Normal cohorts in automated brain atrophy estimation: how many healthy subjects to include?

OBJECTIVES: This study investigates the influence of normal cohort (NC) size and the impact of different NCs on automated MRI-based brain atrophy estimation. METHODS: A pooled NC of 3945 subjects (NCpool) was retrospectively created from five publicly available cohorts. Voxel-wise gray matter volume atrophy maps were calculated for 48 Alzheimer's disease (AD) patients (55-82 years) using veganbagel and dynamic normal templates with an increasing number of healthy subjects randomly drawn from NCpool (initially three, and finally 100 subjects). Over 100 repeats of the process, the mean over a voxel-wise standard deviation of gray matter z-scores was established and plotted against the number of subjects in the templates. The knee point of these curves was defined as the minimum number of subjects required for consistent brain atrophy estimation. Atrophy maps were calculated using each NC for AD patients and matched healthy controls (HC). Two readers rated the extent of mesiotemporal atrophy to discriminate AD/HC. RESULTS: The maximum knee point was at 15 subjects. For 21 AD/21 HC, a sufficient number of subjects were available in each NC for validation. Readers agreed on the AD diagnosis in all cases (Kappa for the extent of atrophy, 0.98). No differences in diagnoses between NCs were observed (intraclass correlation coefficient, 0.91; Cochran's Q, p = 0.19). CONCLUSION: At least 15 subjects should be included in age- and sex-specific normal templates for consistent brain atrophy estimation. In the study's context, qualitative interpretation of regional atrophy allows reliable AD diagnosis with a high inter-reader agreement, irrespective of the NC used. CLINICAL RELEVANCE STATEMENT: The influence of normal cohorts (NCs) on automated brain atrophy estimation, typically comparing individual scans to NCs, remains largely unexplored. Our study establishes the minimum number of NC-subjects needed and demonstrates minimal impact of different NCs on regional atrophy estimation. KEY POINTS: • Software-based brain atrophy estimation often relies on normal cohorts for comparisons. • At least 15 subjects must be included in an age- and sex-specific normal cohort. • Using different normal cohorts does not influence regional atrophy estimation.

Prediction of cognitive performance differences in older age from multimodal neuroimaging data.

Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55-85 years) from the 1000BRAINS study. Prediction potential was examined for each modality and all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.

Diagnostic Value of Perfusion Parameters for Differentiation of Underlying Etiology in Internal Carotid Artery Occlusions.

PURPOSE: Occlusions of the internal carotid artery (ICA) may be caused by dissection, embolic or macroangiopathic pathogenesis, which partially influences the treatment; however, inferring the underlying etiology in computed tomography angiography can be challenging. In this study, we investigated whether computed tomography perfusion (CT-P) parameters could be used to distinguish between etiologies. METHODS: Patients who received CT­P in acute ischemic stroke due to ICA occlusion between 2012 and 2019 were retrospectively analyzed. Group comparisons between etiologies regarding the ratios of CT­P parameters between both hemispheres for relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), time to maximum (Tmax), and mean transit time (MTT) were calculated by one-factorial analysis of variance (ANOVA) and compared by pairwise Bonferroni post hoc tests. An receiver operating characteristics (ROC) analysis was performed if differences in group comparisons were found. Multinomial logistic regression (MLR) including pretherapeutic parameters was calculated for etiologies. RESULTS: In this study 69 patients (age = 70 ± 14 years, dissection = 10, 14.5%, embolic = 19, 27.5% and macroangiopathic = 40, 58.0%) were included. Group differences in ANOVA were only found for MTT ratio (p = 0.003, η2 = 0.164). In the post hoc test, MTT ratio showed a differentiability between embolic and macroangiopathic occlusions (p = 0.002). ROC analysis for differentiating embolic and macroangiopathic ICA occlusions based on MTT ratio showed an AUC of 0.77 (p < 0.001, CI = 0.65-0.89) and a cut-off was yielded at a value of 1.15 for the MTT ratio (sensitivity 73%, specificity 68%). The MLR showed an overall good model performance. CONCLUSION: It was possible to differentiate between patients with embolic and macroangiopathic ICA occlusions based on MTT ratios and to define a corresponding cut-off. Differentiation from patients with dissection versus the other etiologies was not possible by CT­P parameters in our sample.

