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Magnetic resonance image formation is not trivial and remains a difficult subject for teaching. Therefore, we saw an urgent need to facilitate teaching by developing a practical and easily accessible MR image generator. Due to the increasing interest in X-nuclei MRI, sodium image generation is also offered. The tool is implemented as a web application that is compatible with all standard desktop browsers and is open source. The user interface focuses on the parameters needed for the creation and display of the resulting images. Available MR sequences range from the standard Spin Echo and Inversion Recovery over steady-state to conventional sodium and more advanced single and triple quantum sequences. Additionally, the user interface has parameters to alter the resolution, the noise, and the k-space sampling. Our software is free to use and specifically suited for teaching purposes., (© 2023. The Author(s).)

Purpose: Development of an algorithm to self-calibrate arbitrary CBCT trajectories which can be used to reduce metal artifacts. By using feature detection and matching we want to reduce the amount of parameters for the BFGS optimization and thus reduce the runtime., Methods: Each projection is 2D-3D registered on a prior image with AKAZE feature detection and brute force matching. Translational misalignment is calculated directly from the misalignment of feature positions, rotations are aligned using a minimization algorithm that fits a quartic function and determines the minimum of this function., Evaluation: We did three experiments to compare how well the algorithm can handle noise on the different degrees of freedom. Our algorithms are compared to Broyden-Fletcher-Goldfarb-Shanno (BFGS) minimizer with Normalized Gradient Information (NGI) objective function, and BFGS with distance between features objective function using SSIM, nRMSE, and the Dice coefficient of segmented metal object., Results: Our algorithm (Feature ORiented Calibration for Arbitrary Scan Trajectories with Enhanced Reliability (FORCASTER)) performs on par with the state-of-the-art algorithms (BFGS with NGI objective). nRMSE: FORCASTER = 0.3390, BFGS+NGI = 0.3441; SSIM: FORCASTER = 0.83, BFGS + NGI = 0.79; Dice: FORCASTER = 0.86, BFGS + NGI = 0.87., Conclusion: The proposed algorithm can determine the parameters of the projection orientations for arbitrary trajectories with calibration quality comparable to state-of-the-art algorithms, but faster and with higher tolerance to errors in the initially guessed parameters., (© 2022. The Author(s).)

Purpose: The liver is a common site for metastatic disease, which is a challenging and life-threatening condition with a grim prognosis and outcome. We propose a standardized workflow for the diagnosis of oligometastatic disease (OMD), as a gold standard workflow has not been established yet. The envisioned workflow comprises the acquisition of a multimodal image data set, novel image processing techniques, and cone beam computed tomography (CBCT)-guided biopsy for subsequent molecular subtyping. By combining morphological, molecular, and functional information about the tumor, a patient-specific treatment planning is possible. We designed and manufactured an abdominal liver phantom that we used to demonstrate multimodal image acquisition, image processing, and biopsy of the OMD diagnosis workflow., Methods: The anthropomorphic abdominal phantom contains a rib cage, a portal vein, lungs, a liver with six lesions, and a hepatic vessel tree. This phantom incorporates three different lesion types with varying visibility under computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography CT (PET-CT), which reflects clinical reality. The phantom is puncturable and the size of the corpus and the organs is comparable to those of a real human abdomen. By using several modern additive manufacturing techniques, the manufacturing process is reproducible and allows to incorporate patient-specific anatomies. As a first step of the OMD diagnosis workflow, a preinterventional CT, MRI, and PET-CT data set of the phantom was acquired. The image information was fused using image registration and organ information was extracted via image segmentation. A CBCT-guided needle puncture experiment was performed, where all six liver lesions were punctured with coaxial biopsy needles., Results: Qualitative observation of the image data and quantitative evaluation using contrast-to-noise ratio (CNR) confirms that one lesion type is visible only in MRI and not CT. The other two lesion types are visible in CT and MRI. The CBCT-guided needle placement was performed for all six lesions, including those visible only in MRI and not CBCT. This was possible by successfully merging multimodal preinterventional image data. Lungs, bones, and liver vessels serve as realistic inhibitions during needle path planning., Conclusions: We have developed a reusable abdominal phantom that has been used to validate a standardized OMD diagnosis workflow. Utilizing the phantom, we have been able to show that a multimodal imaging pipeline is advantageous for a comprehensive detection of liver lesions. In a CBCT-guided needle placement experiment we have punctured lesions that are invisible in CBCT using registered preinterventional MRI scans for needle path planning., (© 2022 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Cone-beam CT (CBCT) with non-circular acquisition orbits has the potential to improve image quality, increase the field-of view, and facilitate minimal interference within an interventional imaging setting. Because time is of the essence in interventional imaging scenarios, rapid reconstruction methods are advantageous. Model-Based Iterative Reconstruction (MBIR) techniques implicitly handle arbitrary geometries; however, the computational burden for these approaches is particularly high. The aim of this work is to extend a previously proposed framework for fast reconstruction of non-circular CBCT trajectories. The pipeline combines a deconvolution operation on the backprojected measurements using an approximate, shift-invariant system response prior to processing with a Convolutional Neural Network (CNN). We trained and evaluated the CNN for this approach using 1800 randomized arbitrary orbits. Noisy projection data were formed from 1000 procedurally generated tetrahedral phantoms as well as anthropomorphic data in the form of 800 CT and CBCT images from the Lung Image Database Consortium Image Collection (LIDC). Using this proposed reconstruction pipeline, computation time was reduced by 90% as compared to MBIR with only minor differences in performance. Quantitative comparisons of nRMSE, FSIM and SSIM are reported. Performance was consistent for projection data simulated with acquisition orbits the network has not previously been trained on. These results suggest the potential for fast processing of arbitrary CBCT trajectory data with reconstruction times that are clinically relevant and applicable - facilitating the application of non-circular orbits in CT image-guided interventions and intraoperative imaging.

