Non-invasive fractional flow reserve derived from reduced-order coronary model and machine learning prediction of stenosis flow resistance.

BACKGROUND AND OBJECTIVE: Recently, computational fluid dynamics enables the non-invasive calculation of fractional flow reserve (FFR) based on 3D coronary model, but it is time-consuming. Currently, machine learning technique has emerged as an efficient and reliable approach for prediction, which allows saving a lot of analysis time. This study aimed at developing a simplified FFR prediction model for rapid and accurate assessment of functional significance of stenosis. METHODS: A reduced-order lumped parameter model (LPM) of coronary system and cardiovascular system was constructed for rapidly simulating coronary flow, in which a machine learning model was embedded for accurately predicting stenosis flow resistance at a given flow from anatomical features of stenosis. Importantly, the LPM was personalized in both structures and parameters according to coronary geometries from computed tomography angiography and physiological measurements such as blood pressure and cardiac output for personalized simulations of coronary pressure and flow. Coronary lesions with invasive FFR ≤ 0.80 were defined as hemodynamically significant. RESULTS: A total of 91 patients (93 lesions) who underwent invasive FFR were involved in FFR derived from machine learning (FFRML) calculation. Of the 93 lesions, 27 lesions (29.0%) showed lesion-specific ischemia. The average time of FFRML simulation was about 10 min. On a per-vessel basis, the FFRML and FFR were significantly correlated (r = 0.86, p < 0.001). The diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 91.4%, 92.6%, 90.9%, 80.6% and 96.8%, respectively. The area under the receiver-operating characteristic curve of FFRML was 0.984. CONCLUSION: In this selected cohort of patients, the FFRML improves the computational efficiency and ensures the accuracy. The favorable performance of FFRML approach greatly facilitates its potential application in detecting hemodynamically significant coronary stenosis in future routine clinical practice.

A simplified coronary model for diagnosis of ischemia-causing coronary stenosis.

BACKGROUND AND OBJECTIVE: The functional assessment of the severity of coronary stenosis from coronary computed tomography angiography (CCTA)-derived fractional flow reserve (FFR) has recently attracted interest. However, existing algorithms run at high computational cost. Therefore, this study proposes a fast calculation method of FFR for the diagnosis of ischemia-causing coronary stenosis. METHODS: We combined CCTA and machine learning to develop a simplified single-vessel coronary model for rapid calculation of FFR. First, a zero-dimensional model of single-vessel coronary was established based on CCTA, and microcirculation resistance was determined through the relationship between coronary pressure and flow. In addition, a coronary stenosis model based on machine learning was introduced to determine stenosis resistance. Computational FFR (cFFR) was then obtained by combining the zero-dimensional model and the stenosis model with inlet boundary conditions for resting (cFFRr) and hyperemic (cFFRh) aortic pressure, respectively. We retrospectively analyzed 75 patients who underwent clinically invasive FFR (iFFR), and verified the model accuracy by comparison of cFFR with iFFR. RESULTS: The average computing time of cFFR was less than 2 s. The correlations between cFFRr and cFFRh with iFFR were r = 0.89 (p < 0.001) and r = 0.90 (p < 0.001), respectively. Diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio for cFFRr and cFFRh were 90.7%, 95.0%, 89.1%, 76.0%, 98.0%, 8.7, 0.1 and 92.0%, 95.0%, 90.9%, 79.2%, 98.0%, 10.5, 0.1, respectively. CONCLUSIONS: The proposed model enables rapid prediction of cFFR and exhibits high diagnostic performance in selected patient cohorts. The model thus provides an accurate and time-efficient computational tool to detect ischemia-causing stenosis and assist with clinical decision-making.

Craniocaudal toggling increases the risk of screw loosening in osteoporotic vertebrae.