Prediction of therapy response of breast cancer patients with machine learning based on clinical data and imaging data derived from breast [18F]FDG-PET/MRI.

PURPOSE: To evaluate if a machine learning prediction model based on clinical and easily assessable imaging features derived from baseline breast [18F]FDG-PET/MRI staging can predict pathologic complete response (pCR) in patients with newly diagnosed breast cancer prior to neoadjuvant system therapy (NAST). METHODS: Altogether 143 women with newly diagnosed breast cancer (54 ± 12 years) were retrospectively enrolled. All women underwent a breast [18F]FDG-PET/MRI, a histopathological workup of their breast cancer lesions and evaluation of clinical data. Fifty-six features derived from positron emission tomography (PET), magnetic resonance imaging (MRI), sociodemographic / anthropometric, histopathologic as well as clinical data were generated and used as input for an extreme Gradient Boosting model (XGBoost) to predict pCR. The model was evaluated in a five-fold nested-cross-validation incorporating independent hyper-parameter tuning within the inner loops to reduce the risk of overoptimistic estimations. Diagnostic model-performance was assessed by determining the area under the curve of the receiver operating characteristics curve (ROC-AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Furthermore, feature importances of the XGBoost model were evaluated to assess which features contributed most to distinguish between pCR and non-pCR. RESULTS: Nested-cross-validation yielded a mean ROC-AUC of 80.4 ± 6.0% for prediction of pCR. Mean sensitivity, specificity, PPV, and NPV of 54.5 ± 21.3%, 83.6 ± 4.2%, 63.6 ± 8.5%, and 77.6 ± 8.1% could be achieved. Histopathological data were the most important features for classification of the XGBoost model followed by PET, MRI, and sociodemographic/anthropometric features. CONCLUSION: The evaluated multi-source XGBoost model shows promising results for reliably predicting pathological complete response in breast cancer patients prior to NAST. However, yielded performance is yet insufficient to be implemented in the clinical decision-making process.

Novel Insights into Pathophysiology of Delayed Cerebral Ischemia: Effects of Current Rescue Therapy on Microvascular Perfusion Heterogeneity.

General microvascular perfusion and its heterogeneity are pathophysiological features of delayed cerebral ischemia (DCI) that are gaining increasing attention. Recently, CT perfusion (CTP) imaging has made it possible to evaluate them radiologically using mean transit time (MTT) and its heterogeneity (measured by cvMTT). This study evaluates the effect of multimodal rescue therapy (intra-arterial nimodipine administration and elevation of blood pressure) on MTT and cvMTT during DCI in aneurysmal subarachnoid haemorrhage (aSAH) patients. A total of seventy-nine aSAH patients who underwent multimodal rescue therapy between May 2012 and December 2019 were retrospectively included in this study. CTP-based perfusion impairment (MTT and cvMTT) on the day of DCI diagnosis was compared with follow-up CTP after initiation of combined multimodal therapy. The mean MTT was significantly reduced in the follow-up CTP compared to the first CTP (3.7 ± 0.7 s vs. 3.3 ± 0.6 s; p < 0.0001). However, no significant reduction of cvMTT was observed (0.16 ± 0.06 vs. 0.15 ± 0.06; p = 0.44). Mean arterial pressure was significantly increased between follow-up and first CTP (98 ± 17 mmHg vs. 104 ± 15 mmHg; p < 0.0001). The combined multimodal rescue therapy was effective in addressing the general microvascular perfusion impairment but did not affect the mechanisms underlying microvascular perfusion heterogeneity. This highlights the need for research into new therapeutic approaches that also target these pathophysiological mechanisms of DCI.

