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Background Purpose Methods Results Conclusion Accurate preoperative prediction of cervical lymph node metastasis (LNM) for papillary thyroid carcinoma (PTC) patients is essential for disease staging and individualized treatment planning, which can improve prognosis and facilitate better management.To establish a fully automated deep learning‐enabled model (FADLM) for automated tumor segmentation and cervical LNM prediction in PTC using ultrasound (US) video keyframes.The bicentral study retrospective enrolled 518 PTC patients, who were then randomly divided into the training (Hospital 1, n = 340), internal test (Hospital 1, n = 83), and external test cohorts (Hospital 2, n = 95). The FADLM integrated mask region‐based convolutional neural network (Mask R‐CNN) for automatic thyroid primary tumor segmentation and ResNet34 with Bayes strategy for cervical LNM diagnosis. A radiomics model (RM) using the same automated segmentation method, a traditional radiomics model (TRM) using manual segmentation, and a clinical‐semantic model (CSM) were developed for comparison. The dice similarity coefficient (DSC) was used to evaluate segmentation performance. The prediction performance of the models was validated in terms of discrimination and clinical utility with the area under the receiver operator characteristic curve (AUC), heatmap analysis, and decision curve analysis (DCA). The comparison of the predictive performance among different models was conducted by DeLong test. The performances of two radiologists compared with FADLM and the diagnostic augmentation with FADLM's assistance were analyzed in terms of accuracy, sensitivity and specificity using McNemar's x2 test. The p‐value less than 0.05 was defined as a statistically significant difference. The Benjamini‐Hochberg procedure was applied for multiple comparisons to deal with Type I error.The FADLM yielded promising segmentation results in training (DSC: 0.88 ± 0.23), internal test (DSC: 0.88 ± 0.23), and external test cohorts (DSC: 0.85 ± 0.24). The AUCs of FADLM for cervical LNM prediction were 0.78 (95% CI: 0.73, 0.83), 0.83 (95% CI: 0.74, 0.92), and 0.83 (95% CI: 0.75, 0.92), respectively. It all significantly outperformed the RM (AUCs: 0.78 vs. 0.72; 0.83 vs. 0.65; 0.83 vs. 0.68, all adjusted p‐values < 0.05) and CSM (AUCs: 0.78 vs. 0.71; 0.83 vs. 0.62; 0.83 vs. 0.68, all adjusted p‐values < 0.05) across the three cohorts. The RM offered similar performance to that of TRM (AUCs: 0.61 vs. 0.63, adjusted p‐value = 0.60) while significantly reducing the segmentation time (3.3 ± 3.8 vs. 14.1 ± 4.2 s, p‐value < 0.001). Under the assistance of FADLM, the accuracies of junior and senior radiologists were improved by 18% and 15% (all adjusted p‐values < 0.05) and the sensitivities by 25% and 21% (all adjusted p‐values < 0.05) in the external test cohort.The FADLM with elaborately designed automated strategy using US video keyframes holds good potential to provide an efficient and consistent prediction of cervical LNM in PTC. The FADLM displays superior performance to RM, CSM, and radiologists with promising efficacy. [ABSTRACT FROM AUTHOR]

Background: High dose rate brachytherapy is commonly used in the treatment of prostate cancer. Treatment planning is often performed under transrectal ultrasound (US) guidance, but brachytherapy needles can be challenging to digitize due to the presence of poor US conspicuity and imaging artifacts. The plan accuracy and quality, however, are dependent on the proper visualization of the needles with millimeter accuracy. Purpose: This work describes a technique for generating a color overlay of needle locations atop the grayscale US image. Prototype devices were developed to produce vibrations in the brachytherapy needles that generate a high contrast color Doppler (CD) signal that highlights the needle locations with superior contrast and reduced artifacts. Denoted by the acronym color VISION (Vibrationally Induced Shimmering for Identifying an Object's Nature), the technology has the potential to improve applicator conspicuity and facilitate automated applicator digitization. Methods: Three prototype vibrational devices with frequencies between 200–450 Hz were designed in‐house and evaluated with needle implants in a phantom and cadaveric male pelvis using: (1) an actuator attached to the front of a prostate needle template; (2) an actuator attached to the top of the needle template; and (3) a hand‐held actuator with a stylet, inserted directly into a needle's inner lumen. Acquired images were postprocessed in MATLAB to evaluate the potential for automated digitization. Results: All prototype devices produced localized shimmering in implanted brachytherapy needles in both the axial and sagittal planes. The template mounted actuators provided better vibrational coupling and ease of operation than the stylet prototype. The Michelson contrast, or visibility, of the shimmering CD signal was 100% compared with ≤40% for B‐mode imaging of a single needle. Proof‐of‐principle for automated applicator digitization using only the CD signal was demonstrated. Conclusions: The color VISION prototype devices successfully coupled mechanical vibrations into brachytherapy needles to generate US CD shimmering and accurately highlight brachytherapy needle locations. The high contrast and natively registered signal are promising for future work to automate the needle digitization and provide a real‐time visual overlay of the applicator on the B‐mode US image. [ABSTRACT FROM AUTHOR]

