Ultrasound Slices Research Articles

Ultrasound-based jawbone surface quality evaluation after alveolar ridge preservation.

Bone readiness for implant placement is typically evaluated by bone quality/density on 2-dimensional radiographs and cone beam computed tomography at an arbitrary time between 3 and 6months after tooth extraction and alveolar ridge preservation (ARP). The aim of this study is to investigate if high-frequency ultrasound (US) can classify bone readiness in humans, using micro-CT as a reference standard to obtain bone mineral density (BMD) and bone volume fraction (BVTV) of healed sockets receiving ARP in humans. A total of 27 bone cores were harvested during the implant surgery from 24 patients who received prior extraction with ARP. US images were taken immediately before the implant surgery at a site co-registered with the tissue biopsy collection location, made possible with a specially designed guide, and then classified into 3 tiers using B-mode image criteria (1) favorable, (2) questionable, and (3) unfavorable.Bone mineral density (hydroxyapatite) and BVTV were obtained from micro-CT as the gold standard. Hydroxyapatite and BVTV were evaluated within the projected US slice plane and thresholded to favorable (>2200mg/cm3; >0.45mm3/mm3), questionable (1500-2200mg/cm3; 0.4-0.45mm3/mm3), and unfavorable (<1500mg/cm3; <0.4mm3/mm3). The present US B-mode classification inversely scales with BMD. Regression analysis showed a significant relation between US classification and BMD as well as BVTV. T-test analysis demonstrated a significant correlation between US reader scores and the gold standard. When comparing Tier 1 with the combination of Tier 2 and 3, US achieved a significant group differentiation relative to mean BMD (p=0.004, true positive 66.7%, false positive 0%, true negative 100%, false negative 33.3%, specificity 100%, sensitivity 66.7%, receiver operating characteristics area under the curve 0.86). Similar results were found between US-derived tiers and BVTV. Preliminary data suggest US could classify jawbone surface quality that correlates with BMD/BVTV and serve as the basis for future development of US-based socket healing evaluation after ARP.

Deep learning for synovial volume segmentation of the first carpometacarpal joint in osteoarthritis patients

ObjectiveThe objective of this study was to develop a deep-learning-based approach to automatically segment 3D ultrasound images of the synovial tissue in osteoarthritis of the first carpometacarpal (CMC1 OA). DesignDeep learning predictions on 2D ultrasound slices sampled in the transverse plane were used to view the synovial tissue of the first carpometacarpal in patients with OA, followed by reconstruction into 3D surfaces. A modified 2D U-Net was trained using a dataset of 832 2D US images resliced from 89 3D US images. Segmentation accuracy was evaluated using a testing dataset of 208 2D US images resliced from 15 3D US images. Absolute and signed performance metrics were computed, and segmentation performance was compared between the manual segmentations of raters 1 and 2. ResultsResults of the U-Net-based run were mean 3D DSC 86.9 ± 4.8%, recall 93.7 ± 3.6%, precision 81.1 ± 6.9%, volume percent difference 16.9 ± 10.2%, mean surface distance 0.18 ± 0.04 mm, and Hausdorff distance 1.8 ± 0.8 mm. The algorithm demonstrated an overall increase in performance after 3D segmentation reconstruction compared to 2D predictions, but the difference was not statistically significant. ConclusionThis study investigated the use of a modified U-Net algorithm to automatically segment the synovial tissue volume (STV) of CMC1 OA patients and demonstrated that the addition of this deep learning method increases the efficiency of STV estimations in clinical trial settings.