Odynophagia as the first symptom of monkeypox infection.

A 50-year-old patient with confirmed monkeypox infection presented with odynophagia and nocturnal dyspnea. Clinically, there was a lesion on the tongue without any skin lesions and fibrinous plaques on the right tonsil with asymmetry of the palatoglossal arch. Due to a suggested abscess in the CT scan, a tonsillectomy à chaud was performed. By pan-orthopox-specific polymerase chain reaction (PCR) the monkeypox infection was also confirmed in the tonsil tissue. Isolated oral findings may represent a monkeypox infection and should be considered as a currently important differential diagnosis, especially for patients at risks.

Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach.

Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55-85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (MAEs ≥ 0.75) and low to none explained variance (R2 ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.

Evaluation of MTT Heterogeneity of Perfusion CT Imaging in the Early Brain Injury Phase: An Insight into aSAH Pathopysiology.

The concept of early brain injury (EBI) is based on the assumption of a global reduction in brain perfusion following aneurysmal subarachnoid hemorrhage (aSAH). However, the heterogeneity of computed tomography perfusion (CTP) imaging in EBI has not yet been investigated. In contrast, increased mean transit time (MTT) heterogeneity, a possible marker of microvascular perfusion heterogeneity, in the delayed cerebral ischemia (DCI) phase has recently been associated with a poor neurological outcome after aSAH. Therefore, in this study, we investigated whether the heterogeneity of early CTP imaging in the EBI phase is an independent predictor of the neurological outcome after aSAH. We retrospectively analyzed the heterogeneity of the MTT using the coefficient of variation (cvMTT) in early CTP scans (within 24 h after ictus) of 124 aSAH patients. Both linear and logistic regression were used to model the mRS outcome, which were treated as numerical and dichotomized values, respectively. Linear regression was used to investigate the linear dependency between the variables. No significant difference in cvMTT between the patients with and those without EVD could be observed (p = 0.69). We found no correlation between cvMTT in early CTP imaging and initial modified Fisher (p = 0.07) and WFNS grades (p = 0.23). The cvMTT in early perfusion imaging did not correlate significantly with the 6-month mRS for the entire study population (p = 0.15) or for any of the subgroups (without EVD: p = 0.21; with EVD: p = 0.3). In conclusion, microvascular perfusion heterogeneity, assessed by the heterogeneity of MTT in early CTP imaging, does not appear to be an independent predictor of the neurological outcome 6 months after aSAH.

[Odynophagia as the first symptom of monkeypox infection]. / Odynophagie als erstes Symptom bei Affenpocken-Infektion.

A 50-year-old patient with confirmed monkeypox infection presented with odynophagia and nocturnal dyspnea. Clinically, there was a lesion on the tongue without any skin lesions and fibrinous plaques on the right tonsil with asymmetry of the palatoglossal arch. Due to a suggested abscess in the CT scan, a tonsillectomy à chaud was performed. By pan-orthopox-specific polymerase chain reaction (PCR) the monkeypox infection was also confirmed in the tonsil tissue. Isolated oral findings may represent a monkeypox infection and should be considered as a currently important differential diagnosis, especially for patients at risks.

Blood Pressure Affects the Early CT Perfusion Imaging in Patients with aSAH Reflecting Early Disturbed Autoregulation.