Background: Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI‐RADS) category 4 has the highest false‐positive value of about 30% among five categories. The classification task in BI‐RADS category 4 is challenging and has not been fully studied. Purpose: This work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B‐mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy. Methods: First, original US images coming directly from the hospital were cropped and resized. In 1385 B‐mode US BI‐RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K‐means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B‐mode images, K‐means clustering images and entropy images, they are fused in a three‐channel form multi‐feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1‐score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro‐Wilk test. DeLong test and independent t‐test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti‐log compression was conducted but no improvement has shown in CNNs classification results. Results: With multi‐feature fusion images, DenseNet121 has highest accuracy of 80.22 ± 1.45% compared to other CNNs, precision of 77.97 ± 2.89% and AUC of 0.82 ± 0.01. Multi‐feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B‐mode images (p < 0.05). Conclusion: The CNNs with multi‐feature fusion show a good potential of reducing the false‐positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false‐positive rate in breast tumor within US BI‐RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise. [ABSTRACT FROM AUTHOR]

Background: Ultrasound (US) has demonstrated to be an effective guidance technique for lumbar spine injections, enabling precise needle placement without exposing the surgeon or the patient to ionizing radiation. However, noise and acoustic shadowing artifacts make US data interpretation challenging. To mitigate these problems, many authors suggested using computed tomography (CT)‐to‐US registration to align the spine in pre‐operative CT to intra‐operative US data, thus providing localization of spinal landmarks. Purpose: In this paper, we propose a deep learning (DL) pipeline for CT‐to‐US registration and address the problem of a need for annotated medical data for network training. Firstly, we design a data generation method to generate paired CT‐US data where the spine is deformed in a physically consistent manner. Secondly, we train a point cloud (PC) registration network using anatomy‐aware losses to enforce anatomically consistent predictions. Methods: Our proposed pipeline relies on training the network on realistic generated data. In our data generation method, we model the properties of the joints and disks between vertebrae based on biomechanical measurements in previous studies. We simulate the supine and prone position deformation by applying forces on the spine models. We choose the spine models from 35 patients in VerSe dataset. Each spine is deformed 10 times to create a noise‐free data with ground‐truth segmentation at hand. In our experiments, we use one‐leave‐out cross‐validation strategy to measure the performance and the stability of the proposed method. For each experiment, we choose generated PCs from three spines as the test set. From the remaining, data from 3 spines act as the validation set and we use the rest of the data for training the algorithm. To train our network, we introduce anatomy‐aware losses and constraints on the movement to match the physics of the spine, namely, rigidity loss and bio‐mechanical loss. We define rigidity loss based on the fact that each vertebra can only transform rigidly while the disks and the surrounding tissue are deformable. Second, by using bio‐mechanical loss we stop the network from inferring extreme movements by penalizing the force needed to get to a certain pose. Results: To validate the effectiveness of our fully automated data generation pipeline, we qualitatively assess the fidelity of the generated data. This assessment involves verifying the realism of the spinal deformation and subsequently confirming the plausibility of the simulated ultrasound images. Next, we demonstrate that the introduction of the anatomy‐aware losses brings us closer to state‐of‐the‐art (SOTA) and yields a reduction of 0.25 mm in terms of target registration error (TRE) compared to using only mean squared error (MSE) loss on the generated dataset. Furthermore, by using the proposed losses, the rigidity loss in inference decreases which shows that the inferred deformation respects the rigidity of the vertebrae and only introduces deformations in the soft tissue area to compensate the difference to the target PC. We also show that our results are close to the SOTA for the simulated US dataset with TRE of 3.89 mm and 3.63 mm for the proposed method and SOTA respectively. In addition, we show that our method is more robust against errors in the initialization in comparison to SOTA and significantly achieves better results (TRE of 4.88 mm compared to 5.66 mm) in this experiment. Conclusions: In conclusion, we present a pipeline for spine CT‐to‐US registration and explore the potential benefits of utilizing anatomy‐aware losses to enhance registration results. Additionally, we propose a fully automatic method to synthesize paired CT‐US data with physically consistent deformations, which offers the opportunity to generate extensive datasets for network training. The generated dataset and the source code for data generation and registration pipeline can be accessed via https://github.com/mfazampour/medphys_ct_us_registration. [ABSTRACT FROM AUTHOR]

