A new approach for improving coronary plaque component analysis based on intravascular ultrasound images

Virtual histology intravascular ultrasound (VH-IVUS) is a clinically available technique for atherosclerosis plaque characterization. It, however, suffers from a poor longitudinal resolution due to electrocardiogram (ECG)-gated acquisition. This article presents an effective algorithm for IVUS image-based histology to overcome this limitation. After plaque area extraction within an input IVUS image, a textural analysis procedure consisting of feature extraction and classification steps is proposed. The pixels of the extracted plaque area excluding the shadow region were classified into one of the three plaque components of fibro-fatty (FF), calcification (CA) or necrotic core (NC) tissues. The average classification accuracy for pixel and region based validations is 75% and 87% respectively. Sensitivities (specificities) were 79% (85%) for CA, 81% (90%) for FF and 52% (82%) for NC. The kappa (kappa) = 0.61 and p value = 0.02 indicate good agreement of the proposed method with VH images. Finally, the enhancement in the longitudinal resolution was evaluated by reconstructing the IVUS images between the two sequential IVUS-VH images

Coronary Computed Tomography Angiography Based Assessment of Endothelial Shear Stress and Its Association with Atherosclerotic Plaque Distribution In-Vivo

Purpose The relationship between low endothelial shear stress (ESS) and coronary atherosclerosis is well established. ESS assessment so far depended on invasive procedures. The aim of this study was to demonstrate the relationship between ESS and coronary atherosclerosis by using non-invasive coronary computed tomography angiography (CTA) for computational fluid dynamics (CFD) simulations. Methods A total number of 7 consecutive patients with suspected coronary artery disease who received CTA and invasive angiography with IVUS analysis were included in this study. CTA examinations were performed using a dual-source scanner. These datasets were used to build a 3D mesh model. CFD calculations were performed using a validated CFD solver. The presence of plaque was assumed if the thickness of the intima-media complex exceeded 0.3 mm in IVUS. Plaque composition was derived by IVUS radiofrequency data analysis. Results Plaque was present in 32.1% of all analyzed cross-sections. Plaque prevalence was highest in areas of low ESS (49.6%) and high ESS (34.8%). In parts exposed to intermediate-low and intermediate-high ESS few plaques were found (20.0% and 24.0%) (p<0.001). Wall thickness was closely associated with local ESS. Intima-media thickness was 0.43 +/- 0.34mm in low and 0.38 +/- 0.32mm in high ESS segments. It was significantly lower when the arterial wall was exposed to intermediate ESS (0.25 +/- 0.18mm and 0.28 +/- 0.20mm) (p<0.001). Fibrofatty tissue was predominately found in areas exposed to low ESS (p <= 0.023). Conclusions In this study a close association of atherosclerotic plaque distribution and ESS pattern could be demonstrated in-vivo. Adding CFD analysis to coronary CTA offers the possibility to gather morphologic and physiologic data within one non-invasive examination

Manifold learning for image-based gating of intravascular ultrasound(IVUS) pullback sequences

Intravascular Ultrasound(IVUS) is an imaging technology which provides cross-sectional images of internal coronary vessel struc- tures. The IVUS frames are acquired by pulling the catheter back with a motor running at a constant speed. However, during the pullback, some artifacts occur due to the beating heart. These artifacts cause inaccu- rate measurements for total vessel and lumen volume and limitation for further processing. Elimination of these artifacts are possible with an ECG (electrocardiogram) signal, which determines the time interval cor- responding to a particular phase of the cardiac cycle. However, using ECG signal requires a special gating unit, which causes loss of impor- tant information about the vessel, and furthermore, ECG gating function may not be available in all clinical systems. To address this problem, we propose an image-based gating technique based on manifold learning. Quantitative tests are performed on 3 different patients, 6 different pull- backs and 24 different vessel cuts. In order to validate our method, the results of our method are compared to those of ECG-Gating method

Treatment options, complications and long‐term outcomes for limb fractures in pet rabbits