Tumoral and peritumoral vascularization of brain tumours: a study comparing an intraoperative ultrasensitive Doppler and a preoperative first-pass perfusion MRI

IntroductionSurgery for gliomas can be guided by neuronavigation using magnetic resonance imaging (MRI) and intraoperative B-mode ultrasound. An ultrasensitive Doppler (USD) using plane waves is a new method of microvascularization imaging which can be used intraoperatively and could identify tumoral and peritumoral areas with neoangiogenesis but its value requires evaluation. The aim of this pilot study then was to evaluate the correlations between ultrasound measurements of glioma vascularization (tumoral and peritumoral region) obtained by a USD intraoperatively and first-pass perfusion measurements obtained on preoperative MRI. Methods18 patients with proven glial tumors were selected for the analysis. They underwent preoperative MRI and intraoperative USD acquisition. The MRI scans were re-aligned to the reference ultrasound slice plane, and for each patient a segmentation of the tumoral and peritumoral zone was performed. Two perfusion parameters were studied: relative cerebral tumor blood volume (rCCBV) in MRI and fractional moving blood volume (FMBV) in a USD. We studied the correlations between mean rCCBV and mean FMBV measured in the tumoral and peritumoral zones in the reference ultrasound slice plane. ResultsThe mean rCCBV and mean FMBV measured in the tumoral zone were significantly and strongly correlated (r = 0.80; p < 0.001). The mean rCCBV and mean FMBV measured in the peritumoral zone were not statistically correlated, although a tendency towards a correlation was noted (r = 0.45; p = 0.067). ConclusionThere was a good correlation between a tumor FMBV obtained by a USD intraoperatively and rCCBV on a preoperative MRI validating the reliability of USD for intraoperative analyses of tumor microvascularization in gliomas.

CAL-Tutor: A HoloLens 2 Application for Training in Obstetric Sonography and User Motion Data Recording.

Obstetric ultrasound (US) training teaches the relationship between foetal anatomy and the viewed US slice to enable navigation to standardised anatomical planes (head, abdomen and femur) where diagnostic measurements are taken. This process is difficult to learn, and results in considerable inter-operator variability. We propose the CAL-Tutor system for US training based on a US scanner and phantom, where a model of both the baby and the US slice are displayed to the trainee in its physical location using the HoloLens 2. The intention is that AR guidance will shorten the learning curve for US trainees and improve spatial awareness. In addition to the AR guidance, we also record many data streams to assess user motion and the learning process. The HoloLens 2 provides eye gaze, head and hand position, ARToolkit and NDI Aurora tracking gives the US probe positions and an external camera records the overall scene. These data can provide a rich source for further analysis, such as distinguishing expert from novice motion. We have demonstrated the system in a sample of engineers. Feedback suggests that the system helps novice users navigate the US probe to the standard plane. The data capture is successful and initial data visualisations show that meaningful information about user behaviour can be captured. Initial feedback is encouraging and shows improved user assessment where AR guidance is provided.

Anatomical study of the dorsal capsulo-scapholunate septum using high frequency ultrasonography

IntroductionThe dorsal capsulo-scapholunate septum (DCSS) is a recently described capsuloligamentous structure between the dorsal bundle of the scapholunate ligament (SLL) and the joint capsule of the wrist. It acts a secondary stabilizer of the scapholunate joint. The aim of this study was to evaluate the visibility and normal appearance of DCSS on high frequency ultrasound. HypothesisThe DCSS can be analyzed using high frequency ultrasound. Materials and methodsThree cadaveric wrists were dissected in order to study the DCSS; one without labeling and the other two after labeling under ultrasound guidance. On two other wrists, a correlation between the structure considered to be the DCSS on ultrasound and the corresponding CT arthrography and anatomical sections was carried out. Finally, sagittal ultrasound sections of the DCSS region on 42 healthy wrists were analyzed retrospectively. ResultsDuring dissection, the DCSS corresponded to a fibrous structure extending to the dorsal surface of the scaphoid and lunate, with certain fibers converging towards the SLL. On high-frequency ultrasound, a hyperechoic fibrillar structure was visualized at the theoretical position of the DCSS. The dissections performed after ultrasound-guided transfixion showed that its limits corresponded to the limits of the DCSS. The anatomical, CT arthrography and ultrasound sections in the DCSS region were concordant. The DCSS was retrospectively visible on at least one reference ultrasound slice in 90.5% of healthy wrists, and was always hyperechoic. Its average thickness was 1.38±0.24mm. DiscussionOur study describes the normal sonographic characteristics of the DCSS, visible in a majority of healthy patients on high-frequency ultrasound. The analysis of the sonographic characteristics of the DCSS in the context of acute wrist trauma should be evaluated, and a diagnostic decision tree has been proposed. Level of evidenceIV.

