Abstract:

Purpose 

Development and performance measurement of a fully automated pipeline that localizes and segments the locus coeruleus in so-called neuromelanin-sensitive magnetic resonance imaging data for the derivation of quantitative biomarkers of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease.

Methods

We propose a pipeline composed of several 3D-Unet-based convolutional neural networks for iterative multi-scale localization and multi-rater segmentation and non-deep learning-based components for automated biomarker extraction. We trained on the healthy aging cohort and did not carry out any adaption or fine-tuning prior to the application to Parkinson’s disease subjects.

Results

The localization and segmentation pipeline demonstrated sufficient performance as measured by Euclidean distance (on average around 1.3mm on healthy aging subjects and 2.2mm in Parkinson’s disease subjects) and Dice similarity coefficient (overall around 71%71% on healthy aging subjects and 60%60% for subjects with Parkinson’s disease) as well as promising agreement with respect to contrast ratios in terms of intraclass correlation coefficient of ≥0.80≥0.80 for healthy aging subjects compared to a manual segmentation procedure. Lower values (≥0.48≥0.48) for Parkinson’s disease subjects indicate the need for further investigation and tests before the application to clinical samples.

Conclusion

These promising results suggest the usability of the proposed algorithm for data of healthy aging subjects and pave the way for further investigations using this approach on different clinical datasets to validate its practical usability more conclusively.

Abstract:

Purpose 

Quantitative assessment of prospective motion correction (PMC) capability at 7T MRI for compliant healthy subjects to improve high-resolution images in the absence of intentional motion.

Methods

Twenty-one healthy subjects were imaged at 7 T. They were asked not to move, to consider only unintentional motion. An in-bore optical tracking system was used to monitor head motion and consequently update the imaging volume. For all subjects, high-resolution T1 (3D-MPRAGE), T2 (2D turbo spin echo), proton density (2D turbo spin echo), and  (2D gradient echo) weighted images were acquired with and without PMC. The images were evaluated through subjective and objective analysis.

Results

Subjective evaluation overall has shown a statistically significant improvement (5.5%) in terms of image quality with PMC ON. In a separate evaluation of every contrast, three of the four contrasts (T1, T2, and proton density) have shown a statistically significant improvement (9.62%, 9.85%, and 9.26%), whereas the fourth one () has shown improvement, although not statistically significant. In the evaluation with objective metrics, average edge strength has shown an overall improvement of 6% with PMC ON, which was statistically significant; and gradient entropy has shown an overall improvement of 2%, which did not reach statistical significance.

Conclusion

Based on subjective assessment, PMC improved image quality in high-resolution images of healthy compliant subjects in the absence of intentional motion for all contrasts except , in which no significant differences were observed. Quantitative metrics showed an overall trend for an improvement with PMC, but not all differences were significant.

Abstract:

The identification of interesting patterns and relationships is essential to exploratory data analysis. This becomes increasingly difficult in high dimensional datasets. While dimensionality reduction techniques can be utilized to reduce the analysis space, these may unintentionally bury key dimensions within a larger grouping and obfuscate meaningful patterns. With this work we introduce DimLift, a novel visual analysis method for creating and interacting with dimensional bundles. Generated through an iterative dimensionality reduction or user-driven approach, dimensional bundles are expressive groups of dimensions that contribute similarly to the variance of a dataset. Interactive exploration and reconstruction methods via a layered parallel coordinates plot allow users to lift interesting and subtle relationships to the surface, even in complex scenarios of missing and mixed data types. We exemplify the power of this technique in an expert case study on clinical cohort data alongside two additional case examples from nutrition and ecology.

Abstract:

Increasing complexity and volume of research data pose increasing challenges for scientists to manage their data efficiently. At the same time, availability and reuse of research data are becoming more and more important in modern science. The German government has established an initiative to develop research data management (RDM) and to increase accessibility and reusability of research data at the national level, the Nationale Forschungsdateninfrastruktur (NFDI). The NFDI Neuroscience (NFDI-Neuro) consortium aims to represent the neuroscience community in this initiative. Here, we review the needs and challenges in RDM faced by researchers as well as existing and emerging solutions and benefits, and how the NFDI in general and NFDI-Neuro specifically can support a process for making these solutions better available to researchers. To ensure development of sustainable research data management practices, both technical solutions and engagement of the scientific community are essential. NFDI-Neuro is therefore focusing on community building just as much as on improving the accessibility of technical solutions.

