Purpose: To develop a free-breathing (FB) 2D radial balanced steady-state free precession cine cardiac MRI method with 100% respiratory gating efficiency using respiratory auto-calibrated motion correction (RAMCO) based on a motion-sensing camera. Methods: The signal from a respiratory motion-sensing camera was recorded during a FB retrospectively electrocardiogram triggered 2D radial balanced steady-state free precession acquisition using pseudo–tiny-golden-angle ordering. With RAMCO, for each acquisition the respiratory signal was retrospectively auto-calibrated by applying different linear translations, using the resulting in-plane image sharpness as a criterium. The auto-calibration determines the optimal magnitude of the linear translations for each of the in-plane directions to minimize motion blurring caused by bulk respiratory motion. Additionally, motion-weighted density compensation was applied during radial gridding to minimize through-plane and non-bulk motion blurring. Left ventricular functional parameters and sharpness scores of FB radial cine were compared with and without RAMCO, and additionally with conventional breath-hold Cartesian cine on 9 volunteers. Results: FB radial cine with RAMCO had similar sharpness scores as conventional breath-hold Cartesian cine and the left ventricular functional parameters agreed. For FB radial cine, RAMCO reduced respiratory motion artifacts with a statistically significant difference in sharpness scores (P < 0.05) compared to reconstructions without motion correction. Conclusion: 2D radial cine imaging with RAMCO allows evaluation of left ventricular functional parameters in FB with 100% respiratory efficiency. It eliminates the need for breath-holds, which is especially valuable for patients with no or impaired breath-holding capacity. Validation of the proposed method on patients is warranted.

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Purpose: To develop a new 3D radial trajectory based on the natural spiral phyllotaxis (SP), with variable anisotropic FOV. Theory & Methods: A 3D radial trajectory based on the SP with favorable interleaving properties for cardiac imaging has been proposed by Piccini et al (Magn Reson Med. 2011;66:1049-1056), which supports a FOV with a fixed anisotropy. However, a fixed anisotropy can be inefficient when sampling objects with different anisotropic dimensions. We extend Larson’s 3D radial method to provide variable anisotropic FOV for spiral phyllotaxis (VASP). Simulations were performed to measure distance between successive projections, analyze point spread functions, and compare aliasing artifacts for both VASP and conventional SP. VASP was fully implemented on a whole-body clinical MR scanner. Phantom and in vivo cardiac images were acquired at 1.5 tesla. Results: Simulations, phantom, and in vivo experiments confirmed that VASP can achieve variable anisotropic FOV while maintaining the favorable interleaving properties of SP. For an anisotropic FOV with 100:100:35 ratio, VASP required ~65% fewer radial projections than the conventional SP to satisfy Nyquist criteria. Alternatively, when the same number of radial projections were used as in conventional SP, VASP produced fewer aliasing artifacts for anisotropic objects within the excited imaging volumes. Conclusion: We have developed a new method (VASP), which enables variable anisotropic FOV for 3D radial trajectory with SP. For anisotropic objects within the excited imaging volumes, VASP can reduce scan times and/or reduce aliasing artifacts.

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Prostate cancer is one of the most frequently occurring types of cancer in males. It is often treated with radiation therapy, which aims at irradiating tumors with a high dose, while sparing the surrounding healthy tissues. In the course of the years, radiotherapy technology has undergone great advancements. However, tumors are not only different from each other, they are also highly heterogeneous within, consisting of regions with distinct tissue characteristics, which should be treated with different radiation doses. Tailoring radiotherapy planning to the specific needs and intra-tumor tissue characteristics of each patient is expected to lead to more effective treatment strategies. Currently, clinical research is moving towards this direction, but an understanding of the specific tumor characteristics of each patient, and the integration of all available knowledge into a personalizable radiotherapy planning pipeline are still required. The present work describes solutions from the field of Visual Analytics, which aim at incorporating the information from the distinct steps of the personalizable radiotherapy planning pipeline, along with eventual sources of uncertainty, into comprehensible visualizations. All proposed solutions are meant to increase the – up to now, limited – understanding and exploratory capabilities of clinical researchers. These approaches contribute towards the interactive exploration, visual analysis and understanding of the involved data and processes at different steps of the radiotherapy planning pipeline, creating a fertile ground for future research in radiotherapy planning.@en

Accurate segmentation of brain white matter hyperintensities (WMHs) is important for prognosis and disease monitoring. To this end, classifiers are often trained – usually, using T1 and FLAIR weighted MR images. Incorporating additional features, derived from diffusion weighted MRI, could improve classification. However, the multitude of diffusion-derived features requires selecting the most adequate. For this, automated feature selection is commonly employed, which can often be sub-optimal. In this work, we propose a different approach, introducing a semi-automated pipeline to select interactively features for WMH classification. The advantage of this solution is the integration of the knowledge and skills of experts in the process. In our pipeline, a Visual Analytics (VA) system is employed, to enable user-driven feature selection. The resulting features are T1, FLAIR, Mean Diffusivity (MD), and Radial Diffusivity (RD) – and secondarily, C S  CS and Fractional Anisotropy (FA). The next step in the pipeline is to train a classifier with these features, and compare its results to a similar classifier, used in previous work with automated feature selection. Finally, VA is employed again, to analyze and understand the classifier performance and results.@en

In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the probability that a tumor is effectively treated with a given dose, statistical models were built and employed in clinical research. These are called tumor control probability (TCP) models. Recently, TCP models started incorporating additional information from imaging modalities. In this way, patient-specific properties of tumor tissues are included, improving the radiobiological accuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modeling outcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivity are not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enables clinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledge and to confirm or generate hypotheses within their data. Our approach incorporates the following four main components: (1) It supports the exploration of uncertainty and its effect on TCP models; (2) It facilitates parameter sensitivity analysis to common assumptions; (3) It enables the identification of inter-patient response variability; (4) It allows starting the analysis from the desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinical researchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for the exploration and analysis of TCP modeling. @en

Parallel Coordinate Plots (PCPs) is one of the most powerful techniques for the visualization of multivariate data. However, for large datasets, the representation suffers from clutter due to overplotting. In this case, discerning the underlying data information and selecting specific interesting patterns can become difficult. We propose a new and simple technique to improve the display of PCPs by emphasizing the underlying data structure. Our Orientation-enhanced Parallel Coordinate Plots (OPCPs) improve pattern and outlier discernibility by visually enhancing parts of each PCP polyline with respect to its slope. This enhancement also allows us to introduce a novel and efficient selection method, the Orientation-enhanced Brushing (O-Brushing). Our solution is particularly useful when multiple patterns are present or when the view on certain patterns is obstructed by noise. We present the results of our approach with several synthetic and real-world datasets. Finally, we conducted a user evaluation, which verifies the advantages of the OPCPs in terms of discernibility of information in complex data. It also confirms that O-Brushing eases the selection of data patterns in PCPs and reduces the amount of necessary user interactions compared to state-of-the-art brushing techniques.@en