Normal variation of infant hip development : patterns revealed by 3D ultrasound.

Studies of infant hip development to date have been limited by considering only the changes in appearance of a single ultrasound slice (Graf's standard plane). We used 3D ultrasound (3DUS) to establish maturation curves of normal infant hip development, quantifying variation by age, sex, side, and anteroposterior location in the hip. We analyzed 3DUS scans of 519 infants (mean age 64 days (6 to 111 days)) presenting at a tertiary children's hospital for suspicion of developmental dysplasia of the hip (DDH). Hips that did not require ultrasound follow-up or treatment were classified as 'typically developing'. We calculated traditional DDH indices like α angle (αSP), femoral head coverage (FHCSP), and several novel indices from 3DUS like the acetabular contact angle (ACA) and osculating circle radius (OCR) using custom software. α angle, FHC, and ACA indices increased and OCR decreased significantly by age in the first four months, mean αSP rose from 62.2° (SD 5.7°) to 67.3° (SD 5.2°) (p < 0.001) in one- to eight- and nine- to 16-week-old infants, respectively. Mean αSP and mean FHCSP were significantly, but only slightly, lower in females than in males. There was no statistically significant difference in DDH indices observed between left and right hip. All 3DUS indices varied significantly between anterior and posterior section of the hip. Mean 3D indices of α angle and FHC were significantly lower anteriorly than posteriorly: αAnt = 58.2° (SD 6.1°), αPost = 63.8° (SD 6.3°) (p < 0.001), FHCAnt = 43.0 (SD 7.4), and FHCPost = 55.4° (SD 11.2°) (p < 0.001). Acetabular rounding measured byOCR indices was significantly greater in the anterior section of the hip (p < 0.001). We used 3DUS to show that hip shape and normal growth pattern vary significantly between anterior and posterior regions, by magnitudes similar to age-related changes. This highlights the need for careful selection of the Graf plane during 2D ultrasound examination. Whole-joint evaluation by obtaining either 3DUS or manual 'sweep' video images provides more comprehensive DDH assessment.Cite this article: Bone Jt Open2022;3(11):913-923.

Quantifying Patient-Specific in vivo Coronary Plaque Material Properties for Accurate Stress/Strain Calculations: An IVUS-Based Multi-Patient Study.

Introduction: Mechanical forces are closely associated with plaque progression and rupture. Precise quantifications of biomechanical conditions using in vivo image-based computational models depend heavily on the accurate estimation of patient-specific plaque mechanical properties. Currently, mechanical experiments are commonly performed on ex vivo cardiovascular tissues to determine plaque material properties. Patient-specific in vivo coronary material properties are scarce in the existing literature.Methods: In vivo Cine intravascular ultrasound and virtual histology intravascular ultrasound (IVUS) slices were acquired at 20 plaque sites from 13 patients. A three-dimensional thin-slice structure-only model was constructed for each slice to obtain patient-specific in vivo material parameter values following an iterative scheme. Effective Young's modulus (YM) was calculated to indicate plaque stiffness for easy comparison purposes. IVUS-based 3D thin-slice models using in vivo and ex vivo material properties were constructed to investigate their impacts on plaque wall stress/strain (PWS/PWSn) calculations.Results: The average YM values in the axial and circumferential directions for the 20 plaque slices were 599.5 and 1,042.8 kPa, respectively, 36.1% lower than those from published ex vivo data. The YM values in the circumferential direction of the softest and stiffest plaques were 103.4 and 2,317.3 kPa, respectively. The relative difference of mean PWSn on lumen using the in vivo and ex vivo material properties could be as high as 431%, while the relative difference of mean PWS was much lower, about 3.07% on average.Conclusion: There is a large inter-patient and intra-patient variability in the in vivo plaque material properties. In vivo material properties have a great impact on plaque stress/strain calculations. In vivo plaque material properties have a greater impact on strain calculations. Large-scale-patient studies are needed to further verify our findings.