BACKGROUND AND OBJECTIVE: Screw loosening remains a prominent problem for osteoporotic patients undergoing pedicle screw fixation surgeries but its underlying mechanisms are not fully understood. This study sought to examine the interactive effect of craniocaudal or axial cyclic loading (toggling) and osteoporosis on screw fixation. METHODS: QCT-based finite element models of normal (n = 7; vBMD = 156 ± 13 mg/cm3) and osteoporotic vertebrae (n = 7; vBMD = 72 ± 6 mg/cm3) were inserted with pedicle screws and loaded with or without craniocaudal toggling. Among them, a representative normal vertebra (age: 55; BMD: 140 mg/cm3) and an osteoporotic vertebra (age: 64; BMD: 79 mg/cm3) were also loaded with or without axial toggling. The individual and interactive effects of craniocaudal toggling and osteoporosis on screw fixation strength (the force when the pull-up displacement of the screw head reached 1 mm) and bone tissue failure (characterized by equivalent plastic strain) were examined by repeated measure ANOVA. RESULTS: A significant interactive effect between craniocaudal toggling and osteoporosis on screw fixation strength was detected (p = 0.008). Specifically, craniocaudal toggling led to a marked decrease in the fixation strength (68%, p < 0.05) and stiffness (83%, p < 0.05) only in the osteoporotic vertebrae but had no effect on screw fixation strength and stiffness of the normal vertebrae (p > 0.05). Likewise, most of the bone tissues around the screw in the osteoporotic vertebrae yielded following craniocaudal toggling whereas this result was not seen in the normal vertebrae. The axial toggling had no significant effect on bone tissue failure as well as pedicle screw fixation in normal or osteoporotic vertebrae. CONCLUSIONS: Craniocaudal toggling substantially reduces the screw fixation strength of the osteoporotic vertebrae by progressively increasing tissue failure around the screw, and therefore may contribute to the higher rates of screw loosening in osteoporotic compared to normal patients, whereas axial toggling is not a risk factor for pedicle screw loosening in normal or osteoporotic patients.

Effects of dynamization timing and degree on bone healing of different fracture types.

Dynamization, that is, increasing interfragmentary movement (IFM) by reducing fixation stiffness from a rigid to a more flexible state, has been successfully used in clinical practice to promote fracture healing. However, it remains unclear how dynamization timing and degree affect bone healing of different fracture types. Finite element models of tibial fractures based on the OTA/AO classification (Simple: A1-Spiral, A2-Oblique, A3-Transverse; Wedge: B2-Spiral, B3-Fragmented; Complex: C2-Segment, C3-Irregular), in combination with fuzzy logic-based mechano-regulatory tissue differentiation algorithms, were used to simulate the healing process when dynamization of varied degrees (dynamization coefficient or DC = 0-0.9; 0.9 represents 90% reduction in the fixation stiffness relative to a rigid fixation) were applied at different time points after fracture. The fuzzy logic-based algorithms have been validated with a preclinical animal model. The results showed that the healing responses of type A fractures were more sensitive to the changes in dynamization degree and timing comparing with type B or C fractures. Additionally, the optimal dynamization regime for each fracture type was different. For type A fractures, a moderate dynamization degree (e.g., DC = 0.5) applied after Week 1 promoted the recovery of biomechanical integrity. For type B and C fractures, the effective dynamization included a greater dynamization degree (DC = 0.7) applied after Week 2. Our results further demonstrated that the fracture morphology affected interfragmentary strain environments within the callus, leading to varied healing results for different fracture types. These results suggest that the effects of dynamization are highly dependent of the fracture types. Therefore, specific dynamization strategies should be chosen for different fracture types to achieve optimal healing outcomes.

Biomechanical CT-computed bone strength predicts the risk of subsequent vertebral fracture.