Abstract:

The disease and treatment of patients with head and neck cancer can lead to multiple late and long-term sequelae. Especially pain, psychosocial problems, and voice issues can have a high impact on patients’ health-related quality of life. The aim was to show the feasibility of implementing an electronic Patient-Reported Outcome Measure (PROM) in patients with head and neck cancer (HNC). Driven by our department’s intention to assess Patient-Reported Outcomes (PRO) based on the International Classification of Functioning during tumor aftercare, the program “OncoFunction” has been implemented and continuously refined in everyday practice. The new version of “OncoFunction” was evaluated by 20 head and neck surgeons and radiation oncologists in an interview. From 7/2013 until 7/2017, 846 patients completed the PROM during 2,833 of 3,610 total visits (78.5%). The latest software version implemented newly developed add-ins and increased the already high approval ratings in the evaluation as the number of errors and the time required decreased (6 vs. 0 errors, 1.35 vs. 0.95 min; p<0.01). Notably, patients had different requests using PRO in homecare use. An additional examination shows that only 59% of HNC patients use the world wide web. Using OncoFunction for online-recording and interpretation of PROM improved data acquisition in daily HNC patients’ follow-up. An accessory timeline grants access to former consultations and their visualization supported and simplified structured examinations. This provides an easy-to-use representation of the patient’s functional outcome supporting comprehensive aftercare, considering all aspects of the patient’s life.

Abstract:

Here, we present an extension to our previously published structural ultrahigh resolution T1-weighted magnetic resonance imaging (MRI) dataset with an isotropic resolution of 250 µm, consisting of multiple additional ultrahigh resolution contrasts. Included are up to 150 µm Time-of-Flight angiography, an updated 250 µm structural T1-weighted reconstruction, 330 µm quantitative susceptibility mapping, up to 450 µm structural T2-weighted imaging, 700 µm T1-weighted back-to-back scans, 800 µm diffusion tensor imaging, one hour continuous resting-state functional MRI with an isotropic spatial resolution of 1.8 mm as well as more than 120 other structural T1-weighted volumes together with multiple corresponding proton density weighted acquisitions collected over ten years. All data are from the same participant and were acquired on the same 7 T scanner. The repository contains the unprocessed data as well as (pre-)processing results. The data were acquired in multiple studies with individual goals. This is a unique and comprehensive collection comprising a “human phantom” dataset. Therefore, we compiled, processed, and structured the data, making them publicly available for further investigation.

Abstract:

Abstract:

Purpose 

Publicly available data provision is an essential part of open science. However, open data can conflict with data privacy and data protection regulations. Head scans are particularly vulnerable because the subject’s face can be reconstructed from the acquired images. Although defacing can impede subject identification in reconstructed images, this approach is not applicable to k‐space raw data. To address this challenge and allow defacing of raw data for publication, we present chemical shift–based prospective k‐space anonymization (CHARISMA).

Methods

In spin‐warp imaging, fat shift occurs along the frequency‐encoding direction. By placing an oil‐filled mask onto the subject’s face, the shifted fat signal can overlap with the face to deface k‐space during the acquisition. The CHARISMA approach was tested for gradient‐echo sequences in a single subject wearing the oil‐filled mask at 7 T. Different fat shifts were compared by varying the readout bandwidth. Furthermore, intensity‐based segmentation was used to test whether the images could be unmasked retrospectively.

Results

To impede subject identification after retrospective unmasking, the signal of face and shifted oil should overlap. In this single‐subject study, a shift of 3.3 mm to 4.9 mm resulted in the most efficient masking. Independent of CHARISMA, long TEs induce signal decay and dephasing, which impeded unmasking.

Conclusion

To our best knowledge, CHARISMA is the first prospective k‐space defacing approach. With proper fat‐shift direction and amplitude, this easy‐to‐build, low‐cost solution impaired subject identification in gradient‐echo data considerably. Further sequences will be tested with CHARISMA in the future.

Abstract:

Clinical Decision Support Systems (CDSS) provide assistance to physicians in clinical decision-making. Based on patient-specific evidence items triggering the inferencing process, such as examination findings, and expert-modeled or machine-learned clinical knowledge, these systems provide recommendations in finding the right diagnosis or the optimal therapy. The acceptance of, and the trust in, a CDSS are highly dependent on the transparency of the recommendation’s generation. Physicians must know both the key influences leading to a specific recommendation and the contradictory facts. They must also be aware of the certainty of a recommendation and its potential alternatives.

We present a glyph-based, interactive multiple views approach to explainable computerized clinical decision support. Four linked views (1) provide a visual summary of all evidence items and their relevance for the computation result, (2) present linked textual information, such as clinical guidelines or therapy details, (3) show the certainty of the computation result, which includes the recommendation and a set of clinical scores, stagings etc., and (4) facilitate a guided investigation of the reasoning behind the recommendation generation as well as convey the effect of updated evidence items. We demonstrate our approach for a CDSS based on a causal Bayesian network representing the therapy of laryngeal cancer. The approach has been developed in close collaboration with physicians, and was assessed by six expert otolaryngologists as being tailored to physicians’ needs in understanding a CDSS.