Decompression of the suprascapular nerve at the suprascapular notch under combined arthroscopic and ultrasound guidance

Decompression of the suprascapular nerve (SSNe) at the suprascapular notch (SSNo) is usually performed with an arthroscopic procedure. This technique is well described but locating the nerve is complex because it is deeply buried and surrounded by soft tissue. We propose to combine ultrasound and arthroscopy (US-arthroscopy) to facilitate nerve localization, exposure and release. The main objective of this study was to assess the feasibility of this technique. This is an experimental, cadaveric study, carried out on ten shoulders. The first step of our technique is to locate the SSNo using an ultrasound scanner. Then an arthroscope is introduced under ultrasound control to the SSNo. A second portal is then created to dissect the pedicle and perform the ligament release. Ultrasound identification of the SSNo, endoscopic dissection and decompression of the nerve were achieved in 100% of cases. Ultrasound identification of the SSNo took an average of 3 min (± 4) while dissection and endoscopic release time took an average of 8 min (± 5). Ultrasound is an extremely powerful tool for non-invasive localization of nerves through soft tissues, but it is limited by the fact that tissue visualization is limited to the ultrasound slice plane, which is two-dimensional. On the other hand, arthroscopy (extra-articular) allows three-dimensional control of the surgical steps performed, but the locating of the nerve involves significant tissue detachment and a risk of damaging the nerve with the dissection. The combination of the two (US-arthroscopy) offers the possibility of combining the advantages of both techniques.

IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound.

We present an accurate, fast and efficient method for segmentation and muscle mask propagation in 3D freehand ultrasound data, towards accurate volume quantification. A deep Siamese 3D Encoder-Decoder network that captures the evolution of the muscle appearance and shape for contiguous slices is deployed. We use it to propagate a reference mask annotated by a clinical expert. To handle longer changes of the muscle shape over the entire volume and to provide an accurate propagation, we devise a Bidirectional Long Short Term Memory module. Also, to train our model with a minimal amount of training samples, we propose a strategy combining learning from few annotated 2D ultrasound slices with sequential pseudo-labeling of the unannotated slices. We introduce a decremental update of the objective function to guide the model convergence in the absence of large amounts of annotated data. After training with a few volumes, the decremental update strategy switches from a weak supervised training to a few-shot setting. Finally, to handle the class-imbalance between foreground and background muscle pixels, we propose a parametric Tversky loss function that learns to penalize adaptively the false positives and the false negatives. We validate our approach for the segmentation, label propagation, and volume computation of the three low-limb muscles on a dataset of 61600 images from 44 subjects. We achieve a Dice score coefficient of over 95% and a volumetric error of 1.6035 ± 0.587%.

Four-Dimensional Wide-Field Ultrasound Reconstruction System With Sparse Respiratory Signal Matching

Four-dimensional (4D) ultrasound reconstruction can greatly extend the spatial and temporal range of two-dimensional (2D) ultrasound in clinical practice. However, uneven breaths may yield a considerable motion artifact in the reconstructed time sequences of volume ultrasound. In this paper, a system with sparse respiratory signal matching is proposed to realize accurate 4D ultrasound reconstruction by effectively estimating uneven breaths. A slippery platform is built to automatically control the ultrasonic probe for acquiring 2D ultrasound slice sequences in different abdominal areas. Based on these acquired sequences, the signals of the respiratory phases are first extracted. Second, the sparse matched signal pairs are determined through the signal distribution and the corresponding image connectivity. After matching is conducted, signals in different positions are aligned in a common space wherein the outliers of the signals caused by the uneven breaths are excluded. Third, images with the same signal value in different positions are collected as the 3D image at the phase corresponding to the signal, and the 3D images at different phases are employed to reconstruct the final 4D image. The accuracies of the reconstructed 4D image are evaluated and compared with those of five existing methods. Experimental results demonstrate that the spatial and temporal continuities of the 4D ultrasound image reconstructed by our method outperform those obtained by other methods.

