(1) Background: Intracranial pressure (ICP) monitoring plays a key role in the treatment of patients in intensive care units, as well as during long-term surgeries and interventions. The gold standard is invasive measurement and monitoring via ventricular drainage or a parenchymal probe. In recent decades, numerous methods for non-invasive measurement have been evaluated but none have become established in routine clinical practice. The aim of this study was to reflect on the current state of research and shed light on relevant techniques for future clinical application. (2) Methods: We performed a PubMed search for “non-invasive AND ICP AND (measurement OR monitoring)” and identified 306 results. On the basis of these search results, we conducted an in-depth source analysis to identify additional methods. Studies were analyzed for design, patient type (e.g., infants, adults, and shunt patients), statistical evaluation (correlation, accuracy, and reliability), number of included measurements, and statistical assessment of accuracy and reliability. (3) Results: MRI-ICP and two-depth Doppler showed the most potential (and were the most complex methods). Tympanic membrane temperature, diffuse correlation spectroscopy, natural resonance frequency, and retinal vein approaches were also promising. (4) Conclusions: To date, no convincing evidence supports the use of a particular method for non-invasive intracranial pressure measurement. However, many new approaches are under development.

Background: Balanced Steady State Free Precession (b-SSFP) sequences and the newly developed Fast-Spin-Echo (FSE)-sequences enable an optimized visualization of neurovascular compression (NVC) in patients with trigeminal neuralgia (TN). Arterial conflicts are mostly associated with a favorable outcome of microvascular decompression (MVD) compared to venous conflicts. An additional Time-of-Flight (TOF) angiography provides the differentiation between offending arteries and veins and a precise counselling of the patient concerning postoperative pain relief. The goal of this study was to analyze the reliability and impact of the combination of highly-resoluted MRI techniques on the correct prediction of the vessel type and the estimation of postoperative outcome of microvascular decompression (MVD). Methods: In total, 48 patients (m/f: 32/16) underwent MVD for TN. All the preoperative imaging data (T2: b-SFFP and FSE, MRA: TOF) were compared to the intraoperative microsurgical findings during MVD. b-SFFP was available in 14 patients, FSE in 34 patients and an additional TOF sequence was available in 38 patients (9 times in combination with b-SSFP, 29 times in combination with FSE). The patients were categorized into four subgroups: 1) NVC negative, 2) venous NVC, 3) arterial NVC, 4) combined arterial and venous NVC. The preoperative MRI findings were compared to the intraoperative morphological findings. Postoperative pain relief was quantified by the Barrow Neurological Institute pain score. Results: Twenty-five purely arterial NVC, 9 purely venous NVC and 5 combined arterial and venous NVC were detected by MRI. In 9 cases NVC was absent on MRI. Overall, the MRI findings correctly predicted the intraoperative findings in 91.7% of the 48 patients. The percentage of correct prediction increased from 80 to 94.7%, when TOF angiography was adjoined. Conclusion: The visualization of the trigeminal nerve using sequences such as b-SSFP or FSE in combination with TOF angiography enables an optimized delineation of arterial and venous neurovascular conflicts and may allow a more reliable differentiation between veins and arteries, resulting in superior prediction of postoperative pain relief compared to T2 imaging data alone.

Background: Dementia with Lewy bodies (DLB) is the second most common dementia type in patients older than 65 years. Its atrophy patterns remain unknown. Its similarities to Parkinson's disease and differences from Alzheimer's disease are subjects of current research. Methods: The aim of our study was (i) to form a group of patients with DLB (and a control group) and create a 3D MRI data set (ii) to volumetrically analyze the entire brain in these groups, (iii) to evaluate visual and manual metric measurements of the innominate substance for real-time diagnosis, and (iv) to compare our groups and results with the latest literature. We identified 102 patients with diagnosed DLB in our psychiatric and neurophysiological archives. After exclusion, 63 patients with valid 3D data sets remained. We compared them with a control group of 25 patients of equal age and sex distribution. We evaluated the atrophy patterns in both (1) manually and (2) via Fast Surfers segmentation and volumetric calculations. Subgroup analyses were done of the CSF data and quality of 3D T1 data sets. Results: Concordant with the literature, we detected moderate, symmetric atrophy of the hippocampus, entorhinal cortex and amygdala, as well as asymmetric atrophy of the right parahippocampal gyrus in DLB. The caudate nucleus was unaffected in patients with DLB, while all the other measured territories were slightly too moderately atrophied. The area under the curve analysis of the left hippocampus volume ratio (< 3646mm³) revealed optimal 76% sensitivity and 100% specificity (followed by the right hippocampus and left amygdala). The substantia innominata’s visual score attained a 51% optimal sensitivity and 84% specificity, and the measured distance 51% optimal sensitivity and 68% specificity in differentiating DLB from our control group. Conclusions: In contrast to other studies, we observed a caudate nucleus sparing atrophy of the whole brain in patients with DLB. As the caudate nucleus is known to be the last survivor in dopamine-uptake, this could be the result of an overstimulation or compensation mechanism deserving further investigation. Its relative hypertrophy compared to all other brain regions could enable an imaging based identification of patients with DLB via automated segmentation and combined volumetric analysis of the hippocampus and amygdala.