BACKGROUND: Early computed tomography perfusion (CTP) is frequently used to predict delayed cerebral ischemia following aneurysmatic subarachnoid hemorrhage (aSAH). However, the influence of blood pressure on CTP is currently controversial (HIMALAIA trial), which differs from our clinical observations. Therefore, we aimed to investigate the influence of blood pressure on early CTP imaging in patients with aSAH. METHODS: We retrospectively analyzed the mean transit time (MTT) of early CTP imaging within 24 h after bleeding prior to aneurysm occlusion with respect to blood pressure shortly before or after the examination in 134 patients. We correlated the cerebral blood flow with the cerebral perfusion pressure in the case of patients with intracranial pressure measurement. We performed a subgroup analysis of good-grade (World Federation of Neurosurgical Societies [WFNS] I-III), poor-grade (WFNS IV-V), and solely WFNS grade V aSAH patients. RESULTS: Mean arterial pressure (MAP) significantly correlated inversely with the mean MTT in early CTP imaging (R = - 0.18, 95% confidence interval [CI] - 0.34 to - 0.01, p = 0.042). Lower mean blood pressure was significantly associated with a higher mean MTT. Subgroup analysis revealed an increasing inverse correlation when comparing WFNS I-III (R = - 0.08, 95% CI - 0.31 to 0.16, p = 0.53) patients with WFNS IV-V (R = - 0.2, 95% CI - 0.42 to 0.05, p = 0.12) patients, without reaching statistical significance. However, if only patients with WFNS V are considered, a significant and even stronger correlation between MAP and MTT (R = - 0.4, 95% CI - 0.65 to 0.07, p = 0.02) is observed. In patients with intracranial pressure monitoring, a stronger dependency of cerebral blood flow on cerebral perfusion pressure is observed for poor-grade patients compared with good-grade patients. CONCLUSIONS: The inverse correlation between MAP and MTT in early CTP imaging, increasing with the severity of aSAH, suggests an increasing disturbance of cerebral autoregulation with the severity of early brain injury. Our results emphasize the importance of maintaining physiological blood pressure values in the early phase of aSAH and preventing hypotension, especially in patients with poor-grade aSAH.

Whole-brain dynamical modelling for classification of Parkinson's disease.

Simulated whole-brain connectomes demonstrate enhanced inter-individual variability depending on the data processing and modelling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinson's disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioural data, such as subject classes, which we refer to as behavioural model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine learning reported in this study also demonstrated that the performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that the temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improved prediction performance. Based on our findings, we suggest that combining the simulation results with empirical data is effective for inter-individual research and its clinical application.

Treatment of Unique Bilateral Distal Fusiform Superior Cerebellar Artery Aneurysms with Mini-Flow Diverter Device Implantation: Case Report.

Currently, surgical revascularization procedures using intracranial-intracranial (IC-IC) or extracranial-intracranial (EC-IC) bypass and distal clipping or trapping are the valid and rescue treatment modality for extremely rare unilateral distal fusiform superior cerebellar artery (SCA) aneurysms. Yet, in case of bilateral fusiform SCA aneurysms, surgical therapy reaches its limit. Mini-flow diverter devices (FDDs) have only recently become available for treating fusiform aneurysms of such small vessels. We report the unique case of bilateral distal fusiform SCA aneurysms in a 43-year-old man with subarachnoid hemorrhage (Fisher grade IV and World Federation of Neurosurgical Societies [WFNS] grade II) treated with endovascular implantation of bilateral mini-FDDs with excellent outcome and no radiographic signs of infarction. Yet, occlusion of one of the FDDs was found in the follow-up, which again shows the eminent danger of occlusion in case of an implantation of FDDs in such small-caliber vessels, which leaves the discussion about the optimal therapy method open.

Mothership vs. drip-and-ship: evaluation of initial treatment strategies for acute ischemic stroke in a well-developed network of specialized hospitals.