Following primary fractures and percutaneous kyphoplasty (PKP), patients have a high risk of incurring a subsequent vertebral fracture (SVF). Given that SVF is a consequence of mechanical deterioration of the vertebra, we sought to examine whether vertebral strength derived from QCT-based finite element analysis (i.e., BCT) can predict the risk of SVF. Sixty-six patients who underwent PKP were categorized into two groups: control or non-SVF group (age: 70 ± 7 years; n = 40) and SVF group (age: 69 ± 8 years; n = 26). BCT was performed on L4 or L3 vertebrae to noninvasively measure vertebral strength. Vertebral strength was also estimated based upon the geometry and material properties of the vertebra. Additionally, trabecular volumetric bone mineral density (vBMD) and L1 Hounsfield unit (HU) were measured. t-Test, χ2 test or Mann Whitney U test were used to compare differences in these parameters between the two groups. The predictive abilities of BCT strength and other measured parameters were evaluated using the receiver operating characteristic (ROC) analysis. Results showed no significant difference in either vBMD or L1 HU between the control and SVF groups (p > 0.05), whereas BCT-computed and estimated vertebral strength values were significantly reduced by 33 % and 24 % for the SVF group relative to the non-SVF group, respectively. ROC curve indicated that BCT strength had the largest area under the curve, compared to other parameters. These results suggest that BCT-computed vertebral strength may serve as a surrogate for assessing risk of SVF.

Effect of the accordion technique on bone regeneration during distraction osteogenesis: A computational study.

BACKGROUND AND OBJECTIVE: Distraction osteogenesis (DO), a bone lengthening technique, is widely employed to treat congenital and acquired limb length discrepancies and large segmental bone defects. However, a major issue of DO is the prolonged consolidation phase (10-36 months) during which patients must wear a cumbersome external fixator. Attempts have been made to accelerate the healing process of DO by an alternating distraction and compression mode (so-called "accordion" technique or AT). However, it remains unclear how varied AT parameters affect DO outcomes and what the most effective AT mode is. METHODS: Based on an experimentally-verified mechanobiological model, we performed a parametric analysis via in silico simulation of the bone regeneration process of DO under different AT modes, including combinations of varied application times (AT began at week 1-8 of the consolidation phase), durations (AT was used continuously for 1 week, 2 weeks or 4 weeks) and rates (distraction or compression at 0.25, 0.5, 0.75, and 1 mm/12 h). The control group had no AT applied during the consolidation phase. RESULTS: Compared with the control group (no AT), AT applied at an early consolidation stage (e.g. week 1 of the consolidation phase) significantly enhanced bone formation and reduced the overall healing time. However, the effect of AT on bone healing was dependent on its duration and rate. Specifically, a moderate rate of AT (e.g. 0.5 mm/12 h) lasting for two weeks promoted blood perfusion recovery and bone regeneration, ultimately shortening the healing time. Conversely, over-high rates (e.g. 1 mm/12 h) and longer durations (e.g. 4 weeks) of AT adversely affected bone regeneration and blood perfusion recovery, thereby delaying bone bridging. CONCLUSIONS: These results suggest that the therapeutic effects of AT on DO are highly dependent of the AT parameters of choice. Under appropriate durations and rates, the AT applied at an early consolidation phase is beneficial for blood recovery and bone regeneration. These results may provide a basis for selecting effective AT modes to accelerate consolidation and reduce the overall treatment period of DO.

Mechanical testing and biomechanical CT analysis to assess vertebral flexion strength of Chinese cadavers.

Biomechanical CT (BCT), i.e., quantitative computed tomography-based finite element analysis (QCT-FEA), promises an improved technique over bone mineral density (BMD) in predicting bone strength and the risk of osteoporotic vertebral fractures. However, most of the BCT models only consider a uniform compressive loading condition and they have not been validated for Chinese subjects. This study examined the ability of BCT to predict wedge fracture-related vertebral flexion strength in a cohort of Chinese cadaveric vertebrae. Twelve human vertebrae were scanned with dual energy X-ray absorptiometry (DXA) and QCT to measure areal and volumetric BMD, respectively. To produce wedge fractures, the cadaveric vertebrae were experimentally loaded until failure under a 15° flexion. Vertebral flexion stiffness and strength were measured from the force-displacement curve. Voxel-based heterogeneous FE models of the vertebrae were created and virtually tested in uniform compression and 15° flexion to compute compressive and flexion strength (and stiffness), respectively. The predictions of vertebral flexion strength with BMD or BCT measures were evaluated with linear regression analyses. Results showed weak correlations between experimentally-measured flexion strength vs. DXA-aBMD (R2 = 0.26) or QCT-vBMD (R2 = 0.39). However, there were strong correlations between experimentally-measured flexion strength vs. BCT-computed vertebral strength under either flexion (R2 = 0.71) or compression (R2 = 0.70) loading conditions, although flexion reduced the BCT-computed vertebral strength by 9.2%. These results suggest that, regardless of whether a uniform compression or a flexion loading is simulated, BCT can predict in vitro vertebral flexion strength better than BMD.

