VirtMRI: A Tool for Teaching MRI.

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.

Fast CBCT Reconstruction using Convolutional Neural Networks for Arbitrary Robotic C-arm Orbits.

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.

Development of an abdominal phantom for the validation of an oligometastatic disease diagnosis workflow.

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.

Feature-based CBCT self-calibration for arbitrary trajectories.

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.

Automated Screening for Abdominal Aortic Aneurysm in CT Scans under Clinical Conditions Using Deep Learning.

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.

Generation of annotated multimodal ground truth datasets for abdominal medical image registration.

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.

Convolutional Neural Network Ensemble Segmentation With Ratio-Based Sampling for the Arteries and Veins in Abdominal CT Scans.

OBJECTIVE: Three-dimensional (3D) blood vessel structure information is important for diagnosis and treatment in various clinical scenarios. We present a fully automatic method for the extraction and differentiation of the arterial and venous vessel trees from abdominal contrast enhanced computed tomography (CE-CT) volumes using convolutional neural networks (CNNs). METHODS: We used a novel ratio-based sampling method to train 2D and 3D versions of the U-Net, the V-Net and the DeepVesselNet. Networks were trained with a combination of the Dice and cross entropy loss. Performance was evaluated on 20 IRCAD subjects. Best performing networks were combined into an ensemble. We investigated seven different weighting schemes. Trained networks were additionally applied to 26 BTCV cases to validate the generalizability. RESULTS: Based on our experiments, the optimal configuration is an equally weighted ensemble of 2D and 3D U- and V-Nets. Our method achieved Dice similarity coefficients of 0.758 ± 0.050 (veins) and 0.838 ± 0.074 (arteries) on the IRCAD data set. Application to the BTCV data set showed a high transfer ability. CONCLUSION: Abdominal vascular structures can be segmented more accurately using ensembles than individual CNNs. 2D and 3D networks have complementary strengths and weaknesses. Our ensemble of 2D and 3D U-Nets and V-Nets in combination with ratio-based sampling achieves a high agreement with manual annotations for both artery and vein segmentation. Our results surpass other state-of-the-art methods. SIGNIFICANCE: Our segmentation pipeline can provide valuable information for the planning of living donor organ transplantations.

Deterministic Arterial Input Function selection in DCE-MRI for automation of quantitative perfusion calculation of colorectal cancer.

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 Ktrans. The AIF found by our algorithm has a lower mean squared error (0.0022 ±â€¯0.0021) in reference to the manual annotation than comparable algorithms. The error of Ktrans (21.52 ±â€¯17.2%) is lower than that of other algorithms. Our algorithm generates reproducible results and thus supports a robust and comparable perfusion analysis.