PURPOSE: Two strategies of initial patient care exist in endovascular thrombectomy (ET) depending on the site of initial admission: the mothership (MS) and drip-and-ship (DnS) principles. This study compares both strategies in regard to patient outcome in a local network of specialized hospitals. METHODS: Two-hundred-and-two patients undergoing ET in anterior circulation ischemic stroke between June 2016 and May 2018 were enrolled. Ninety two patients were directly admitted to our local facility (MS), One-hundred-and-ten were secondarily referred to our facility. Group comparisons between admission strategies in three-months modified Rankin Scale (mRS), Maas Score and Alberta-Stroke-Program-Early-computed-tomography-score (ASPECTS), National-Institutes-of-Health-Stroke-Scale (NIHSS), age and onset-to-recanalization-time were performed. Correlation between admission strategy and mRS was calculated. A binary logistic regression model was computed including mRS as dependent variable. RESULTS: There were neither significant group differences in three-months mRS between MS and DnS nor significant correlations. Patients tended to achieve a better outcome with DnS. Collateralization status differed between MS and DnS (p = 0.003) with better collateralization in DnS. There were no significant group differences in NIHSS or ASPECTS but in onset-to-recanalization-time (p < 0.001) between MS and DnS. Binary logistic regression showed a high explanation of variance of mRS but no significant results for admission strategy. CONCLUSIONS: Functional outcome in patients treated with ET is comparable between the MS and DnS principles. Tendentially better outcome in the DnS subgroup may be explained by selection bias due to a higher willingness to apply ET in patients with worse baseline conditions (e.g. worse collateralization), if patients undergoing MS are already on site.

Clinical Decision Support for Axillary Lymph Node Staging in Newly Diagnosed Breast Cancer Patients Based on 18F-FDG PET/MRI and Machine Learning.

In addition to its high prognostic value, the involvement of axillary lymph nodes in breast cancer patients also plays an important role in therapy planning. Therefore, an imaging modality that can determine nodal status with high accuracy in patients with primary breast cancer is desirable. Our purpose was to investigate whether, in newly diagnosed breast cancer patients, machine-learning prediction models based on simple assessable imaging features on MRI or PET/MRI are able to determine nodal status with performance comparable to that of experienced radiologists; whether such models can be adjusted to achieve low rates of false-negatives such that invasive procedures might potentially be omitted; and whether a clinical framework for decision support based on simple imaging features can be derived from these models. Methods: Between August 2017 and September 2020, 303 participants from 3 centers prospectively underwent dedicated whole-body 18F-FDG PET/MRI. Imaging datasets were evaluated for axillary lymph node metastases based on morphologic and metabolic features. Predictive models were developed for MRI and PET/MRI separately using random forest classifiers on data from 2 centers and were tested on data from the third center. Results: The diagnostic accuracy for MRI features was 87.5% both for radiologists and for the machine-learning algorithm. For PET/MRI, the diagnostic accuracy was 89.3% for the radiologists and 91.2% for the machine-learning algorithm, with no significant differences in diagnostic performance between radiologists and the machine-learning algorithm for MRI (P = 0.671) or PET/MRI (P = 0.683). The most important lymph node feature was tracer uptake, followed by lymph node size. With an adjusted threshold, a sensitivity of 96.2% was achieved by the random forest classifier, whereas specificity, positive predictive value, negative predictive value, and accuracy were 68.2%, 78.1%, 93.8%, and 83.3%, respectively. A decision tree based on 3 simple imaging features could be established for MRI and PET/MRI. Conclusion: Applying a high-sensitivity threshold to the random forest results might potentially avoid invasive procedures such as sentinel lymph node biopsy in 68.2% of the patients.

Impact of normative brain volume reports on the diagnosis of neurodegenerative dementia disorders in neuroradiology: A real-world, clinical practice study.

Background: Normative brain volume reports (NBVR) are becoming more available in the work-up of patients with suspected dementia disorders, potentially leveraging the value of structural MRI in clinical settings. The present study aims to investigate the impact of NBVRs on the diagnosis of neurodegenerative dementia disorders in real-world clinical practice. Methods: We retrospectively analyzed data of 112 memory clinic patients, who were consecutively referred for MRI and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) during a 12-month period. Structural MRI was assessed by two residents with 2 and 3 years of neuroimaging experience. Statements and diagnostic confidence regarding the presence of a neurodegenerative disorder in general (first level) and Alzheimer's disease (AD) pattern in particular (second level) were recorded without and with NBVR information. FDG-PET served as the reference standard. Results: Overall, despite a trend towards increased accuracy, the impact of NBVRs on diagnostic accuracy was low and non-significant. We found a significant drop of sensitivity (0.75-0.58; p < 0.001) and increase of specificity (0.62-0.85; p < 0.001) for rater 1 at identifying patients with neurodegenerative dementia disorders. Diagnostic confidence increased for rater 2 (p < 0.001). Conclusions: Overall, NBVRs had a limited impact on diagnostic accuracy in real-world clinical practice. Potentially, NBVR might increase diagnostic specificity and confidence of neuroradiology residents. To this end, a well-defined framework for integration of NBVR in the diagnostic process and improved algorithms of NBVR generation are essential.

