MR image reconstruction using deep density priors

Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction. Leveraging this, recent methods employed DL to learn mappings from undersampled to fully sampled images using paired datasets, including undersampled and corresponding fully sampled images, integrating prior knowledge implicitly. In this article, we propose an alternative approach that learns the probability distribution of fully sampled MR images using unsupervised DL, specifically Variational Autoencoders (VAE), and use this as an explicit prior term in reconstruction, completely decoupling the encoding operation from the prior. The resulting reconstruction algorithm enjoys a powerful image prior to compensate for missing k-space data without requiring paired datasets for training nor being prone to associated sensitivities, such as deviations in undersampling patterns used in training and test time or coil settings. We evaluated the proposed method with T1 weighted images from a publicly available dataset, multi-coil complex images acquired from healthy volunteers (N=8) and images with white matter lesions. The proposed algorithm, using the VAE prior, produced visually high quality reconstructions and achieved low RMSE values, outperforming most of the alternative methods on the same dataset. On multi-coil complex data, the algorithm yielded accurate magnitude and phase reconstruction results. In the experiments on images with white matter lesions, the method faithfully reconstructed the lesions. Keywords: Reconstruction, MRI, prior probability, machine learning, deep learning, unsupervised learning, density estimationComment: Published in IEEE TMI. Main text and supplementary material, 19 pages tota

Visual Feature Attribution using Wasserstein GANs

Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer vision, with applications ranging from weakly supervised localisation to understanding hidden effects in the data. In recent years, approaches based on interpreting a previously trained neural network classifier have become the de facto state-of-the-art and are commonly used on medical as well as natural image datasets. In this paper, we discuss a limitation of these approaches which may lead to only a subset of the category specific features being detected. To address this problem we develop a novel feature attribution technique based on Wasserstein Generative Adversarial Networks (WGAN), which does not suffer from this limitation. We show that our proposed method performs substantially better than the state-of-the-art for visual attribution on a synthetic dataset and on real 3D neuroimaging data from patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). For AD patients the method produces compellingly realistic disease effect maps which are very close to the observed effects.Comment: Accepted to CVPR 201

Cell-Instructive Surface Gradients of Photoresponsive Amyloid-like Fibrils

[Image: see text] Gradients of bioactive molecules play a crucial role in various biological processes like vascularization, tissue regeneration, or cell migration. To study these complex biological systems, it is necessary to control the concentration of bioactive molecules on their substrates. Here, we created a photochemical strategy to generate gradients using amyloid-like fibrils as scaffolds functionalized with a model epitope, that is, the integrin-binding peptide RGD, to modulate cell adhesion. The self-assembling β-sheet forming peptide (CKFKFQF) was connected to the RGD epitope via a photosensitive nitrobenzyl linker and assembled into photoresponsive nanofibrils. The fibrils were spray-coated on glass substrates and macroscopic gradients were generated by UV-light over a centimeter-scale. We confirmed the gradient formation using matrix-assisted laser desorption ionization mass spectroscopy imaging (MALDI-MSI), which directly visualizes the molecular species on the surface. The RGD gradient was used to instruct cells. In consequence, A549 adapted their adhesion properties in dependence of the RGD-epitope density

A methodology for the derivation of unloaded abdominal aortic aneurysm geometry with experimental validation