A novel calibration phantom for combining echocardiography with electromagnetic tracking

Abstract Ultrasound compounding techniques offer the possibility to enlarge the otherwise limited field of view of ultrasound. However, existing works mainly rely on larger ultrasound sensors. In this work, we attach electromagnetic (EM) tracking sensors to small tubular echo probes, namely an intracardiac echocardiographic (ICE) probe and a transesophageal echocardiographic (TEE) transducer. The EM tracking allows, when synchronized to the ultrasound, localization of the probes in either 5 DOF (Degrees of Freedom) or 6 DOF without line-of-sight requirement. For computation of the references between the two systems, we developed a novel customized 3D-printable phantom, which is especially convenient for tubular probes that acquire images laterally. Calibration with the phantom and 3D volume reconstruction was conducted in the Plus Toolkit. The volume reconstructor uses the captured position and orientation information to fuse 2D ultrasound slices into a compounded volume. Mean calibration error is below 2.5 mm for ICE and TEE. An accuracy evaluation of the 3D reconstruction using an object of known geometry revealed that tracking with 5 DOF provides unsatisfactory results, while the combination of 6 DOF and TEE achieved a mean absolute difference of 3.08 mm. Our calibration phantom fCal-Echo1.0 is openly available at http://perk-software.cs.queensu.ca/plus/doc/nightly/modelcatalog/.

Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales.

To evaluate the application of machine learning for the detection of subpleural pulmonary lesions (SPLs) in ultrasound (US) scans, we propose a novel boundary-restored network (BRN) for automated SPL segmentation to avoid issues associated with manual SPL segmentation (subjectivity, manual segmentation errors, and high time consumption). In total, 1612 ultrasound slices from 255 patients in which SPLs were visually present were exported. The segmentation performance of the neural network based on the Dice similarity coefficient (DSC), Matthews correlation coefficient (MCC), Jaccard similarity metric (Jaccard), Average Symmetric Surface Distance (ASSD), and Maximum symmetric surface distance (MSSD) was assessed. Our dual-stage boundary-restored network (BRN) outperformed existing segmentation methods (U-Net and a fully convolutional network (FCN)) for the segmentation accuracy parameters including DSC (83.45 ± 16.60%), MCC (0.8330 ± 0.1626), Jaccard (0.7391 ± 0.1770), ASSD (5.68 ± 2.70mm), and MSSD (15.61 ± 6.07mm). It also outperformed the original BRN in terms of the DSC by almost 5%. Our results suggest that deep learning algorithms aid fully automated SPL segmentation in patients with SPLs. Further improvement of this technology might improve the specificity of lung cancer screening efforts and could lead to new applications of lung US imaging.

TTTS-STgan: Stacked Generative Adversarial Networks for TTTS Fetal Surgery Planning Based on 3D Ultrasound.