Background: Dementia can be caused by numerous different diseases that present variable clinical courses and reveal multiple patterns of brain atrophy, making its accurate early diagnosis by conventional examinative means challenging. Although highly accurate and powerful, magnetic resonance imaging (MRI) currently plays only a supportive role in dementia diagnosis, largely due to the enormous volume and diversity of data it generates. AI-based software solutions/algorithms that can perform automated segmentation and volumetry analyses of MRI data are being increasingly used to address this issue. Numerous commercial and non-commercial software solutions for automated brain segmentation and volumetry exist, with FreeSurfer being the most frequently used.Objectives: This Review is an account of the current situation regarding the application of automated brain segmentation and volumetry to dementia diagnosis.Methods: We performed a PubMed search for “FreeSurfer AND Dementia” and obtained 493 results. Based on these search results, we conducted an in-depth source analysis to identify additional publications, software tools, and methods. Studies were analyzed for design, patient collective, and for statistical evaluation (mathematical methods, correlations).Results: In the studies identified, the main diseases and cohorts represented were Alzheimer’s disease (n = 276), mild cognitive impairment (n = 157), frontotemporal dementia (n = 34), Parkinson’s disease (n = 29), dementia with Lewy bodies (n = 20), and healthy controls (n = 356). The findings and methods of a selection of the studies identified were summarized and discussed.Conclusion: Our evaluation showed that, while a large number of studies and software solutions are available, many diseases are underrepresented in terms of their incidence. There is therefore plenty of scope for targeted research.

Background: Brain metastases are particularly common in patients with small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC showing a less aggressive clinical course and lower chemo- and radio sensitivity compared to SCLC. Early adequate therapy is highly desirable and depends on a reliable classification of tumor type. The apparent diffusion coefficient is a noninvasive neuroimaging marker with the potential to differentiate between major histological subtypes. Here we determine the sensitivity and specificity of the apparent diffusion coefficient to distinguish between NSCLC and SCLC. Methods: We enrolled all NSCLC and SCLC patients diagnosed between 2008 and 2019 at the University Medical Center Göttingen. Cranial MR scans were visually inspected for brain metastases and the ratio of the apparent diffusion coefficient (ADC) was calculated by dividing the ADC measured within the solid part of a metastasis by a reference ADC extracted from an equivalent region in unaffected tissue on the contralateral hemisphere. Results: Out of 411 enrolled patients, we detected 129 patients (83 NSCLC, 46 SCLC) with sufficiently large brain metastases with histologically classified lung cancer and no hemorrhage. We analyzed 185 brain metastases, 84 of SCLC and 101 of NSCLC. SCLC brain metastases showed an ADC ratio of 0.68 ± 0.12 SD, and NSCLC brain metastases showed an ADC ratio of 1.47 ± 0.31 SD. Receiver operating curve statistics differentiated brain metastases of NSCLC from SCLC with an area under the curve of 0.99 and a 95% CI of 0.98 to 1, p < 0.001. Youden's J cut-point is 0.97 at a sensitivity of 0.989 and a specificity of 0.988. Conclusions: In patients with lung cancer and brain metastases with solid tumor parts, ADC ratio enables an ad hoc differentiation of SCLC and NSCLC, easily achieved during routine neuroradiological examination. Non-invasive MR imaging enables an early-individualized management of brain metastases from lung cancer. Trial registration: The study was registered in the German Clinical Trials Register (DRKS00023016).

Background: The aim of this study was to evaluate the reproducibility and clinical value of the novel single-shot T1 mapping method for rapid and accurate multi-slice coverage of the whole brain, described by Wang et al. 2015. Methods: At a field strength of 3 Tesla, T1 mappings of 139 patients (51 of them without pathologic findings) and two repeats of five volunteers were performed at 0.5 mm in-plane resolution. Mean T1 values were determined in 18 manually segmented regions-of-interest without pathologic findings. Reproducibility of the repeated scans was calculated using mean coefficient of variations. Pathologies were grouped and separately evaluated. Results: The mean age of the cohort was 49 (range 1–95 years). T1 relaxation times for ordinary brain and pathologies were in accordance with the literature values. Intra- and inter-subject reproducibility was excellent, and mean coefficient of variations were 2.4% and 3.8%, respectively. Discussion: The novel rapid T1 mapping method is a reliable magnetic resonance imaging technique for identifying and quantifying normal brain structures and may thus serve as a basis for assessing pathologies. The fast and parallel online calculation enables a comfortable use in everyday clinical practice. We see a possible clinical value in a large spectrum of diseases, which should be investigated in further studies.