Simulation-informed learning for time-resolved angiographic contrast agent concentration reconstruction.

Three-dimensional Digital Subtraction Angiography (3D-DSA) is a well-established X-ray-based technique for visualizing vascular anatomy. Recently, four-dimensional DSA (4D-DSA) reconstruction algorithms have been developed to enable the visualization of volumetric contrast flow dynamics through time-series of volumes. This reconstruction problem is ill-posed mainly due to vessel overlap in the projection direction and geometric vessel foreshortening, which leads to information loss in the recorded projection images.

Quantitative evaluation of the effects of flow diverter treatment on aneurysm hemodynamics using time-resolved rotational angiography.

Background: There have been immense advancements in the hardware and software of digital subtraction angiography systems over the last several years. These advancements continue to make progress toward the goals of offering better visualization and reducing radiation exposure. A newer advancement in this arena is presenting three-dimension data over time resulting in four-dimensional-digital subtracted angiography visualization.

BOSS: Bones, organs and skin shape model.

A virtual anatomical model of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions. For these tasks, it is highly desirable that the patient's surface and internal organs are of high quality for any pose and shape estimate. At present, the majority of statistical shape models (SSMs) are restricted to a small number of organs or bones or do not adequately represent the general population.

Learning-based occupational x-ray scatter estimation.

During x-ray-guided interventional procedures, the medical staff is exposed to scattered ionizing radiation caused by the patient. To increase the staff's awareness of the invisible radiation and monitor dose online, computational scatter estimation methods are convenient. However, such methods are usually based on Monte Carlo (MC) simulations, which are inherently computationally expensive.

"Virtual stent" - clinical evaluation and user experience of on-the-fly stent simulation in treatment of cerebral aneurysms.

Background: Predicting final stent position can be challenging when treating cerebral aneurysms. Third-Party software proved helpful in selecting proper stents in treatment planning. Recent angiographic systems provide basic stent simulation capabilities integrated in the post-processing software to simulate stent position.

X-Ray Scatter Estimation Using Deep Splines.

X-ray scatter compensation is a very desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based scatter removal approaches yielded promising results. However, as there are no physics' constraints applied to the output of the U-Net, it cannot be ruled out that it yields spurious results.

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images.

In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise linear transformation of a point cloud for the joint tasks of registration and segmentation. The actor network estimates a set of plausible actions and the value network aims to select the optimal action for the current observation.

XDose: toward online cross-validation of experimental and computational X-ray dose estimation.

Purpose: As the spectrum of X-ray procedures has increased both for diagnostic and for interventional cases, more attention is paid to X-ray dose management. While the medical benefit to the patient outweighs the risk of radiation injuries in almost all cases, reproducible studies on organ dose values help to plan preventive measures helping both patient as well as staff. Dose studies are either carried out retrospectively, experimentally using anthropomorphic phantoms, or computationally.

A NanoFE simulation-based surrogate machine learning model to predict mechanical functionality of protein networks from live confocal imaging.

Sub-cellular mechanics plays a crucial role in a variety of biological functions and dysfunctions. Due to the strong structure-function relationship in cytoskeletal protein networks, light can be shed on their mechanical functionality by investigating their structures. Here, we present a data-driven approach employing a combination of confocal live imaging of fluorescent tagged protein networks, in silico mechanical experiments and machine learning to investigate this relationship.

Decoding rejuvenating effects of mechanical loading on skeletal aging using in vivo μCT imaging and deep learning.

Throughout the process of aging, dynamic changes of bone material, micro- and macro-architecture result in a loss of strength and therefore in an increased likelihood of fragility fractures. To date, precise contributions of age-related changes in bone (re)modeling and (de)mineralization dynamics to this fragility increase are not completely understood. Here, we present an image-based deep learning approach to quantitatively describe the effects of short-term aging and adaptive response to cyclic loading applied to proximal mouse tibiae and fibulae.

Effects of Long-Term Sclerostin Deficiency on Trabecular Bone Mass and Adaption to Limb Loading Differ in Male and Female Mice.

A new therapeutic option to treat osteoporosis is focused on Wnt signaling and its inhibitor sclerostin, a product of the Sost gene. In this work, we study the effect of sclerostin deficiency on trabecular bone formation and resorption in male and female mice and whether it affects mechano-responsiveness. Male and female 10- and 26-week-old Sost knockout (KO) and littermate controls (LCs) were subjected to in vivo mechanical loading of the left tibia for 2 weeks.

Physics-driven learning of x-ray skin dose distribution in interventional procedures.

Purpose: Radiation doses accumulated during very complicated image-guided x-ray procedures have the potential to cause stochastic, but also deterministic effects, such as skin rashes or even hair loss. To monitor and reduce radiation-related risks to patients' skin, x-ray imaging devices are equipped with online air kerma monitoring components. Traditionally, such measurements have been used to estimate skin entrance dose by (a) estimating air kerma at the interventional reference point (IRP), (b) forward projecting the dose distribution, and (c) considering a backscatter factor among other correction factors.

Pitfalls in interventional X-ray organ dose assessment-combined experimental and computational phantom study: application to prostatic artery embolization.