Twin-to-twin transfusion syndrome (TTTS) is characterized by an unbalanced blood transfer through placental abnormal vascular connections. Prenatal ultrasound (US) is the imaging technique to monitor monochorionic pregnancies and diagnose TTTS. Fetoscopic laser photocoagulation is an elective treatment to coagulate placental communications between both twins. To locate the anomalous connections ahead of surgery, preoperative planning is crucial. In this context, we propose a novel multi-task stacked generative adversarial framework to jointly learn synthetic fetal US generation, multi-class segmentation of the placenta, its inner acoustic shadows and peripheral vasculature, and placenta shadowing removal. Specifically, the designed architecture is able to learn anatomical relationships and global US image characteristics. In addition, we also extract for the first time the umbilical cord insertion on the placenta surface from 3D HD-flow US images. The database consisted of 70 US volumes including singleton, mono- and dichorionic twins at 17-37 gestational weeks. Our experiments show that 71.8% of the synthesized US slices were categorized as realistic by clinicians, and that the multi-class segmentation achieved Dice scores of 0.82 ± 0.13, 0.71 ± 0.09, and 0.72 ± 0.09, for placenta, acoustic shadows, and vasculature, respectively. Moreover, fetal surgeons classified 70.2% of our completed placenta shadows as satisfactory texture reconstructions. The umbilical cord was successfully detected on 85.45% of the volumes. The framework developed could be implemented in a TTTS fetal surgery planning software to improve the intrauterine scene understanding and facilitate the location of the optimum fetoscope entry point.

Novel Similarity Metric for Image-Based Out-Of-Plane Motion Estimation in 3D Ultrasound.

Over the past decade, Freehand 3D Ultrasound(US) reconstruction using only image information has become a widely researched topic because it eliminates the need for an external tracking system and provides real-time volumetric information. But most of the state-of-art methods are inhibited by their inability to find a simple and robust similarity metric that could learn and estimate the spatial transformation between two US slices in a US sweep. In this work, we propose a novel similarity metric (TexSimAR), which computes the similarity value between two consecutive US images by correlating the parametric representation of the image-texture instead of the image itself. The purpose of this approach is to capture and compare the dynamics in the texture characteristics of two US images. We modelled these dynamics using a parametrical auto-regressive (AR) model. Experiments were performed on forearm datasets of three subjects. For every pair of consecutive US slices, we computed our TexSimAR similarity value and out-of-plane transformation from the ground truth to train a Support Vector Machine (SVM) based regression model, which was then used to predict the out-of-plane transformation with the similarity value as input. The proposed method shows promising results with predictions better than state-of-the-art methods even with 1/8th of training data compared to other methods in the literature.

Abstract 646: A Simple Multi-Risk-Factor Decision-Making Strategy for Improved Coronary Plaque Burden Increase Prediction: a Patient-Specific 3D FSI Study Using IVUS Follow-up

Plaque progression and vulnerability are influenced by many risk factors. Our goal is to find simple methods to combine multiple risk factors for better plaque development predictions. A sample size of 374 intravascular ultrasound (IVUS) slices with matched follow-up was obtained from 9 patients (Mean age 59, 7 m) with informed consent obtained. 3D fluid-structure interaction models were constructed to obtain plaque stress/strain conditions. Four morphological and biomechanical factors (plaque burden (PB), cap thickness (CT), lipid percent (LP) and average plaque wall stress (PWS)) were chosen to predict plaque burden increase defined as PBI = (PB at follow-up) - (PB at baseline). For a given slice Si, the ground truth Y PBI is define as Y PBI (Si)=1 if PBI(Si)&gt;0; Y PBI =0 if PBI(Si)≤0. For a single predictor W, a threshold value Wc was used to assign the binary prediction outcome: Y W (Si)=1 if W&gt;Wc; Y W (Si)=0 if W≤Wc. Wc was chosen to get optimal agreement between Y W and Y PBI . To use multiple predictors (say, W1, W2, W3) to PBI, a new predictor Combo(W1,W2,W3) was created with its values defined as Combo(W1,W2,W3)=Y W1 +Y W2 +Y W3 , where Y W1 , Y W2 and Y W3 were evaluated the same way as before. Combo was then treated as a single predictor and a threshold value was determined to achieve best agreement with Y PBI . Table 1 summarizes the optimal thresholds and agreement rates for all 15 strategies. Agreement rate using PB alone was 57.5%. PWS was the best single predictor for PBI with agreement rate 62.6%. Combining CT and PWS achieved 66.5% agreement rate, 9% better over PB, which was also obtained by combining 4 risk factors. The method presented here could be used to combine predictors from different sources (stenosis, cap, lipid, inflammation, macrophage, hemorrhage, stress, strain, flow shear stress, FFR, smoking, diabetes, cholesterol, alcohol, hypertension, pro-rupture genes, etc.) to improve prediction accuracy and help decision-making in clinical practice.