Background: Needle‐based procedures, such as fine needle aspiration and thermal ablation, are often applied for thyroid nodule diagnosis and therapeutic purposes, respectively. With blood vessels and nerves nearby, these procedures can pose risks in damaging surrounding critical structures. Purpose: The development and validation of innovative strategies to manage these risks require a test object with well‐characterized physical properties. For this work, we focus on the application of ultrasound‐guided thermal radiofrequency ablation. Methods: We have developed a single‐use anthropomorphic phantom mimicking the thyroid and surrounding anatomical and physiological structures that are relevant to ultrasound‐guided thermal ablation. The phantom was composed of a mixture of polyacrylamide, water, and egg white extract and was cast using molds in multiple steps. The thermal, acoustical, and electrical characteristics were experimentally validated. The ablation zones were analyzed via non‐destructive T2‐weighted magnetic resonance imaging scans utilizing the relaxometry changes of coagulated egg albumen, and the temperature distribution was monitored using an array of fiber Bragg grating sensors. Results: The physical properties of the phantom were verified both on ultrasound as well as in terms of the phantom response to thermal ablation. The final temperature achieved (92°C), the median percentage of the nodule ablated (82.1%), the median volume ablated outside the nodule (0.8 mL), and the median number of critical structures affected (0) were quantified. Conclusion: An anthropomorphic phantom that can provide a realistic model for development and training in ultrasound‐guided needle‐based thermal interventions for thyroid nodules has been presented. [ABSTRACT FROM AUTHOR]

Background: The prevalence of liver diseases, especially steatosis, requires a more convenient and noninvasive tool for liver diagnosis, which can be a surrogate for the gold standard biopsy. Magnetic resonance (MR) measurement offers potential, however ultrasound (US) has better accessibility than MR. Purpose: This study aims to suggest a multiparametric US approach which demonstrates better quantification and imaging performance than MR imaging‐based proton density fat fraction (MRI‐PDFF) for hepatic steatosis assessment. Methods: We investigated early‐stage steatosis to evaluate our approach. An in vivo (within the living) animal study was performed. Fat inclusions were accumulated in the animal livers by feeding a methionine and choline deficient (MCD) diet for 2 weeks. The animals (n = 19) underwent US and MR imaging, and then their livers were excised for histological staining. From the US, MR, and histology images, fat accumulation levels were measured and compared: multiple US parameters; MRI‐PDFF; histology fat percentages. Seven individual US parameters were extracted using B‐mode measurement, Burr distribution estimation, attenuation estimation, H‐scan analysis, and shear wave elastography. Feature selection was performed, and the selected US features were combined, providing quantification of fat accumulation. The combined parameter was used for visualizing the localized probability of fat accumulation level in the liver; This procedure is known as disease‐specific imaging (DSI). Results: The combined US parameter can sensitively assess fat accumulation levels, which is highly correlated with histology fat percentage (R = 0.93, p‐value < 0.05) and outperforms the correlation between MRI‐PDFF and histology (R = 0.89, p‐value < 0.05). Although the seven individual US parameters showed lower correlation with histology compared to MRI‐PDFF, the multiparametric analysis enabled US to outperform MR. Furthermore, this approach allowed DSI to detect and display gradual increases in fat accumulation. From the imaging output, we measured the color‐highlighted area representing fatty tissues, and the fat fraction obtained from DSI and histology showed strong agreement (R = 0.93, p‐value < 0.05). Conclusions: We demonstrated that fat quantification utilizing a combination of multiple US parameters achieved higher performance than MRI‐PDFF; therefore, our multiparametric analysis successfully combined selected features for hepatic steatosis characterization. We anticipate clinical use of our proposed multiparametric US analysis, which could be beneficial in assessing steatosis in humans. [ABSTRACT FROM AUTHOR]

Background: There is increasing interest in using ultrasound for thermal ablation, histotripsy, and thermal or cavitational enhancement of drug delivery for the treatment of pancreatic cancer. Ultrasonic and thermal modelling conducted as part of the treatment planning process requires acoustic property values for all constituent tissues, but the literature contains no data for the human pancreas. Purpose: This study presents the first acoustic property measurements of human pancreatic samples and provides examples of how these properties impact a broad range of ultrasound therapies. Methods: Data were collected on human pancreatic tissue samples at physiological temperature from 23 consented patients in cooperation with a hospital pathology laboratory. Propagation of ultrasound over the 2.1–4.5 MHz frequency range through samples of various thicknesses and pathologies was measured using a set of custom‐built ultrasonic calipers, with the data processed to estimate sound speed and attenuation. The results were used in acoustic and thermal simulations to illustrate the impacts on extracorporeal ultrasound therapies for mild hyperthermia, thermal ablation, and histotripsy implemented with a CE‐marked clinical system operating at 0.96 MHz. Results: The mean sound speed and attenuation coefficient values for human samples were well below the range of values in the literature for non‐human pancreata, while the human attenuation power law exponents were substantially higher. The simulated impacts on ultrasound mediated therapies for the pancreas indicated that when using the human data instead of the literature average, there was a 30% reduction in median temperature elevation in the treatment volume for mild hyperthermia and 43% smaller volume within a 60°C contour for thermal ablation, all driven by attenuation. By comparison, impacts on boiling and intrinsic threshold histotripsy were minor, with peak pressures changing by less than 15% (positive) and 1% (negative) as a consequence of the counteracting effects of attenuation and sound speed. Conclusion: This study provides the most complete set of speed of sound and attenuation data available for the human pancreas, and it reiterates the importance of acoustic material properties in the planning and conduct of ultrasound‐mediated procedures, particularly thermal therapies. [ABSTRACT FROM AUTHOR]