In this work, we present a novel method for the derivation of the unloaded geometry of an abdominal aortic aneurysm (AAA) from a pressurized geometry in turn obtained by 3D reconstruction of computed tomography (CT) images. The approach was experimentally validated with an aneurysm phantom loaded with gauge pressures of 80, 120, and 140mm Hg. The unloaded phantom geometries estimated from these pressurized states were compared to the actual unloaded phantom geometry, resulting in mean nodal surface distances of up to 3.9% of the maximum aneurysm diameter. An in-silico verification was also performed using a patient-specific AAA mesh, resulting in maximum nodal surface distances of 8 lm after running the algorithm for eight iterations. The methodology was then applied to 12 patient-specific AAA for which their corresponding unloaded geometries were generated in 5-8 iterations. The wall mechanics resulting from finite element analysis of the pressurized (CT image-based) and unloaded geometries were compared to quantify the relative importance of using an unloaded geometry for AAA biomechanics. The pressurized AAA models underestimate peak wall stress (quantified by the first principal stress component) on average by 15% compared to the unloaded AAA models. The validation and application of the method, readily compatible with any finite element solver, underscores the importance of generating the unloaded AAA volume mesh prior to using wall stress as a biomechanical marker for rupture risk assessment

AI-based automated evaluation of image quality and protocol tailoring in patients undergoing MRI for suspected prostate cancer

PURPOSE: To develop and validate an artificial intelligence (AI) application in a clinical setting to decide whether dynamic contrast-enhanced (DCE) sequences are necessary in multiparametric prostate MRI. METHODS: This study was approved by the institutional review board and requirement for study-specific informed consent was waived. A mobile app was developed to integrate AI-based image quality analysis into clinical workflow. An expert radiologist provided reference decisions. Diagnostic performance parameters (sensitivity and specificity) were calculated and inter-reader agreement was evaluated. RESULTS: Fully automated evaluation was possible in 87% of cases, with the application reaching a sensitivity of 80% and a specificity of 100% in selecting patients for multiparametric MRI. In 2% of patients, the application falsely decided on omitting DCE. With a technician reaching a sensitivity of 29% and specificity of 98%, and resident radiologists reaching sensitivity of 29% and specificity of 93%, the use of the application allowed a significant increase in sensitivity. CONCLUSION: The presented AI application accurately decides on a patient-specific MRI protocol based on image quality analysis, potentially allowing omission of DCE in the diagnostic workup of patients with suspected prostate cancer. This could streamline workflow and optimize time utilization of healthcare professionals

Computer‐assisted bone augmentation, implant planning and placement: An in vitro investigation

Aim To assess in vitro the workflow for alveolar ridge augmentation with customised 3D printed block grafts and simultaneous computer-assisted implant planning and placement. Methods Twenty resin mandible models with an edentulous area and horizontal ridge defect in the region 34–36 were scanned with cone beam computed tomography (CBCT). A block graft for horizontal ridge augmentation in the region 34–36 and an implant in the position 35 were digitally planned. Twenty block grafts were 3D printed out of resin and one template for guided implant placement were stereolithographically produced. The resin block grafts were positioned onto the ridge defects and stabilised with two fixation screws each. Subsequently, one implant was inserted in the position 35 through the corresponding template for guided implant placement. Optical scans of the study models together with the fixated block graft were performed prior to and after implant placement. The scans taken after block grafting were superimposed with the virtual block grafting plan through a best-fit algorithm, and the linear deviation between the planned and the achieved block positions was calculated. The precision of the block fixation was obtained by superimposing the 20 scans taken after grafting and calculating the deviation between the corresponding resin blocks. The superimposition between the scans taken after and prior to implant placement was performed to measure a possible displacement in the block position induced by guided implant placement. The (98–2%)/2 percentile value was determined as a parameter for surface deviation. Results The mean deviation in the position of the block graft compared to the virtual plan amounted to 0.79 ± 0.13 mm. The mean deviation between the positions of the 20 block grafts measured 0.47 ± 0.2 mm, indicating a clinically acceptable precision. Guided implant placement induced a mean shift of 0.16 ± 0.06 mm in the position of the block graft. Conclusions Within the limitations of this in vitro study, it can be concluded that customised block grafts fabricated through CBCT, computer-assisted design and 3D printing allow alveolar ridge augmentation with clinically acceptable predictability and reproducibility. Computer-assisted implant planning and placement can be performed simultaneously with computer-assisted block grafting leading to clinically non-relevant dislocation of block grafts