Portable Tracked Ultrasound System to Monitor Spinal Curvature

ntroduction: Adolescent idiopathic scoliosis (AIS) is the most frequent spinal deformation. It is assessed by frequent x-ray imaging, exposing patients to frequent radiation, increasing the risk of cancer. Tracked ultrasound imaging produce a three-dimensional visual of the spine without risk. We proposed using an optically tracked handheld USB Interson ultrasound probe, which is much less expensive than the current standard ultrasounds.&#x0D; Methods: A practical setup was developed for scoliosis monitoring. Reference markers were on the patient’s shoulder and the wall to account for patient motion, and simulate the plane of the x-ray detector to allow future comparisons. The optical tracker tracked the markers and ultrasound probe. This was compared to the electromagnetically tracked non-portable Ultrasonix ultrasound. Scanning captured the thoracic and lumbar regions. A three-dimensional image was composited by stacking a series of two-dimensional ultrasound slices in their tracked physical positions. To compare the two-ultrasound setups and ensure the ease of identification, a novice attempted to manually identify transverse processes. These were compared to see if all transverse processes scanned could be marked from both ultrasound setups. Two clinical experts then confirmed the markings were anatomically correct.&#x0D; Results and Conclusions: In all scans 100% of the transverse processes scanned (n = 51) were identified in Interson and Ultrasonix images. Curvatures measured using the Ultrasonix method have been previously validated to curvature measurements from x-ray images. Thus the 100% correspondence of the Interson and Ultrasonix setups indicates this inexpensive method is a promising tool to reduce radiation exposure during scoliosis monitoring.

Fluid-structure interaction models based on patient-specific IVUS at baseline and follow-up for prediction of coronary plaque progression by morphological and biomechanical factors: A preliminary study

Plaque morphology and biomechanics are believed to be closely associated with plaque progression. In this paper, we test the hypothesis that integrating morphological and biomechanical risk factors would result in better predictive power for plaque progression prediction. A sample size of 374 intravascular ultrasound (IVUS) slices was obtained from 9 patients with IVUS follow-up data. 3D fluid-structure interaction models were constructed to obtain both structural stress/strain and fluid biomechanical conditions. Data for eight morphological and biomechanical risk factors were extracted for each slice. Plaque area increase (PAI) and wall thickness increase (WTI) were chosen as two measures for plaque progression. Progression measure and risk factors were fed to generalized linear mixed models and linear mixed-effect models to perform prediction and correlation analysis, respectively. All combinations of eight risk factors were exhausted to identify the optimal predictor(s) with highest prediction accuracy defined as sum of sensitivity and specificity. When using a single risk factor, plaque wall stress (PWS) at baseline was the best predictor for plaque progression (PAI and WTI). The optimal predictor among all possible combinations for PAI was PWS + PWSn + Lipid percent + Min cap thickness + Plaque Area (PA) + Plaque Burden (PB) (prediction accuracy = 1.5928) while Wall Thickness (WT) + Plaque Wall Strain (PWSn) + Plaque Area (PA) was the best for WTI (1.2589). This indicated that PAI was a more predictable measure than WTI. The combination including both morphological and biomechanical parameters had improved prediction accuracy, compared to predictions using only morphological features.