Background: The distension properties of the vagina are critical to its function including support of surrounding organs, childbirth, and intercourse. It could be altered by many pathophysiological processes like pregnancy, radiotherapy, and reconstruction surgery. However, there are no clinically available diagnostic tools capable of quantifying the distension properties of the vagina. Purpose: A proof‐of‐concept study was designed to assess the feasibility of a novel three‐dimensional (3D) ultrasound imaging technique that allows quantitative evaluation of the vagina under distension. Methods: Patients with symptomatic pelvic organ prolapse (POP) were recruited for the study. An ultrathin, oversized bag was inserted into the vagina and filled with water using a modified urodynamics system. The instilled water volume and intravaginal pressure were continuously recorded. At maximum vaginal capacity, 3D transintroital ultrasound of the distended vagina and surrounding pelvic structures was performed. Exams were performed in duplicate for each patient, two hours apart (round A and round B). Following the development of a 3D surface model of the distended vagina from each scan, several measurements were obtained, including cross‐sectional area, anteroposterior (AP) length and lateral width in the plane of minimum hiatal dimensions (PMHD), AP and lateral diameter at the pubic symphysis (PS) level, maximum and minimum diameter, and maximum vertical length. To assess repeatability between measurements in two rounds, the coefficient of variation (CV) and the intraclass correlation coefficient (ICC) were calculated for each measurement. Correlations between physical measurements including the pelvic organ prolapse quantification (POP‐Q) system and vaginal diameter measurements, and obtained metrics were also assessed. Results: Sixteen patients with POP (average age 69 years) completed both rounds of imaging. There was sufficient echogenicity on 3D transintroital ultrasound of the distended vaginal wall to establish boundaries for 3D surface models of the vagina. Overall, all metrics had good or excellent reliability (ICC = 0.77–0.93, p < 0.05; CV = 3%–18%) except maximum diameter, which demonstrated only moderate reliability (ICC = 0.67, p = 0.092). Strong correlations were found between physical exam measurements including D point of POP‐Q, introitus diameter and lateral diameter at apex, and maximum vaginal capacity, maximum vertical length, lateral diameter at PS, minimum diameter, and distended PMHD measurements. The results demonstrated that this system could generate 3D models of the shape of the distended vagina and provide multiple metrics that could be reliably calculated from automated analyses of the models. Conclusions: A novel system for evaluation of the distension properties of the vagina was developed and preliminary evaluation was performed. This system may represent a technique for evaluation of the biomechanical and structural properties of the vagina. [ABSTRACT FROM AUTHOR]

Background: Pulsed wave Doppler ultrasound is a useful modality for assessing vascular health as it quantifies blood flow characteristics. To facilitate accurate diagnosis, accuracy and consistency of this modality should be assessed through Doppler quality assurance (QA). Purpose: The purpose of this study was to characterize the accuracy, reproducibility, and inter‐scanner variability of ultrasound flow velocity measurements via a flow phantom, with a focus on the effect of systematic acquisition parameters on measured flow velocity accuracy. Methods: Using a manufacturer‐calibrated flow phantom, pulsed wave measurements were acquired on five clinical systems (iU22, Philips) with three models of transducers, including both linear and curvilinear models. The peak and mean flow velocities were estimated by vendor‐supplied spectral analysis tools. To investigate intra‐ and inter‐scanner variability, measurements were repeated using each scanner‐transducer pair under a standardized set of conditions. Inter‐scanner variability was assessed using ANOVA. Flow velocity accuracy was investigated by mean absolute percentage error. The impacts of receive gain, measurement depth, and beam steering on measured flow velocity accuracy were examined by varying each parameter over its available range and comparing to the ground truth flow velocity. Results: Inter‐scanner variability was statistically significant for peak flow measurements made using both linear and curvilinear transducers, though absolute differences in measured velocity were small. Inter‐scanner variability was not statistically significant for mean flow velocity. Receive gain, measurement depth, and beam steering were all found to impact the accuracy of measured flow characteristics for linear transducers. Accuracy of the flow measurements made with the curvilinear transducer demonstrated high consistency to changes in receive gain at a constant depth, though were impacted by increasing the measurement depth. Conclusions: Carefully and consistently selected acquisition and set‐up parameters are essential in order to establish a reliable and meaningful QA program. [ABSTRACT FROM AUTHOR]

