Real-time biomechanics using the finite element method and machine learning: Review and perspective.

PURPOSE: The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM-based mechanical simulations require considerable time, limiting their use in clinical applications that require real-time responses, such as haptics simulators. Machine learning (ML) approaches have been proposed to help with the reduction of the required time. The present paper reviews cases where ML could help to generate faster simulations, without considerably affecting the performance results. METHODS: This review details the ML approaches used, considering the anatomical structures involved, the data collection strategies, the selected ML algorithms, with corresponding features, the metrics used for validation, and the resulting time gains. RESULTS: A total of 41 references were found. ML algorithms are mainly trained with FEM-based simulations in 32 publications. The preferred ML approach is neural networks, including deep learning in 35 publications. Tissue deformation is simulated in 18 applications, but other features are also considered. The average distance error and mean squared error are the most frequently used performance metrics, in 14 and 17 publications, respectively. The time gains were considerable, going from hours or minutes for purely FEM-based simulations to milliseconds, when using ML. CONCLUSIONS: ML algorithms can be used to accelerate FEM-based biomechanical simulations of anatomical structures, possibly reaching real-time responses. Fast and real-time simulations of anatomical structures, generated with ML algorithms, can help to reduce the time required by FEM-based simulations and accelerate their adoption in the clinical practice.

Segmentation-Based Blood Flow Parameter Refinement in Cerebrovascular Structures Using 4-D Arterial Spin Labeling MRA.

OBJECTIVE: Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Time-resolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and blood flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets. METHODS: A previously published segmentation method, which uses blood flow data to improve its accuracy, is extended to estimate blood flow parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis. RESULTS: The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated blood flow parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined blood flow estimation is more realistic compared to the raw hemodynamic parameters. CONCLUSION: The proposed method can provide accurate segmentations and blood flow parameter estimations in the cerebrovascular system using 4D ASL MRA datasets. SIGNIFICANCE: The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively.

A methodology for generating four-dimensional arterial spin labeling MR angiography virtual phantoms.

Four-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is a non-invasive medical imaging modality that can be used for anatomical and hemodynamic analysis of the cerebrovascular system. However, it generates a considerable amount of data, which is tedious to analyze visually. As an alternative, medical image processing methods can be used to process the data and present measurements of the geometry and blood flow in the cerebrovascular system to the user, such as vessel radius, tortuosity, blood flow volume, and transit time. Nevertheless, evaluating medical image processing methods developed for this modality requires annotated data, which can be time-consuming and expensive to obtain. Alternatively, virtual simulations are a faster and less expensive option that can be used for initial evaluation of image processing methods. The present work proposes a methodology for generating annotated 4D ASL MRA virtual phantoms, in different scenarios with different acquisition parameter settings. In each scenario, the phantoms are generated using real cerebrovascular geometries of healthy volunteers, where blood flow is simulated according to a mathematical model specifically designed to describe the signal observed in 4D ASL MRA images. Realistic noise is added using an homomorphic approach, designed to replicate noise characteristic of multi-coil acquisitions. In order to exemplify the utility of the phantoms, they are used to evaluate the accuracy of a method to estimate blood flow parameter values, such as relative blood volume and transit time, in different scenarios. The estimated values are then compared to its corresponding virtual ground-truth values. The accuracy of the results is ranked according to the average absolute error. The results of the experiments show that blood flow parameters can be more accurately estimated when blood is magnetically labeled for longer periods of time and when the datasets are acquired with higher temporal resolution. In summary, the present work describes a methodology to create annotated virtual phantoms, which represent a useful alternative for initial evaluation of medical image processing methods for 4D ASL MRA images.

Association of Cardiovascular Risk Factors With MRI Indices of Cerebrovascular Structure and Function and White Matter Hyperintensities in Young Adults.