Impact of defacing on automated brain atrophy estimation.

BACKGROUND: Defacing has become mandatory for anonymization of brain MRI scans; however, concerns regarding data integrity were raised. Thus, we systematically evaluated the effect of different defacing procedures on automated brain atrophy estimation. METHODS: In total, 268 Alzheimer's disease patients were included from ADNI, which included unaccelerated (n = 154), within-session unaccelerated repeat (n = 67) and accelerated 3D T1 imaging (n = 114). Atrophy maps were computed using the open-source software veganbagel for every original, unmodified scan and after defacing using afni_refacer, fsl_deface, mri_deface, mri_reface, PyDeface or spm_deface, and the root-mean-square error (RMSE) between z-scores was calculated. RMSE values derived from unaccelerated and unaccelerated repeat imaging served as a benchmark. Outliers were defined as RMSE > 75th percentile and by using Grubbs's test. RESULTS: Benchmark RMSE was 0.28 ± 0.1 (range 0.12-0.58, 75th percentile 0.33). Outliers were found for unaccelerated and accelerated T1 imaging using the 75th percentile cutoff: afni_refacer (unaccelerated: 18, accelerated: 16), fsl_deface (unaccelerated: 4, accelerated: 18), mri_deface (unaccelerated: 0, accelerated: 15), mri_reface (unaccelerated: 0, accelerated: 2) and spm_deface (unaccelerated: 0, accelerated: 7). PyDeface performed best with no outliers (unaccelerated mean RMSE 0.08 ± 0.05, accelerated mean RMSE 0.07 ± 0.05). The following outliers were found according to Grubbs's test: afni_refacer (unaccelerated: 16, accelerated: 13), fsl_deface (unaccelerated: 10, accelerated: 21), mri_deface (unaccelerated: 7, accelerated: 20), mri_reface (unaccelerated: 7, accelerated: 6), PyDeface (unaccelerated: 5, accelerated: 8) and spm_deface (unaccelerated: 10, accelerated: 12). CONCLUSION: Most defacing approaches have an impact on atrophy estimation, especially in accelerated 3D T1 imaging. Only PyDeface showed good results with negligible impact on atrophy estimation.

Typical doses and typical values for fluoroscopic diagnostic and interventional procedures.

To implement typical doses (TD) and typical values (TV) for fluoroscopic diagnostic and interventional procedures. A total of 3811 fluoroscopic procedures performed within 34 months on three devices were included in this retrospective study. Dose-, patient- and procedure-related information were extracted using the institutional dose management system (DMS). TD/TV were defined as median dose and calculated for the five most frequent procedures per device for dose area product (DAP), cumulative air kerma (CAK) and fluoroscopy time (FT). National diagnostic reference levels and other single facility studies were compared to our results. Additionally, the five procedures with the highest doses of each device were analysed. To evaluate the data coverage of the DMS compared to the picture archiving and communication system (PACS), procedure lists were extracted from the PACS and compared to the procedure information extracted from the DMS. TD/TV for 15 procedures were implemented. Among all devices, TD for DAP ranged between 0.6 Gycm2for port catheter control (n= 64) and 145.9 Gycm2for transarterial chemoembolisation (n= 84). TD for CAK ranged between 5 mGy for port catheter control and 1397 mGy for aneurysm treatment (n= 129) and TV for FT ranged between 0.3 min for upper cavography (n= 67) and 51.4 min for aneurysm treatment. TD for DAP and CAK were lower or within the range of other single facility studies. The five procedures with the highest median DAP per device were identified, 6 of 15 procedures were also found to be among the most frequent procedures. Data coverage of the DMS compared to the PACS ranged between 71% (device 2, stroke treatment) and 78% (device 1, lower limb angiography) for the most common procedure per device. Thus, in 22%-29% of cases dose data of the performed procedure was not transferred into the DMS. We implemented TD/TV for fluoroscopic diagnostic and interventional procedures which enable a comprehensive dose analysis and comparison with previously published values.