Background: Characterizations based on anatomically realistic phantoms are highly effective to perform accurate technical validation of imaging systems. Specifically for photoacoustic imaging (PAI), although a variety of phantom models with simplified geometries are reported, an unmet need still exists to establish morphologically realistic heterogeneous pre‐clinical phantoms. So the development of a mouse‐mimicking phantom can reduce the use of animals for the validation and standardization studies of pre‐clinical PAI systems and thus eventually translate the PAI technology to clinical research. Purpose: Here we designed, developed, and fabricated a stable phantom that mimics the detailed morphology of a mouse, to be used as a realistic tool for PAI. Methods: The mouse phantom, has been designed by using a combination of image modeling and 3D‐printing techniques. As a tissue‐mimicking material, we have used copolymer‐in‐oil‐based material that was recently proposed by the International Photoacoustic Standardization Consortium (IPASC). In particular, the anatomically realistic phantom has been modeled by using the real atlas of a mouse as a reference. The mouse phantom includes a 3D‐printed skeleton and the main abdominal organs such as the liver, spleen, and kidneys obtained by using doped copolymer‐in‐oil material with 3D‐printed molds. In addition, the acoustic and optical properties of the tissue‐mimicking material and the long‐term stability have been broadly characterized. Results: Furthermore, our studies showed that the phantom is durable and stable for more than 200 days, under normal storage and repeated use. Fabrication protocol is easy to reproduce. As a result, the proposed morphologically realistic mouse phantom offers durability, material compatibility, and an unprecedented realistic resemblance to the actual rodents' anatomy in PAI. Conclusion: This durable morphologically realistic mouse phantom would minimize the animal experiments in compliance with the 3R principle of Replacement, Reduction, and Refinement. To our knowledge, this is the first time an anatomically realistic heterogeneous mouse phantom has been proposed for PAI in pre‐clinical animal imaging and tested its durability over 200 days. [ABSTRACT FROM AUTHOR]

Background: Clinically relevant models that enable certain tasks such as calibration of medical imaging devices or techniques, device validation, training healthcare professionals, and more are vital to research throughout the medical field and are referred to as phantoms. Phantoms range in complexity from a vile of water to complex designs that emulate in vivo properties. Purpose: Specific phantoms that model the lungs have focused on replication of tissue properties but lack replication of the anatomy. This limits the use across multiple imaging modalities and for device testing when anatomical considerations as well as tissue properties are needed. This work reports a lung phantom design utilizing materials that accurately mimic the ultrasound and magnetic resonance imaging (MRI) properties of in vivo lungs and includes relevant anatomical equivalence. Methods: The tissue mimicking materials were selected based on published studies of the materials, through qualitative comparisons of the materials with ultrasound imaging, and quantitative MRI relaxation values. A PVC ribcage was used as the structural support. The muscle/fat combined layer and the skin layer were constructed with various types of silicone with graphite powder added as a scattering agent where appropriate. Lung tissue was mimicked with silicone foam. The pleural layer was replicated by the interface between the muscle/fat layer and the lung tissue layer, requiring no additional material. Results: The design was validated by accurately mimicking the distinct tissue layers expected with in vivo lung ultrasound while maintaining tissue‐mimicking relaxation values in MRI as compared to reported values. Comparisons between the muscle/fat material and in vivo muscle/fat tissue demonstrated a 1.9% difference in T1 relaxation and a 19.8% difference in T2 relaxation. Conclusions: Qualitative US and quantitative MRI analysis verified the proposed lung phantom design for accurate modeling of the human lungs. [ABSTRACT FROM AUTHOR]