Global Patch Matching (GPM) for freehand 3D ultrasound reconstruction

Background3D ultrasound volume reconstruction from B-model ultrasound slices can provide more clearly and intuitive structure of tissue and lesion for the clinician.MethodsThis paper proposes a novel Global Path Matching method for the 3D reconstruction of freehand ultrasound images. The proposed method composes of two main steps: bin-filling scheme and hole-filling strategy. For the bin-filling scheme, this study introduces two operators, including the median absolute deviation and the inter-quartile range absolute deviation, to calculate the invariant features of each voxel in the 3D ultrasound volume. And the best contribution range for each voxel is obtained by calculating the Euclidian distance between current voxel and the voxel with the minimum invariant features. Hence, the intensity of the filling vacant voxel can be obtained by weighted combination of the intensity distribution of pixels in the best contribution range. For the hole-filling strategy, three conditions, including the confidence term, the data term and the gradient term, are designed to calculate the weighting coefficient of the matching patch of the vacant voxel. While the matching patch is obtained by finding patches with the best similarity measure that defined by the three conditions in the whole 3D volume data.ResultsCompared with VNN, PNN, DW, FMM, BI and KR methods, the proposed Global Path Matching method can restore the 3D ultrasound volume with minimum difference.ConclusionsExperimental results on phantom and clinical data sets demonstrate the effectiveness and robustness of the proposed method for the reconstruction of ultrasound volume.

On the reproducibility of expert-operated and robotic ultrasound acquisitions.

We present the evaluation of the reproducibility of measurements performed using robotic ultrasound imaging in comparison with expert-operated sonography. Robotic imaging for interventional procedures may be a valuable contribution, but requires reproducibility for its acceptance in clinical routine. We study this by comparing repeated measurements based on robotic and expert-operated ultrasound imaging. Robotic ultrasound acquisition is performed in three steps under user guidance: First, the patient is observed using a 3D camera on the robot end effector, and the user selects the region of interest. This allows for automatic planning of the robot trajectory. Next, the robot executes a sweeping motion following the planned trajectory, during which the ultrasound images and tracking data are recorded. As the robot is compliant, deviations from the path are possible, for instance due to patient motion. Finally, the ultrasound slices are compounded to create a volume. Repeated acquisitions can be performed automatically by comparing the previous and current patient surface. After repeated image acquisitions, the measurements based on acquisitions performed by the robotic system and expert are compared. Within our case series, the expert measured the anterior-posterior, longitudinal, transversal lengths of both of the left and right thyroid lobes on each of the 4 healthy volunteers 3 times, providing 72 measurements. Subsequently, the same procedure was performed using the robotic system resulting in a cumulative total of 144 clinically relevant measurements. Our results clearly indicated that robotic ultrasound enables more repeatable measurements. A robotic ultrasound platform leads to more reproducible data, which is of crucial importance for planning and executing interventions.

FUIQA: Fetal Ultrasound Image Quality Assessment With Deep Convolutional Networks.

The quality of ultrasound (US) images for the obstetric examination is crucial for accurate biometric measurement. However, manual quality control is a labor intensive process and often impractical in a clinical setting. To improve the efficiency of examination and alleviate the measurement error caused by improper US scanning operation and slice selection, a computerized fetal US image quality assessment (FUIQA) scheme is proposed to assist the implementation of US image quality control in the clinical obstetric examination. The proposed FUIQA is realized with two deep convolutional neural network models, which are denoted as L-CNN and C-CNN, respectively. The L-CNN aims to find the region of interest (ROI) of the fetal abdominal region in the US image. Based on the ROI found by the L-CNN, the C-CNN evaluates the image quality by assessing the goodness of depiction for the key structures of stomach bubble and umbilical vein. To further boost the performance of the L-CNN, we augment the input sources of the neural network with the local phase features along with the original US data. It will be shown that the heterogeneous input sources will help to improve the performance of the L-CNN. The performance of the proposed FUIQA is compared with the subjective image quality evaluation results from three medical doctors. With comprehensive experiments, it will be illustrated that the computerized assessment with our FUIQA scheme can be comparable to the subjective ratings from medical doctors.