Mechanical thrombectomy in stroke patients with acute occlusion of the M1- compared to the M2-segment: Safety, efficacy, and clinical outcome.

PURPOSE: Endovascular treatment (ET) in occlusions of the M1- and proximal M2-segment of the middle cerebral artery (MCA) is an established procedure. In contrast, ET in distal M2-occlusions has not been sufficiently evaluated yet. The purpose of this study was to assess relevant parameters for clinical outcome, efficacy, and safety of patients undergoing ET in M1-, proximal M2-, and distal M2-occlusions. METHODS: One hundred seventy-four patients undergoing ET in acute ischemic stroke with an occlusion of the M1- or M2-segment of the MCA were enrolled prospectively. Non-parametric analysis of variance in 3-month mRS, TICI scale, and complication rates were performed with Kruskal-Wallis test between M1- and proximal and distal M2-occlusions. Subsequent pairwise group comparisons were calculated using Mann-Whitney U-tests. Binary logistic regression models were calculated for each occlusion site. RESULTS: There were no significant group differences in 3-month mRS, mTICI scale, or complication rates between M1- and M2-occlusions nor between proximal and distal M2-occlusions. Binary logistic regression in patients with M1-occlusions showed a substantial explanation of variance (NR2=0.35). NIHSS (p=0.009) and Maas Score as parameter for collateralization (p=0.01) appeared as significant contributing parameters. Binary logistic regression in M2-occlusions showed a high explanation of variance (NR2=0.50) of mRS but no significant factors. CONCLUSIONS: Clinical outcome and procedural safety of patients with M2-occlusions undergoing ET are comparable to those of patients with M1-occlusions. Clinical outcome of patients with M1-occlusions undergoing ET is primarily influenced by the initial neurological deficit and the collateralization of the occlusions. By contrast, clinical outcome in patients with M2-occlusions undergoing ET is more multifactorial.

Within- and across-network alterations of the sensorimotor network in Parkinson's disease.

PURPOSE: Parkinson's disease (PD) is primarily defined by motor symptoms and is associated with alterations of sensorimotor areas. Evidence for network changes of the sensorimotor network (SMN) in PD is inconsistent and a systematic evaluation of SMN in PD yet missing. We investigate functional connectivity changes of the SMN in PD, both, within the network, and to other large-scale connectivity networks. METHODS: Resting-state fMRI was assessed in 38 PD patients under long-term dopaminergic treatment and 43 matched healthy controls (HC). Independent component analysis (ICA) into 20 components was conducted and the SMN was identified within the resulting networks. Functional connectivity within the SMN was analyzed using a dual regression approach. Connectivity between the SMN and the other networks from group ICA was investigated with FSLNets. We investigated for functional connectivity changes between patients and controls as well as between medication states (OFF vs. ON) in PD and for correlations with clinical parameters. RESULTS: There was decreased functional connectivity within the SMN in left inferior parietal and primary somatosensory cortex in PD OFF. Across networks, connectivity between SMN and two motor networks as well as two visual networks was diminished in PD OFF. All connectivity decreases partially normalized in PD ON. CONCLUSION: PD is accompanied by functional connectivity losses of the SMN, both, within the network and in interaction to other networks. The connectivity changes in short- and long-range connections are probably related to impaired sensory integration for motor function in PD. SMN decoupling can be partially compensated by dopaminergic therapy.