Background: The safety and efficacy of proton therapy is currently hampered by range uncertainties. The combination of ultrasound imaging with injectable radiation‐sensitive superheated nanodroplets was recently proposed for in vivo range verification. The proton range can be estimated from the distribution of nanodroplet vaporization events, which is stochastically related to the stopping distribution of protons, as nanodroplets are vaporized by protons reaching their maximal LET at the end of their range. Purpose: Here, we aim to estimate the range estimation precision of this technique. As for any stochastic measurement, the precision will increase with the sample size, that is, the number of detected vaporizations. Thus, we first develop and validate a model to predict the number of vaporizations, which is then applied to estimate the range verification precision for a set of conditions (droplet size, droplet concentration, and proton beam parameters). Methods: Starting from the thermal spike theory, we derived a model that predicts the expected number of droplet vaporizations in an irradiated sample as a function of the droplet size, concentration, and number of protons. The model was validated by irradiating phantoms consisting of size‐sorted perfluorobutane droplets dispersed in an aqueous matrix. The number of protons was counted with an ionization chamber, and the droplet vaporizations were recorded and counted individually using high frame rate ultrasound imaging. After validation, the range estimate precision was determined for different conditions using a Monte Carlo algorithm. Results: A good agreement between theory and experiments was observed for the number of vaporizations, especially for large (5.8 ± 2.2 µm) and medium (3.5 ± 1.1 µm) sized droplets. The number of events was lower than expected in phantoms with small droplets (2.0 ± 0.7 µm), but still within the same order of magnitude. The inter‐phantom variability was considerably larger (up to 30x) than predicted by the model. The validated model was then combined with Monte Carlo simulations, which predicted a theoretical range retrieval precision improving with the square‐root of the number of vaporizations, and degrading at high beam energies due to range straggling. For single pencil beams with energies between 70 and 240 MeV, a range verification precision below 1% of the range required perfluorocarbon concentrations in the order of 0.3‐2.4 µM. Conclusion: We proposed and experimentally validated a model to provide a quick estimate of the number of vaporizations for a given set of conditions (droplet size, droplet concentration, and proton beam parameters). From this model, promising range verification performances were predicted for realistic perfluorocarbon concentrations. These findings are an incentive to move towards preclinical studies, which are critical to assess the achievable droplet distribution in and around the tumor, and hence the in vivo range verification precision. [ABSTRACT FROM AUTHOR]

Background: Age‐related macular degeneration (AMD) is a major cause of irreversible central vision loss. The main reason for lost vision due to AMD is choroidal neovascularization (CNV). In the clinic, current treatments for CNV include photodynamic therapy, laser photocoagulation, and anti‐vascular endothelial growth factor (VEGF) therapy. Purpose: This study evaluates a novel treatment technique combining synchronized nanosecond laser pulses and ultrasound bursts, namely photo‐mediated ultrasound therapy (PUT) as a potential treatment method for CNV, for its efficacy and safety in the treatment of CNV via the experiments in a clinically‐relevant rabbit model in vivo. Methods: CNV was created by subretinal injection of Matrigel and vascular endothelial growth factor (M&V) in 10 New Zealand white rabbits. Six rabbits were used in the PUT group. In the control groups, two rabbits were treated by laser‐only, and two rabbits were treated by ultrasound‐only. The treatment efficacy was evaluated through fundus photography and fluorescein angiography (FA) longitudinally for up to 4 weeks. Rabbits were sacrificed for histopathology 3 months after treatment to examine the safety of PUT. Results: The fluorescein leakage on FA was quantified to longitudinally evaluate treatment outcome. Compared with baseline, the relative intensity index was reduced by 26.57% ± 8.66% at 3 days after treatment, 27.24% ± 6.21% at 1 week after treatment, 27.79% ± 2.61% at 2 weeks after treatment, and 32.12% ± 3.23% at 4 weeks after treatment, all with a statistically significant difference of p < 0.01. The comparison between the relative intensity indexes from the two control groups (laser‐only treatment and ultrasound‐only treatment) did not show any statistically significant difference at all time points. Safety evaluation at 3 months with histopathology demonstrated that the PUT did not result in morphologic changes to the neurosensory retina. Conclusions: This study introduces PUT for the first time for the treatment of CNV. The results demonstrated good efficacy and safety of PUT to treat CNV in a clinically‐relevant rabbit model. With a single session of treatment, PUT can safely reduce the leakage of CNV for at least 1 month after treatment. [ABSTRACT FROM AUTHOR]

