Hip centre regression progression: Same equations, better numbers.

Accurate estimation of the hip joint centre (HJC) location is critical for modelling the kinematics and kinetics of the lower limb. Regression equations are commonly used to predict the HJC from anatomical landmarks on the pelvis, such as those published by Tylkowski et al., Andriacchi et al., Bell et al., and Seidel et al. Using a population of 159 CT-segmented pelvises, we assessed the accuracy of these methods as originally reported, and refined their parameters based on our larger cohort. We found the Tylkowski, Bell, and Seidel methods had mean Euclidean errors of 22.5, 26.4, and 17.9 mm, respectively. With new parameters for each method 'back-calculated' from our pelvic population, each method's error was reduced by an average of 69 %, with mean absolute errors of 7.9, 6.6, and 5.9 mm, respectively. For all methods, error has been reduced to below 1 cm, well below published levels for pelvic landmark estimation methods. These results highlight the need to validate and re-calibrate joint centre prediction methods on large, representative datasets to account for natural morphological variations.

Vascular assessment in small bowel obstruction: can CT predict requirement for surgical intervention?

PURPOSE: Small bowel obstruction (SBO) is a common cause of emergency presentations for abdominal pain and can be complicated by mesenteric ischemia. Computed tomography is currently central to diagnosis and management planning. Currently accepted signs identify secondary effects of the root physiological insult, which is vascular obstruction. We hypothesized that with advancements in CT technology and reconstruction algorithms, we can now more closely interrogate the mesenteric vasculature for obstruction and more accurately predict the need for surgical intervention. METHODS: We retrospectively audited the charts of all patients presenting with a clinical diagnosis of SBO at a single institution in a 12-month period. Two blinded consultant radiologists were then asked to analyze 3D MIP reconstruction CT scans for vascular obstruction in addition to any currently accepted signs of ischemia. Comparison between vascular cutoff and accepted current signs in the need for surgical intervention and the presence of any signs of ischemia in theater were recorded. RESULTS: Vascular cutoff had a comparable, with a trend towards superiority, sensitivity and specificity and inter-observer agreement to currently accepted signs of ischemia. The absence of a cut off sign has an excellent negative predictive value for ischemia with only 2 (3.7%) patients demonstrating ischemia at surgery where the vascular cutoff sign was not seen to be present. CONCLUSION: Interrogating vascular obstruction using 3D MIP reconstructions in small bowel obstruction may be a more sensitive and specific sign for prediction of surgical intervention, possibly occurring earlier in the obstruction pathway.

Influence of operator expertise and coronary luminal segmentation technique on diagnostic performance, precision and reproducibility of reduced-order CT-derived fractional flow reserve technique.

BACKGROUND: Onsite workstation-based CT-derived Fractional-Flow-Reserve (CT-FFR) is accurate in assessing hemodynamic-significance of coronary stenoses. We aim to describe the influence of operator expertise and luminal-segmentation technique on the diagnostic performance, precision and reproducibility of CT-FFR in identifying hemodynamically-significant stenosis (FFR≤0.8). METHODS: Forty-eight consecutive stable-patients (86 vessels) with suspected CAD underwent research indicated invasive-FFR and 320-detector CT-coronary-angiography (CTA). CT-FFR was derived using reduced-order model on standard desktop-computer. Semi-automated coronary luminal segmentation was performed using focused-technique with manual adjustments at regions of stenosis and calcification or comprehensive-technique with manual adjustments along the entire course of the vessel. CT-FFR analysis was performed using 3 blinded operators; core-laboratory engineer using focused-technique and radiographer and cardiologist using the comprehensive-technique. Diagnostic performance was assessed by area under receiver-operating-curve (AUC). Precision with invasive FFR was determined by Bland-Altman analysis, and reproducibility by intraclass-correlation-coefficient (ICC). RESULTS: Diagnostic performance was comparable among operators (Engineer: AUC = 0.88, Radiographer 0.84; Cardiologist 0.87; P = 0.59). Coronary luminal-segmentation time was shortest using focused technique (engineer 6:17 ± 2.43 min), compared with comprehensive technique (cardiologist 14.83 ± 7.09, radiographer 24.74 ± 12.65; P < 0.001). Use of focused technique was associated with widest limits of agreement (LOA) with FFR and moderate intra-operator reproducibility (engineer LOA -0.20-0.33; ICC 0.66), when compared with the comprehensive technique which demonstrated narrower LOA and excellent reproducibility [radiographer (LOA -0.17-0.20, ICC = 0.91) and cardiologist (LOA-0.15-0.23, ICC = -0.93)] CONCLUSION: A workstation-based CT-FFR technique was reproducible with high and comparable diagnostic performance among operators with different expertise. A comprehensive luminal segmentation technique was the most time-consuming and associated with the highest reproducibility and precision with FFR.

Performance of computed tomography-derived fractional flow reserve using reduced-order modelling and static computed tomography stress myocardial perfusion imaging for detection of haemodynamically significant coronary stenosis.