Purpose: With X-ray radiation protection and dose management constantly gaining interest in interventional radiology, novel procedures often undergo prospective dose studies using anthropomorphic phantoms to determine expected reference organ-equivalent dose values. Due to inherent uncertainties, such as impact of exact patient positioning, generalized geometry of the phantoms, limited dosimeter positioning options, and composition of tissue-equivalent materials, these dose values might not allow for patient-specific risk assessment. Therefore, first the aim of this study is to quantify the influence of these parameters on local X-ray dose to evaluate their relevance in the assessment of patient-specific organ doses.

A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model.

Purpose: With the recent introduction of fully assisting scanner technologies by Siemens Healthineers (Erlangen, Germany), a patient surface model was introduced to the diagnostic imaging device market. Such a patient representation can be used to automate and accelerate the clinical imaging workflow, manage patient dose, and provide navigation assistance for computed tomography diagnostic imaging. In addition to diagnostic imaging, a patient surface model has also tremendous potential to simplify interventional imaging.

Cytological analysis and structural quantification of FtsZ1-2 and FtsZ2-1 network characteristics in Physcomitrella patens.

Although the concept of the cytoskeleton as a cell-shape-determining scaffold is well established, it remains enigmatic how eukaryotic organelles adopt and maintain a specific morphology. The Filamentous Temperature Sensitive Z (FtsZ) protein family, an ancient tubulin, generates complex polymer networks, with striking similarity to the cytoskeleton, in the chloroplasts of the moss Physcomitrella patens. Certain members of this protein family are essential for structural integrity and shaping of chloroplasts, while others are not, illustrating the functional diversity within the FtsZ protein family.

Computational 3D imaging to quantify structural components and assembly of protein networks.

Unlabelled: Traditionally, protein structures have been described by the secondary structure architecture and fold arrangement. However, the relatively novel method of 3D confocal microscopy of fluorescent-protein-tagged networks in living cells allows resolving the detailed spatial organization of these networks. This provides new possibilities to predict network functionality, as structure and function seem to be linked at various scales.

Sost deficiency led to a greater cortical bone formation response to mechanical loading and altered gene expression.

Bone adaptation optimizes mass and structure, but the mechano-response is already reduced at maturation. Downregulation of sclerostin was believed to be a mandatory step in mechano-adaptation, but in young mice it was shown that load-induced formation can occur independent of sclerostin, a product of the Sost gene. We hypothesized that the bone formation and resorption response to loading is not affected by Sost deficiency, but is age-specific.

Tomography-Based Quantification of Regional Differences in Cortical Bone Surface Remodeling and Mechano-Response.

Bone has an adaptive capacity to maintain structural integrity. However, there seems to be a heterogeneous cortical (re)modeling response to loading at different regions within the same bone, which may lead to inconsistent findings since most studies analyze only one region. It remains unclear if the local mechanical environment is responsible for this heterogeneous response and whether both formation and resorption are affected.

The Periosteal Bone Surface is Less Mechano-Responsive than the Endocortical.

Dynamic processes modify bone micro-structure to adapt to external loading and avoid mechanical failure. Age-related cortical bone loss is thought to occur because of increased endocortical resorption and reduced periosteal formation. Differences in the (re)modeling response to loading on both surfaces, however, are poorly understood.

Aging Leads to a Dysregulation in Mechanically Driven Bone Formation and Resorption.

Physical activity is essential to maintain skeletal mass and structure, but its effect seems to diminish with age. To test the hypothesis that bone becomes less sensitive to mechanical strain with age, we used a combined in vivo/in silico approach. We investigated how maturation and aging influence the mechanical regulation of bone formation and resorption to 2 weeks of noninvasive in vivo controlled loading in mice.

Monitoring in vivo (re)modeling: a computational approach using 4D microCT data to quantify bone surface movements.

Bone undergoes continual damage repair and structural adaptation to changing external loads with the aim of maintaining skeletal integrity throughout life. The ability to monitor bone (re)modeling would allow for a better understanding in how various pathologies and interventions affect bone turnover and subsequent bone strength. To date, however, current methods to monitor bone (re)modeling over time and in space are limited.

Skeletal maturity leads to a reduction in the strain magnitudes induced within the bone: a murine tibia study.

Bone adapts to changes in the local mechanical environment (e.g. strains) through formation and resorption processes.

The influence of age on adaptive bone formation and bone resorption.

Bone is a tissue with enormous adaptive capacity, balancing resorption and formation processes. It is known that mechanical loading shifts this balance towards an increased formation, leading to enhanced bone mass and mechanical performance. What is not known is how this adaptive response to mechanical loading changes with age.

Mineralizing surface is the main target of mechanical stimulation independent of age: 3D dynamic in vivo morphometry.

Mechanical loading can increase cortical bone mass by shifting the balance between bone formation and resorption towards increased formation. With advancing age resorption outpaces formation resulting in a net loss in cortical bone mass. How cortical bone (re)modeling - especially resorption - responds to mechanical loading with aging remains unclear.

Diminished response to in vivo mechanical loading in trabecular and not cortical bone in adulthood of female C57Bl/6 mice coincides with a reduction in deformation to load.

Bone loss occurs during adulthood in both women and men and affects trabecular bone more than cortical bone. The mechanism responsible for trabecular bone loss during adulthood remains unexplained, but may be due at least in part to a reduced mechanoresponsiveness. We hypothesized that trabecular and cortical bone would respond anabolically to loading and that the bone response to mechanical loading would be reduced and the onset delayed in adult compared to postpubescent mice.