Purpose: BUS‐Set is a reproducible benchmark for breast ultrasound (BUS) lesion segmentation, comprising of publicly available images with the aim of improving future comparisons between machine learning models within the field of BUS. Method: Four publicly available datasets were compiled creating an overall set of 1154 BUS images, from five different scanner types. Full dataset details have been provided, which include clinical labels and detailed annotations. Furthermore, nine state‐of‐the‐art deep learning architectures were selected to form the initial benchmark segmentation result, tested using five‐fold cross‐validation and MANOVA/ANOVA with Tukey statistical significance test with a threshold of 0.01. Additional evaluation of these architectures was conducted, exploring possible training bias, and lesion size and type effects. Results: Of the nine state‐of‐the‐art benchmarked architectures, Mask R‐CNN obtained the highest overall results, with the following mean metric scores: Dice score of 0.851, intersection over union of 0.786 and pixel accuracy of 0.975. MANOVA/ANOVA and Tukey test results showed Mask R‐CNN to be statistically significant better compared to all other benchmarked models with a p‐value >0.01. Moreover, Mask R‐CNN achieved the highest mean Dice score of 0.839 on an additional 16 image dataset, that contained multiple lesions per image. Further analysis on regions of interest was conducted, assessing Hamming distance, depth‐to‐width ratio (DWR), circularity, and elongation, which showed that the Mask R‐CNN's segmentations maintained the most morphological features with correlation coefficients of 0.888, 0.532, 0.876 for DWR, circularity, and elongation, respectively. Based on the correlation coefficients, statistical test indicated that Mask R‐CNN was only significantly different to Sk‐U‐Net. Conclusions: BUS‐Set is a fully reproducible benchmark for BUS lesion segmentation obtained through the use of public datasets and GitHub. Of the state‐of‐the‐art convolution neural network (CNN)‐based architectures, Mask R‐CNN achieved the highest performance overall, further analysis indicated that a training bias may have occurred due to the lesion size variation in the dataset. All dataset and architecture details are available at GitHub: https://github.com/corcor27/BUS‐Set, which allows for a fully reproducible benchmark. [ABSTRACT FROM AUTHOR]

It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi‐static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayered perceptron, convolutional neural network, and recurrent neural network, are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected. [ABSTRACT FROM AUTHOR]

Background: As of 2022, breast cancer continues to be the most diagnosed cancer worldwide. This problem persists within the United States as well, as the American Cancer Society has reported that ∼12.5% of women will be diagnosed with invasive breast cancer over the course of their lifetime. Therefore, a clinical need continues to exist to address this disease from a treatment and therapeutic perspective. Current treatments for breast cancer and cancers more broadly include surgery, radiation, and chemotherapy. Adjuncts to these methods have been developed to improve the clinical outcomes for patients. One such adjunctive treatment is mild hyperthermia therapy (MHTh), which has been shown to be successful in the treatment of cancers by increasing effectiveness and reduced dosage requirements for radiation and chemotherapies. MHTh‐assisted treatments can be performed with invasive thermal devices, noninvasive microwave induction, heating and recirculation of extracted patient blood, or whole‐body hyperthermia with hot blankets. Purpose: One common method for inducing MHTh is by using microwave for heat induction and magnetic resonance imaging for temperature monitoring. However, this leads to a complex, expensive, and inaccessible therapy platform. Therefore, in this work we aim to show the feasibility of a novel all‐acoustic MHTh system that uses focused ultrasound (US) to induce heating while also using US tomography (UST) to provide temperature estimates. Changes in sound speed (SS) have been shown to be strongly correlated with temperature changes and can therefore be used to indirectly monitor heating throughout the therapy. Additionally, these SS estimates allow for heterogeneous SS‐corrected phase delays when heating complex and heterogeneous tissue structures. Methods: Feasibility to induce localized heat in tissue was investigated in silico with a simulated breast model, including an embedded tumor using continuous wave US. Here, both heterogenous acoustic and thermal properties were modeled in addition to blood perfusion. We further demonstrate, with ex vivo tissue phantoms, the feasibility of using ring‐based UST to monitor temperature by tracking changes in SS. Two phantoms (lamb tissue and human abdominal fat) with latex tubes containing varied temperature flowing water were imaged. The measured SS of the water at each temperature were compared against values that are reported in literature. Results: Results from ex vivo tissue studies indicate successful tracking of temperature under various phantom configurations and ranges of water temperature. The results of in silico studies show that the proposed system can heat an acoustically and thermally heterogenous breast model to the clinically relevant temperature of 42°C while accounting for a reasonable time needed to image the current cross section (200 ms). Further, we have performed an initial in silico study demonstrating the feasibility of adjusting the transmit waveform frequency to modify the effective heating height at the focused region. Lastly, we have shown in a simpler 2D breast model that MHTh level temperatures can be maintained by adjusting the transmit pressure intensity of the US ring. Conclusions: This work has demonstrated the feasibility of using a 256‐element ring array transducer for temperature monitoring; however, future work will investigate minimizing the difference between measured SS and the values shown in literature. A hypothesis attributes this bias to potential volumetric average artifacts from the ray‐based SS inversion algorithm that was used, and that moving to a waveform‐based SS inversion algorithm will greatly improve the SS estimates. Additionally, we have shown that an all‐acoustic MHTh system is feasible via in silico studies. These studies have indicated that the proposed system can heat a tumor within a heterogenous breast model to 42°C within a narrow time frame. This holds great promise for increasing the accessibility and reducing the complexity of a future all‐acoustic MHTh system. [ABSTRACT FROM AUTHOR]