Background Limb fractures represent the most common orthopaedic disease in pet rabbits. However, only a few studies have evaluated therapeutic details of limb fractures. There are no data available for long‐term outcomes of limb fracture treatment. Methods The medical records of six institutions were reviewed retrospectively to identify cases of traumatic limb bone fractures in pet rabbits between 1999 and 2020. The medical records (n = 387) were analysed for details of fracture prevalence, aetiology, therapy protocols, treatment complications, outcome and long‐term effects. In addition to the retrospective data evaluation, 13 rabbits were re‐evaluated in person in recent clinical analyses, including orthopaedic examination, radiography and computed‐tomographic imaging. Details of long‐term effects of fracture treatment were requested over the telephone for a further 232 animals using a standardised questionnaire. Results Long bone fractures accounted for the majority of all fractures (296/387; 76.5%). Hindlimb fractures (301/387; 77.7%) were more common than forelimb fractures (86/387; 22.2%), and tibial fractures and combined fractures of the tibia and fibula (119/387; 30.8%) were observed most frequently. Most fracture treatments were based on osteosynthesis procedures (243/328; 74.1%). Treatment complications occurred in 130 out of 328 (39.6%) cases. A high bodyweight (p = 0.047) and an older age (p = 0.01) were found to be significant risk factors for the emergence of therapy complications. Overall, 75.4% of animals (175/232) had a satisfactory long‐term outcome. Limb posture anomalies were evaluated in 61 cases (26.3%). Limitations The multi‐centre approach led to the inclusion of various institutions, veterinarians, treatment protocols and rabbit populations that might have influenced the results. The medical records were reviewed retrospectively, so there were some data that were lacking or could not be collected in a standardised manner. Furthermore, rabbit owners' evaluation of long‐term outcomes might be prone to error, despite the use of a standardised interview questionnaire. Conclusion Limb fractures are a common orthopaedic issue in pet rabbits. The patient's bodyweight and age are significant risk factors for the emergence of complications during the fracture treatment process. Long‐term orthopaedic effects, such as abnormal limb posture and permanent lameness of the affected limb, were observed regularly

Coronary computed tomography angiography based assessment of endothelial shear stress and its association with atherosclerotic plaque distribution in-vivo.

PURPOSE: The relationship between low endothelial shear stress (ESS) and coronary atherosclerosis is well established. ESS assessment so far depended on invasive procedures. The aim of this study was to demonstrate the relationship between ESS and coronary atherosclerosis by using non-invasive coronary computed tomography angiography (CTA) for computational fluid dynamics (CFD) simulations. METHODS: A total number of 7 consecutive patients with suspected coronary artery disease who received CTA and invasive angiography with IVUS analysis were included in this study. CTA examinations were performed using a dual-source scanner. These datasets were used to build a 3D mesh model. CFD calculations were performed using a validated CFD solver. The presence of plaque was assumed if the thickness of the intima-media complex exceeded 0.3 mm in IVUS. Plaque composition was derived by IVUS radiofrequency data analysis. RESULTS: Plaque was present in 32.1% of all analyzed cross-sections. Plaque prevalence was highest in areas of low ESS (49.6%) and high ESS (34.8%). In parts exposed to intermediate-low and intermediate-high ESS few plaques were found (20.0% and 24.0%) (p<0.001). Wall thickness was closely associated with local ESS. Intima-media thickness was 0.43±0.34 mm in low and 0.38±0.32 mm in high ESS segments. It was significantly lower when the arterial wall was exposed to intermediate ESS (0.25±0.18 mm and 0.28 ± 0.20 mm) (p<0.001). Fibrofatty tissue was predominately found in areas exposed to low ESS (p≤0.023). CONCLUSIONS: In this study a close association of atherosclerotic plaque distribution and ESS pattern could be demonstrated in-vivo. Adding CFD analysis to coronary CTA offers the possibility to gather morphologic and physiologic data within one non-invasive examination

Manifold Learning for Image-Based Gating of Intravascular Ultrasound(IVUS) Pullback Sequences

Abstract. Intravascular Ultrasound(IVUS) is an imaging technology which provides cross-sectional images of internal coronary vessel structures. The IVUS frames are acquired by pulling the catheter back with a motor running at a constant speed. However, during the pullback, some artifacts occur due to the beating heart. These artifacts cause inaccurate measurements for total vessel and lumen volume and limitation for further processing. Elimination of these artifacts are possible with an ECG (electrocardiogram) signal, which determines the time interval corresponding to a particular phase of the cardiac cycle. However, using ECG signal requires a special gating unit, which causes loss of important information about the vessel, and furthermore, ECG gating function may not be available in all clinical systems. To address this problem, we propose an image-based gating technique based on manifold learning. Quantitative tests are performed on 3 different patients, 6 different pullbacks and 24 different vessel cuts. In order to validate our method, the results of our method are compared to those of ECG-Gating method

Three dimensional mesh model of a right coronary artery.

<p>Panel (A) shows the inflow (arrow) at the ostium of a right coronary artery and outflows of two small side branches (chevrons). Panel (B) shows a magnification of the outflow shown in panel (A). Notice the three boundary layers with small cell size (arrow head), necessary to accurately calculate ESS. Towards the inner lumen of the vessel, cell size increases to reduce the total number of cells and thus computation time.</p

Wall thickness.

<p>Mean wall thickness was highest in quartile 1 (low endothelial shear stress (ESS)) and lowest in quartiles 2 and 3 (intermediate ESS). Vessel wall thickness in quartile 4 (high ESS) was in between. Differences were not significant between quartile 2 and 3 (p = 0.15). All other differences were statistically significant (p < 0.001).</p><p>SD: standard deviation</p><p>Wall thickness.</p