In vivo and in silico monitoring bone regeneration during distraction osteogenesis of the mouse femur.

BACKGROUND AND OBJECTIVE: Distraction osteogenesis (DO) is a mechanobiological process of producing new bone by gradual and controlled distraction of the surgically separated bone segments. Mice have been increasingly used to study the role of relevant biological factors in regulating bone regeneration during DO. However, there remains a lack of in silico DO models and related mechano-regulatory tissue differentiation algorithms for mouse bone. This study sought to establish an in silico model based on in vivo experimental data to simulate the bone regeneration process during DO of the mouse femur. METHODS: In vivo micro-CT, including time-lapse morphometry was performed to monitor the bone regeneration in the distraction gap. A 2D axisymmetric finite element model, with a geometry originating from the experimental data, was created. Bone regeneration was simulated with a fuzzy logic-based two-stage (distraction and consolidation) mechano-regulatory tissue differentiation algorithm, which was adjusted from that used for fracture healing based on our in vivo experimental data. The predictive potential of the model was further tested with varied distraction frequencies and distraction rates. RESULTS: The computational simulations showed similar bone regeneration patterns to those observed in the micro-CT data from the experiment throughout the DO process. This consisted of rapid bone formation during the first 10 days of the consolidation phase, followed by callus reshaping via bone remodeling. In addition, the computational model predicted a faster and more robust bone healing response as the model's distraction frequency was increased, whereas higher or lower distraction rates were not conducive to healing. CONCLUSIONS: This in silico model could be used to investigate basic mechanobiological mechanisms involved in bone regeneration or to optimize DO strategies for potential clinical applications.

The combined effects of dynamization time and degree on bone healing.

Dynamization, increasing the interfragmentary movement (IFM) by reducing the fixation stiffness from a rigid to a more flexible condition, is widely used clinically to promote fracture healing. However, it remains unknown how dynamization degree (relative change in fixation stiffness/IFM from a rigid to a flexible fixation) affects bone healing at various stages. To address this issue, we used a fuzzy logic-based mechano-regulated tissue differentiation algorithm on published experimental data from a sheep osteotomy healing model. We applied a varied degree of dynamization, from 0 (fully rigid fixation) to 0.9 (90% reduction in stiffness relative to the rigid fixation) after 1, 2, 3, and 4 weeks of osteotomy (R1wF, R2wF, R3wF, and R4wF) and computationally evaluated bone regeneration and biomechanical integrity over the healing process of 8 weeks. Compared with the constant rigid fixation, early dynamization (R1wF and R2wF) led to delays in bone bridging and biomechanical recovery of the osteotomized bone. However, the effect of early dynamization on healing was dependent of the degree of dynamization. Specifically, a higher dynamization degree (e.g., 0.9 for R1wF) led to a prolonged delay in bone bridging and largely unrecovered bending stiffness (48% relative to the intact bone), whereas a moderate degree of dynamization (e.g., 0.5 or 0.7) significantly enhanced bone formation and biomechanical properties of the osteotomized bone. These results suggest that dynamization degree and timing interactively affect the healing process. A combination of early dynamization with a moderate degree could enhance the ultimate biomechanical recovery of the fractured bone.

Enhancing the Efficiency of Distraction Osteogenesis through Rate-Varying Distraction: A Computational Study.