Importance: Risk of stroke and brain atrophy in later life relate to levels of cardiovascular risk in early adulthood. However, it is unknown whether cerebrovascular changes are present in young adults. Objective: To examine relationships between modifiable cardiovascular risk factors and cerebrovascular structure, function, and white matter integrity in young adults. Design, Setting, and Participants: A cross-sectional observational study of 125 young adults (aged 18-40 years) without clinical evidence of cerebrovascular disease. Data collection was completed between August 2014 and May 2016 at the University of Oxford, United Kingdom. Final data collection was completed on May 31, 2016. Exposures: The number of modifiable cardiovascular risk factors at recommended levels, based on the following criteria: body mass index (BMI) <25; highest tertile of cardiovascular fitness and/or physical activity; alcohol consumption <8 drinks/week; nonsmoker for >6 months; blood pressure on awake ambulatory monitoring <130/80 mm Hg; a nonhypertensive diastolic response to exercise (peak diastolic blood pressure <90 mm Hg); total cholesterol <200 mg/dL; and fasting glucose <100mg/dL. Each risk factor at the recommended level was assigned a value of 1, and participants were categorized from 0-8, according to the number of risk factors at recommended levels, with higher numbers indicating healthier risk categories. Main Outcomes and Measures: Cerebral vessel density, caliber and tortuosity, brain white matter hyperintensity lesion count. In a subgroup (n = 52), brain blood arrival time and cerebral blood flow assessed by brain magnetic resonance imaging (MRI). Results: A total of 125 participants, mean (SD) age 25 (5) years, 49% women, with a mean (SD) score of 6.0 (1.4) modifiable cardiovascular risk factors at recommended levels, completed the cardiovascular risk assessment and brain MRI protocol. Cardiovascular risk factors were correlated with cerebrovascular morphology and white matter hyperintensity count in multivariable models. For each additional modifiable risk factor categorized as healthy, vessel density was greater by 0.3 vessels/cm3 (95% CI, 0.1-0.5; P = .003), vessel caliber was greater by 8 µm (95% CI, 3-13; P = .01), and white matter hyperintensity lesions were fewer by 1.6 lesions (95% CI, -3.0 to -0.5; P = .006). Among the 52 participants with available data, cerebral blood flow varied with vessel density and was 2.5 mL/100 g/min higher for each healthier category of a modifiable risk factor (95% CI, 0.16-4.89; P = .03). Conclusions and Relevance: In this preliminary study involving young adults without clinical evidence of cerebrovascular disease, a greater number of modifiable cardiovascular risk factors at recommended levels was associated with higher cerebral vessel density and caliber, higher cerebral blood flow, and fewer white matter hyperintensities. Further research is needed to verify these findings and determine their clinical importance.

Automatic Temporal Segmentation of Vessels of the Brain Using 4D ASL MRA Images.

OBJECTIVE: Automatic vessel segmentation can be used to process the considerable amount of data generated by four-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) images. Previous segmentation approaches for dynamic series of images propose either reducing the series to a temporal average (tAIP) or maximum intensity projection (tMIP) prior to vessel segmentation, or a separate segmentation of each image. This paper introduces a method that combines both approaches to overcome the specific drawbacks of each technique. METHODS: Vessels in the tAIP are enhanced by using the ranking orientation responses of path operators and multiscale vesselness enhancement filters. Then, tAIP segmentation is performed using a seed-based algorithm. In parallel, this algorithm is also used to segment each frame of the series and identify small vessels, which might have been lost in the tAIP segmentation. The results of each individual time frame segmentation are fused using an or boolean operation. Finally, small vessels found only in the fused segmentation are added to the tAIP segmentation. RESULTS: In a quantitative analysis using ten 4D ASL MRA image series from healthy volunteers, the proposed combined approach reached an average Dice coefficient of 0.931, being more accurate than the corresponding tMIP, tAIP, and single time frame segmentation methods with statistical significance. CONCLUSION: The novel combined vessel segmentation strategy can be used to obtain improved vessel segmentation results from 4D ASL MRA and other dynamic series of images. SIGNIFICANCE: Improved vessel segmentation of 4D ASL MRA allows a fast and accurate assessment of cerebrovascular structures.

Comparison of vessel enhancement algorithms applied to time-of-flight MRA images for cerebrovascular segmentation.