Aims: To compare the diagnostic performance of a reduced-order computed tomography-derived fractional flow reserve (CT-FFR) technique derived from luminal deformation and static CT stress myocardial perfusion (CTP). Methods and results: Forty-six patients (84 vessels) with suspected coronary artery disease from a single institution planned for elective coronary angiography prospectively underwent research indicated invasive fractional flow reserve (FFR) and 320-detector CT coronary angiography (CTA) and static CTP. Analyses were performed in separate blinded core laboratories for CT-FFR and CTP. CT-FFR was derived using a reduced-order model with dedicated software on a standard desktop computer. CTP was assessed visually and quantitatively by transmural perfusion ratio (TPR). Invasive FFR was significant in 33% (28/84) of vessels. Overall per-vessel sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for CT-FFR were 81%, 84%, 71%, 90%, and 83%, respectively, those of visual CTP were 54%, 92%, 79%, 77%, and 78%, respectively, and TPR were 64%, 48%, 42%, 70%, and 54%, respectively. Per-vessel receiver operator curve analysis demonstrated a significantly larger area under the curve (AUC) for CT-FFR (0.89) with that for visual CTP (0.72; P = 0.016), TPR (0.55; P < 0.0001), and CTA (0.76; P = 0.04). The addition of CT-FFR to CTA provided superior improvement in performance (AUC 0.93; P < 0.0001) compared with CTA alone, a combination of CTA with visual CTP (AUC 0.82; P = 0.007) and CTA with TPR (AUC 0.78; P = 0.0006). Conclusion: Based on this selected cohort of patients, a reduced-order CT-FFR technique is superior to visual and quantitatively assessed static CTP in detecting haemodynamically significant coronary stenosis as assessed by invasive FFR.

Noninvasive CT-Derived FFR Based on Structural and Fluid Analysis: A Comparison With Invasive FFR for Detection of Functionally Significant Stenosis.

OBJECTIVES: This study describes the feasibility and accuracy of a novel computed tomography (CT) fractional flow reserve (FFR) technique based on alternative boundary conditions. BACKGROUND: Techniques used to compute FFR based on images acquired from coronary computed tomography angiography (CTA) are described. Boundary conditions were typically determined by allometric scaling laws and assumptions regarding microvascular resistance. Alternatively, boundary conditions can be derived from the structural deformation of coronary lumen and aorta, although its accuracy remains unknown. METHODS: Forty-two patients (78 vessels) in a single institution prospectively underwent 320-detector coronary CTA and FFR. Deformation of coronary cross-sectional lumen and aorta, computed from coronary CTA images acquired over diastole, was used to determine the boundary conditions based on hierarchical Bayes modeling. CT-FFR was derived using a reduced order model performed using a standard desktop computer and dedicated software. First, 12 patients (20 vessels) formed the derivation cohort to determine optimal CT-FFR threshold with which to detect functional stenosis, defined as FFR of ≤0.8, which was validated in the subsequent 30 patients (58 vessels). RESULTS: Derivation cohort results demonstrated optimal threshold for CT-FFR was 0.8 with 67% sensitivity and 91% specificity. In the validation cohort, CT-FFR was successfully computed in 56 of 58 vessels (97%). Compared with coronary CTA, CT-FFR at ≤0.8 demonstrated a higher specificity (87% vs. 74%, respectively) and positive predictive value (74% vs. 60%, respectively), with comparable sensitivity (78% vs. 79%, respectively), negative predictive value (89% vs. 88%, respectively), and accuracy (area under the curve: 0.88 vs. 0.77, respectively; p = 0.22). Based on Bland-Altman analysis, mean intraobserver and interobserver variability values for CT-FFR were, respectively, -0.02 ± 0.05 (95% limits of agreement: -0.12 to 0.08) and 0.03 ± 0.06 (95% limits: 0.07 to 0.19). Mean time per patient for CT-FFR analysis was 27.07 ± 7.54 min. CONCLUSIONS: CT-FFR based on alternative boundary conditions and reduced-order fluid model is feasible, highly reproducible, and may be accurate in detecting FFR ≤ 0.8. It requires a short processing time and can be completed at point-of-care. Further validation is required in large prospective multicenter settings.

Predictive statistical models of baseline variations in 3-D femoral cortex morphology.

Quantifying human femoral cortex morphology is important for forensic science, surgical planning, prosthesis design and musculoskeletal modeling. Previous studies have been restricted by traditional zero or one dimensional morphometric measurements at discrete locations. We have used automatic image segmentation and statistical shape modeling methods to create predictive models of baseline 3-D femoral cortex morphology on a statistically significant population. A total of 204 femurs were automatically segmented and measured to obtain 3-D shape, whole-surface cortical thickness, and morphometric measurements. Principal components of shape and cortical thickness were correlated to anthropological data (age, sex, height and body mass) to produce predictive statistical models. We show that predictions of an individual's age, height, and sex can be improved by using 3-D shape and cortical thickness when compared with traditional morphometric measurements. We also show that femoral cortex geometry can be predicted from anthropological data combined with femoral measurements with less than 2.3 mm root mean square error, and cortical thickness with less than 0.5 mm root mean square error. The predictive models presented offer new ways to infer subject-specific 3-D femur morphology from sparse subject data for biomechanical simulations, and inversely infer subject data from femur morphology for anthropological and forensic studies.

Lower limb estimation from sparse landmarks using an articulated shape model.

Rapid generation of lower limb musculoskeletal models is essential for clinically applicable patient-specific gait modeling. Estimation of muscle and joint contact forces requires accurate representation of bone geometry and pose, as well as their muscle attachment sites, which define muscle moment arms. Motion-capture is a routine part of gait assessment but contains relatively sparse geometric information. Standard methods for creating customized models from motion-capture data scale a reference model without considering natural shape variations. We present an articulated statistical shape model of the left lower limb with embedded anatomical landmarks and muscle attachment regions. This model is used in an automatic workflow, implemented in an easy-to-use software application, that robustly and accurately estimates realistic lower limb bone geometry, pose, and muscle attachment regions from seven commonly used motion-capture landmarks. Estimated bone models were validated on noise-free marker positions to have a lower (p=0.001) surface-to-surface root-mean-squared error of 4.28mm, compared to 5.22mm using standard isotropic scaling. Errors at a variety of anatomical landmarks were also lower (8.6mm versus 10.8mm, p=0.001). We improve upon standard lower limb model scaling methods with shape model-constrained realistic bone geometries, regional muscle attachment sites, and higher accuracy.