Distraction osteogenesis (DO) is a mechanobiological process of producing new bone and overlying soft tissues through the gradual and controlled distraction of surgically separated bone segments. The process of bone regeneration during DO is largely affected by distraction parameters. In the present study, a distraction strategy with varying distraction rates (i.e., "rate-varying distraction") is proposed, with the aim of shortening the distraction time and improving the efficiency of DO. We hypothesized that faster and better healing can be achieved with rate-varying distractions, as compared with constant-rate distractions. A computational model incorporating the viscoelastic behaviors of the callus tissues and the mechano-regulatory tissue differentiation laws was developed and validated to predict the bone regeneration process during DO. The effect of rate-varying distraction on the healing outcomes (bony bridging time and bone formation) was examined. Compared to the constant low-rate distraction, a low-to-high rate-varying distraction provided a favorable mechanical environment for angiogenesis and bone tissue differentiation, throughout the distraction and consolidation phase, leading to an improved healing outcome with a shortened healing time. These results suggest that a rate-varying clinical strategy could reduce the overall treatment time of DO and decrease the risk of complications related to the external fixator.

In Vivo Assessment of Age- and Loading Configuration-Related Changes in Multiscale Mechanical Behavior of the Human Proximal Femur Using MRI-Based Finite Element Analysis.

BACKGROUND: MRI-based finite element analysis (MRI-FEA) is the only method able to assess microstructural and whole-bone mechanical properties of the hip in vivo. PURPOSE: To examine whether MRI-FEA is capable of discriminating age-related changes in whole-bone mechanical performance and micromechanical behavior of the proximal femur, particularly considering the most common hip fracture-related sideways fall loading. STUDY TYPE: Retrospective. SUBJECTS: A total of nine younger (27 ± 3.2 years) and nine elderly (61 ± 3.9 years) healthy volunteers. FIELD STRENGTH/SEQUENCE: 3T; 3D fast field echo sequence. ASSESSMENT: The left proximal femurs were scanned and FE models created. FEA was performed to simulate sideways fall and stance loading for each femoral model. Apparent stiffness and high-risk (90th percentile) tensile and compressive strains of the proximal femur as well as the average strains within cubic regions of the femoral neck and greater trochanter were assessed. STATISTICAL TESTS: Paired and unpaired t-tests. RESULTS: Compared to the young group, the femoral stiffness of the elderly decreased by 39% and 40% (both P < 0.05) under the sideways fall and stance conditions, respectively. Accordingly, the high-risk tensile and compressive stains were elevated with aging (40% and 23% for sideways fall, 23% and 11% for stance conditions; all P < 0.05). However, the loading configuration-induced difference was only observed in the elderly group for the high-risk strains (22% for tension and 12% for compression; both P < 0.05). Additionally, compared to the stance condition, the sideways fall increased the average tensile (young: 108%, elderly: 123%; both P < 0.05) and compressive strains (young: 631%, elderly: 617%, both P < 0.05) within the greater trochanter rather than the femoral neck region. DATA CONCLUSION: In vivo MRI-FEA is capable of capturing age-related changes in both apparent-level stiffness and tissue-level micromechanical behavior of the proximal femur. However, the effect of sideways fall loading might be better reflected by tissue-level micromechanics rather than apparent stiffness. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1.

[Numerical simulation of fracture healing].

Fracture is a common physical injury. Its healing process involves complex biological activities at tissue, cellular and molecular levels and is affected by mechanical and biological factors. Over recent years, numerical simulation methods have been widely used to explore the mechanisms of fracture healing, design fixators and develop novel treatment strategies, etc. This paper mainly recommend the numerical methods used for simulating fracture healing and their latest research progress, which helps people better understand the mechanism of fracture healing, and also provides direction and guidance for the numerical simulation research of fracture healing in the future. First, the fracture healing process and its relationship with mechanical stimulation and biological factors are described. Then, the numerical models used for simulating fracture healing (including mechano-regulatory model, biological regulatory model and mechano-biological regulatory model) and corresponding modeling techniques (mainly including agent-based techniques and fuzzy logic controlling method) were summarized in particular. Finally, the future research directions in numerical simulation of fracture healing were preliminarily prospected.