Abdominal aortic aneurysms (AAA) may remain clinically silent until they enlarge and patients present with a potentially lethal rupture. This necessitates early detection and elective treatment. The goal of this study was to develop an easy-to-train algorithm which is capable of automated AAA screening in CT scans and can be applied to an intra-hospital environment. Three deep convolutional neural networks (ResNet, VGG-16 and AlexNet) were adapted for 3D classification and applied to a dataset consisting of 187 heterogenous CT scans. The 3D ResNet outperformed both other networks. Across the five folds of the first training dataset it achieved an accuracy of 0.856 and an area under the curve (AUC) of 0.926. Subsequently, the algorithms performance was verified on a second data set containing 106 scans, where it ran fully automated and resulted in an accuracy of 0.953 and an AUC of 0.971. A layer-wise relevance propagation (LRP) made the decision process interpretable and showed that the network correctly focused on the aortic lumen. In conclusion, the deep learning-based screening proved to be robust and showed high performance even on a heterogeneous multi-center data set. Integration into hospital workflow and its effect on aneurysm management would be an exciting topic of future research.

Purpose: Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets., Methods: We use a CycleGAN network architecture to generate multimodal synthetic data from the 4D extended cardiac-torso (XCAT) phantom and real patient data. Organ masks are provided by the XCAT phantom; therefore, the generated dataset can serve as ground truth for image segmentation and registration. Realistic simulation of respiration and heartbeat is possible within the XCAT framework. To underline the usability as a registration ground truth, a proof of principle registration is performed., Results: Compared to real patient data, the synthetic data showed good agreement regarding the image voxel intensity distribution and the noise characteristics. The generated T1-weighted magnetic resonance imaging, computed tomography (CT), and cone beam CT images are inherently co-registered. Thus, the synthetic dataset allowed us to optimize registration parameters of a multimodal non-rigid registration, utilizing liver organ masks for evaluation., Conclusion: Our proposed framework provides not only annotated but also multimodal synthetic data which can serve as a ground truth for various tasks in medical imaging processing. We demonstrated the applicability of synthetic data for the development of multimodal medical image registration algorithms., (© 2021. The Author(s).)

Development of a deterministic algorithm for automated detection of the Arterial Input Function (AIF) in DCE-MRI of colorectal cancer. Using a filter pipeline to determine the AIF region of interest. Comparison to algorithms from literature with mean squared error and quantitative perfusion parameter K trans . The AIF found by our algorithm has a lower mean squared error (0.0022 ± 0.0021) in reference to the manual annotation than comparable algorithms. The error of K trans (21.52 ± 17.2%) is lower than that of other algorithms. Our algorithm generates reproducible results and thus supports a robust and comparable perfusion analysis., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Progressive multifocal leukoencephalopathy is a rare but potentially lethal adverse event in natalizumab treated multiple sclerosis patients. We report on a 40-year old Caucasian man with typical relapsing progressive multiple sclerosis, who developed a reversible leukoencephalopathy syndrome after 43 natalizumab infusions mimicking progressive multifocal leukoencephalopathy. To our knowledge, this is the first case of its kind. Our case suggests that awareness ought to be sharpened for reversible leukoencephalopathy syndrome in the follow-up of natalizumab treated multiple sclerosis patients.

Electrophysiological studies found traumatic lesions of the prefrontal cortex (PFC) to affect the preparation of self-initiated movements. However, a precise localization of the observed effects has not been reported yet. Moreover, previous reports did not account for effects of lesion location. Therefore, the present study utilized functional MRI to investigate the influence of predominantly left or right hemispheric PFC lesions on movement preparation in chronic traumatic brain injury (TBI) patients. 18 TBI patients with MRI-defined contusions of the PFC as well as 18 healthy control subjects matched for age and gender were examined. Patients were divided into subgroups of predominantly left or right hemispheric lesions. During functional image acquisition, subjects performed self-initiated abductions of their right index finger. Neuropsychological tests of attention and working memory, which are supposed to modulate preparatory processes, were conducted. Patients with predominantly left contusions demonstrated enhanced activity of the dorsal lateral premotor cortex in comparison to healthy control subjects. In predominantly right lesioned patients, reduced activity within the right caudate head was observed. Compared to predominantly left lesioned patients, neuropsychological tests yielded reduced task performances in the right lesion subgroup. In line with previous electrophysiological research, imaging results of the present study are interpreted to represent altered preparatory networks in chronic prefrontal TBI patients. Since attentional and working memory functions are supposed to modulate preparatory processes, differences between the patient subgroups are suggested to result from the more pronounced cognitive impairments in the right-lesioned group.