 ± 3.3) and controls (T2appTSE 58.1 ± 1.0, P = 0.0028; T2appMSE 72.8 ± 0.7, P < 0.0001), whereas differences between varTTR-carriers (T2appTSE 61.8 ± 1.5; T2appMSE 76.7 ± 1.3) and ATTRv amyloidosis were observed only for T2appMSE (P = 0.0082). The ρ value differentiated well between healthy controls (ρTSE 365.1 ± 7.2; ρMSE 170.4 ± 3.8) versus varTTR-carriers (ρTSE 415.7 ± 9.8, P = 0.0027; ρMSE 193.7 ± 5.3, P = 0.0398) and versus symptomatic ATTRv amyloidosis (ρTSE 487.8 ± 17.9; ρMSE 244.7 ± 13.1, P < 0.0001, respectively), but also between varTTR-carriers and ATTRv amyloidosis (ρTSEP = 0.0001; ρMSEP < 0.0001). CONCLUSIONS: Dual-echo TSE and 12-echo MSE sequences provide equally robust and reliable T2-relaxometry data when calculating T2app and ρ. Due to their shorter acquisition time and higher resolution, TSE sequences may be preferred in future magnetic resonance imaging protocols. As a secondary result, ρ can be confirmed as a sensitive biomarker to detect early nerve lesions as it differentiated best among healthy controls, asymptomatic varTTR-carriers, and symptomatic ATTRv amyloidosis, whereas T2app might be beneficial in already manifest ATTRv amyloidosis.

Long-term Follow-up and Histological Correlation of Peripheral Nervous System Alterations in Neurofibromatosis Type 2.

PURPOSE: To examine long-term alterations of the dorsal root ganglia (DRG) and the peripheral nerve in patients with neurofibromatosis type 2 (NF2) by in vivo high-resolution magnetic resonance neurography (MRN) and their correlation to histology. METHODS: In this prospective study the lumbosacral DRG, the right sciatic, tibial, and peroneal nerves were examined in 6 patients diagnosed with NF2 and associated polyneuropathy (PNP) by a standardized MRN protocol at 3 T. Volumes of DRG L3-S2 as well as peripheral nerve lesions were assessed and compared to follow-up examinations after 14-100 months. In one patient, imaging findings were further correlated to histology. RESULTS: Follow-up MRN examination showed a non-significant increase of volume for the DRG L3: +0.41% (p = 0.10), L4: +22.41% (p = 0.23), L5: +3.38% (p = 0.09), S1: +10.63% (p = 0.05) and S2: +1.17% (p = 0.57). Likewise, peripheral nerve lesions were not significantly increased regarding size (2.18 mm2 vs. 2.15 mm2, p = 0.89) and number (9.00 vs. 9.33, p = 0.36). Histological analyses identified schwannomas as the major correlate of both DRG hyperplasia and peripheral nerve lesions. For peripheral nerve microlesions additionally clusters of onion-bulb formations were identified. CONCLUSION: Peripheral nervous system alterations seem to be constant or show only a minor increase in adult NF2. Thus, symptoms of PNP may not primarily attributed to the initial schwannoma growth but to secondary long-term processes, with symptoms only occurring if a certain threshold is exceeded. Histology identified grouped areas of Schwann cell proliferations as the correlate of DRG hyperplasia, while for peripheral nerve lesions different patterns could be found.

Dynamics of cerebral perfusion and oxygenation parameters following endovascular treatment of acute ischemic stroke.

BACKGROUND: We studied the effects of endovascular treatment (EVT) and the impact of the extent of recanalization on cerebral perfusion and oxygenation parameters in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO). METHODS: Forty-seven patients with anterior LVO underwent computed tomography perfusion (CTP) before and immediately after EVT. The entire ischemic region (Tmax >6 s) was segmented before intervention, and tissue perfusion (time-to-maximum (Tmax), time-to-peak (TTP), mean transit time (MTT), cerebral blood volume (CBV), cerebral blood flow (CBF)) and oxygenation (coefficient of variation (COV), capillary transit time heterogeneity (CTH), metabolic rate of oxygen (CMRO2), oxygen extraction fraction (OEF)) parameters were quantified from the segmented area at baseline and the corresponding area immediately after intervention, as well as within the ischemic core and penumbra. The impact of the extent of recanalization (modified Treatment in Cerebral Infarction (mTICI)) on CTP parameters was assessed with the Wilcoxon test and Pearson's correlation coefficients. RESULTS: The Tmax, MTT, OEF and CTH values immediately after EVT were lower in patients with complete (as compared with incomplete) recanalization, whereas CBF and COV values were higher (P<0.05) and no differences were found in other parameters. The ischemic penumbra immediately after EVT was lower in patients with complete recanalization as compared with those with incomplete recanalization (P=0.002), whereas no difference was found for the ischemic core (P=0.12). Specifically, higher mTICI scores were associated with a greater reduction of ischemic penumbra volumes (R²=-0.48 (95% CI -0.67 to -0.22), P=0.001) but not of ischemic core volumes (P=0.098). CONCLUSIONS: Our study demonstrates that the ischemic penumbra is the key target of successful EVT in patients with AIS and largely determines its efficacy on a tissue level. Furthermore, we confirm the validity of the mTICI score as a surrogate parameter of interventional success on a tissue perfusion level.

What is the impact of head movement on automated CT perfusion mismatch evaluation in acute ischemic stroke?

OBJECTIVES: Automated CT perfusion mismatch assessment is an established treatment decision tool in acute ischemic stroke. However, the reliability of this method in patients with head motion is unclear. We therefore sought to evaluate the influence of head movement on automated CT perfusion mismatch evaluation. METHODS: Using a realistic CT brain-perfusion-phantom, 7 perfusion mismatch scenarios were simulated within the left middle cerebral artery territory. Real CT noise and artificial head movement were added. Thereafter, ischemic core, penumbra volumes and mismatch ratios were evaluated using an automated mismatch analysis software (RAPID, iSchemaView) and compared with ground truth simulated values. RESULTS: While CT scanner noise alone had only a minor impact on mismatch evaluation, a tendency towards smaller infarct core estimates (mean difference of -5.3 (-14 to 3.5) mL for subtle head movement and -7.0 (-14.7 to 0.7) mL for strong head movement), larger penumbral estimates (+9.9 (-25 to 44) mL and +35 (-14 to 85) mL, respectively) and consequently larger mismatch ratios (+0.8 (-1.5 to 3.0) for subtle head movement and +1.9 (-1.3 to 5.1) for strong head movement) were noted in dependence of patient head movement. CONCLUSIONS: Motion during CT perfusion acquisition influences automated mismatch evaluation. Potentially treatment-relevant changes in mismatch classifications in dependence of head movement were observed and occurred in favor of mechanical thrombectomy.