Presentation: https://youtu.be/aW5F6HfX-RI?t=3983

Abstract:

Abstract:

Purpose

Abstract:

Pathological alterations to the locus coeruleus, the major source of noradrenaline in the brain, are histologically evident in early stages of neurodegenerative diseases. Novel MRI approaches now provide an opportunity to quantify structural features of the locus coeruleus in vivo during disease progression. In combination with neuropathological biomarkers, in vivo locus coeruleus imaging could help to understand the contribution of locus coeruleus neurodegeneration to clinical and pathological manifestations in Alzheimer's disease, atypical neurodegenerative dementias and Parkinson's disease. Moreover, as the functional sensitivity of the noradrenergic system is likely to change with disease progression, in vivo measures of locus coeruleus integrity could provide new pathophysiological insights into cognitive and behavioural symptoms. Locus coeruleus imaging also holds the promise to stratify patients into clinical trials according to noradrenergic dysfunction. In this article, we present a consensus on how non-invasive in vivo assessment of locus coeruleus integrity can be used for clinical research in neurodegenerative diseases. We outline the next steps for in vivo, post-mortem and clinical studies that can lay the groundwork to evaluate the potential of locus coeruleus imaging as a biomarker for neurodegenerative disease.

Abstract:

Considerable evidence suggests a close relationship between vascular and degenerative pathology in the human hippocampus. Due to the intrinsic fragility of its vascular network, the hippocampus appears less able to cope with hypoperfusion and anoxia than other cortical areas. Although hippocampal blood supply is generally provided by the collateral branches of the posterior cerebral artery (PCA) and the anterior choroidal artery (AChA), different vascularization patterns have been detected postmortem. To date, a methodology that enables the classification of individual hippocampal vascularization patterns in vivo has not been established. In this study, using high-resolution 7 Tesla time-of-flight angiography data (0.3 mm isotropic resolution) in young adults, we classified individual variability in hippocampal vascularization patterns involved in medial temporal lobe blood supply in vivo. A strong concordance between our classification and previous autopsy findings was found, along with interesting anatomical observations, such as the variable contribution of the AChA to hippocampal supply, the relationships between hippocampal and PCA patterns, and the different distribution patterns of the right and left hemispheres. The approach presented here for determining hippocampal vascularization patterns in vivomay provide new insights into not only the vulnerability of the hippocampus to vascular and neurodegenerative diseases but also hippocampal vascular plasticity after exercise training.

Abstract:

We present a Cerebral Aneurysm Vortex Classification (CAVOCLA) that allows to classify blood flow in cerebral aneurysms. Medical studies assume a strong relation between the progression and rupture of aneurysms and flow patterns. To understand how flow patterns impact the vessel morphology, they are manually classified according to predefined classes. However, manual classifica- tions are time-consuming and exhibit a high inter-observer variability. In contrast, our approach is more objective and faster than manual methods. The classification of integral lines, representing steady or unsteady blood flow, is based on a mapping of the aneurysm surface to a hemisphere by calculating polar-based coordinates. The lines are clustered and for each cluster a representative is calculated. Then, the polar-based coordinates are transformed to the representative as basis for the classification. Classes are based on the flow complexity. The classification results are presented by a detail-on-demand approach using a visual transition from the representative over an enclosing surface to the associated lines. Based on seven representative datasets, we conduct an informal interview with five domain experts to evaluate the system. They confirmed that CAVOCLA allows for a robust classification of intra-aneurysmal flow patterns. The detail-on-demand visualization enables an efficient exploration and interpretation of flow patterns.

Abstract:

Simulations and measurements of blood and airflow inside the human circulatory and respiratory system play an increasingly important role in personalized medicine for prevention, diagnosis and treatment of diseases. This survey focuses on three main application areas. (1) Computational fluid dynamics (CFD) simulations of blood flow in cerebral aneurysms assist in predicting the outcome of this pathologic process and of therapeutic interventions. (2) CFD simulations of nasal airflow allow for investigating the effects of obstructions and deformities and provide therapy decision support. (3) 4D phase-contrast (4D PC) magnetic resoJ. Müller, V. Zebralla, S. Wiegand, and S. Oeltze-Jafra, “Interactive Visual Analysis of Patient-Reported Outcomes for Improved Cancer Aftercare,” EuroVis Workshop on Visual Analytics (EuroVA), p. 5 pages, 2019.nance imaging of aortic haemodynamics supports the diagnosis of various vascular and valve pathologies as well as their treatment. An investigation of the complex and often dynamic simulation and measurement data requires the coupling of sophisticated visualization, interaction and data analysis techniques. In this paper, we survey the large body of work that has been conducted within this realm. We extend previous surveys by incorporating nasal airflow, addressing the joint investigation of blood flow and vessel wall properties and providing a more fine-granular taxonomy of the existing techniques. From the survey, we extract major research trends and identify open problems and future challenges. The survey is intended for researchers interested in medical flow but also more general, in the combined visualization of physiology and anatomy, the extraction of features from flow field data and feature-based visualization, the visual comparison of different simulation results and the interactive visual analysis of the flow field and derived characteristics.