PURPOSE: Vessel enhancement algorithms are often used as a preprocessing step for vessel segmentation in medical images to improve the overall segmentation accuracy. Each algorithm uses different characteristics to enhance vessels, such that the most suitable algorithm may vary for different applications. This paper presents a comparative analysis of the accuracy gains in vessel segmentation generated by the use of nine vessel enhancement algorithms: Multiscale vesselness using the formulas described by Erdt (MSE), Frangi (MSF), and Sato (MSS), optimally oriented flux (OOF), ranking orientations responses path operator (RORPO), the regularized Perona-Malik approach (RPM), vessel enhanced diffusion (VED), hybrid diffusion with continuous switch (HDCS), and the white top hat algorithm (WTH). METHODS: The filters were evaluated and compared based on time-of-flight MRA datasets and corresponding manual segmentations from 5 healthy subjects and 10 patients with an arteriovenous malformation. Additionally, five synthetic angiographic datasets with corresponding ground truth segmentation were generated with three different noise levels (low, medium, and high) and also used for comparison. The parameters for each algorithm and subsequent segmentation were optimized using leave-one-out cross evaluation. The Dice coefficient, Matthews correlation coefficient, area under the ROC curve, number of connected components, and true positives were used for comparison. RESULTS: The results of this study suggest that vessel enhancement algorithms do not always lead to more accurate segmentation results compared to segmenting nonenhanced images directly. Multiscale vesselness algorithms, such as MSE, MSF, and MSS proved to be robust to noise, while diffusion-based filters, such as RPM, VED, and HDCS ranked in the top of the list in scenarios with medium or no noise. Filters that assume tubular-shapes, such as MSE, MSF, MSS, OOF, RORPO, and VED show a decrease in accuracy when considering patients with an AVM, because vessels may vary from its tubular-shape in this case. CONCLUSIONS: Vessel enhancement algorithms can help to improve the accuracy of the segmentation of the vascular system. However, their contribution to accuracy has to be evaluated as it depends on the specific applications, and in some cases it can lead to a reduction of the overall accuracy. No specific filter was suitable for all tested scenarios.

Medical image segmentation via atlases and fuzzy object models: Improving efficacy through optimum object search and fewer models.

PURPOSE: Statistical object shape models (SOSMs), known as probabilistic atlases, are popular in medical image segmentation. They register an image into the atlas coordinate system, such that a desired object can be delineated from the constraints of its shape model. While this strategy facilitates segmenting objects with even weak-boundary contrast, it tends to require more models per object to cope with possible registration errors. Fuzzy object shape models (FOSMs) gain substantial speed by avoiding image registration and placing more relaxed model constraints with optimum object search. However, they tend to require stronger object boundary contrast for effective delineation. In this work, the authors show that optimum object search, the essential underpinning of FOSMs, can improve segmentation efficacy of SOSMs with fewer models per object. METHODS: For the sake of efficiency, the authors use three atlases per object (SOSM-3) as baseline for segmentation based on the best match with posterior probability maps. A novel strategy for SOSM with a single atlas and optimum object search (SOSM-S) is presented. When registering an image to the atlas system, one should expect that the object's boundary falls within the uncertainty region of the model-region wherein voxels show probabilities greater than 0 and less than 1 to be in the object. Since registration may fail, SOSM-S translates the atlas locally and, at each location, delineates and scores a candidate object in the uncertainty region. Segmentation is defined by the candidate with the highest score. The presented FOSM also uses a single model per object, but model construction uses only shape translations, building a fuzzy object model with larger uncertainty region. Optimum object search requires estimation of the object's location and/or optimization algorithms to speed-up segmentation. RESULTS: The authors evaluate SOSM-3, SOSM-S, and FOSM on 75 CT-images of the thorax and 35 MR T1-weighted images of the brain, with nine objects of interest. The results show that SOSM-S and FOSM can segment seven out of the nine objects with higher accuracy than SOSM-3, according to the average symmetric surface distance and statistical test. SOSM-S was consistently more accurate than FOSM, FOSM being 2-3 orders of magnitude faster than SOSM-S and SOSM-3 for model construction and hundreds of times faster than them for segmentation. CONCLUSIONS: Although multiple models per object can usually improve segmentation efficacy, the optimum object search has shown to reduce the number of required models. The efficiency gain of FOSM over SOSM-S motivates its use for interactive applications and studies with large image data sets. FOSM and SOSM impose different degrees of shape constraints from the model, making one approach more suitable than the other, depending on contrast. This suggests the use of hybrid models that can take advantage from the strengths of fuzzy and statistical models.