Background: Mammographic screening has reduced mortality in women through the early detection of breast cancer. However, the sensitivity for breast cancer detection is significantly reduced in women with dense breasts, in addition to being an independent risk factor. Ultrasound (US) has been proven effective in detecting small, early‐stage, and invasive cancers in women with dense breasts. Purpose: To develop an alternative, versatile, and cost‐effective spatially tracked three‐dimensional (3D) US system for whole‐breast imaging. This paper describes the design, development, and validation of the spatially tracked 3DUS system, including its components for spatial tracking, multi‐image registration and fusion, feasibility for whole‐breast 3DUS imaging and multi‐planar visualization in tissue‐mimicking phantoms, and a proof‐of‐concept healthy volunteer study. Methods: The spatially tracked 3DUS system contains (a) a six‐axis manipulator and counterbalanced stabilizer, (b) an in‐house quick‐release 3DUS scanner, adaptable to any commercially available US system, and removable, allowing for handheld 3DUS acquisition and two‐dimensional US imaging, and (c) custom software for 3D tracking, 3DUS reconstruction, visualization, and spatial‐based multi‐image registration and fusion of 3DUS images for whole‐breast imaging. Spatial tracking of the 3D position and orientation of the system and its joints (J1–6) were evaluated in a clinically accessible workspace for bedside point‐of‐care (POC) imaging. Multi‐image registration and fusion of acquired 3DUS images were assessed with a quadrants‐based protocol in tissue‐mimicking phantoms and the target registration error (TRE) was quantified. Whole‐breast 3DUS imaging and multi‐planar visualization were evaluated with a tissue‐mimicking breast phantom. Feasibility for spatially tracked whole‐breast 3DUS imaging was assessed in a proof‐of‐concept healthy male and female volunteer study. Results: Mean tracking errors were 0.87 ± 0.52, 0.70 ± 0.46, 0.53 ± 0.48, 0.34 ± 0.32, 0.43 ± 0.28, and 0.78 ± 0.54 mm for joints J1–6, respectively. Lookup table (LUT) corrections minimized the error in joints J1, J2, and J5. Compound motions exercising all joints simultaneously resulted in a mean tracking error of 1.08 ± 0.88 mm (N = 20) within the overall workspace for bedside 3DUS imaging. Multi‐image registration and fusion of two acquired 3DUS images resulted in a mean TRE of 1.28 ± 0.10 mm. Whole‐breast 3DUS imaging and multi‐planar visualization in axial, sagittal, and coronal views were demonstrated with the tissue‐mimicking breast phantom. The feasibility of the whole‐breast 3DUS approach was demonstrated in healthy male and female volunteers. In the male volunteer, the high‐resolution whole‐breast 3DUS acquisition protocol was optimized without the added complexities of curvature and tissue deformations. With small post‐acquisition corrections for motion, whole‐breast 3DUS imaging was performed on the healthy female volunteer showing relevant anatomical structures and details. Conclusions: Our spatially tracked 3DUS system shows potential utility as an alternative, accurate, and feasible whole‐breast approach with the capability for bedside POC imaging. Future work is focused on reducing misregistration errors due to motion and tissue deformations, to develop a robust spatially tracked whole‐breast 3DUS acquisition protocol, then exploring its clinical utility for screening high‐risk women with dense breasts. [ABSTRACT FROM AUTHOR]

Purpose: To provide a comprehensive set of commissioning tests for clinical implementation of three‐dimensional transvaginal ultrasound (3D TVUS) as a replacement of computed tomography (CT) for applicator reconstruction in gynecologic (GYN) intracavitary high‐dose‐rate brachytherapy (HDRBT) with a multi‐channel vaginal cylinder (MCVC). Methods: We introduce an ultrasound‐compatible "surrogate" vaginal cylinder (SVC) for reconstruction of Elekta's CT‐MR Multi Channel Applicator (MCVC) in 3D TVUS. The MCVC is digitized over the SVC in 3DUS using digital library model overlay. Consulting guidelines from various sources (CPQR, GEC‐ESTRO, AAPM), we identify and describe three tests specific to commissioning the SVC: (1) verification of SVC outer dimensions, (2) source position accuracy of MCVC digitization over the SVC in 3D TVUS, and (3) MRI/US registration error. Results: The SVC outer dimensions (diameter and A‐D marker locations) were well matched to the MCVC, however a 0.6 mm discrepancy in length between cylinder tips was observed. Source position accuracy was within 1 mm (tolerance recommended by CPQR) when reconstructing the MCVC in 3D TVUS. Dice similarity coefficients and target registration error for MRI/3D TVUS registration was similar to MRI/CT registration, which is the clinical standard. Conclusions: These commissioning tests are performed using institutional equipment but provide the framework for any practitioner to repeat in their own setup, to demonstrate safe adoption of the 3D TVUS system for patient treatments. We demonstrate that MRI/US‐based workflow achieves similar source position accuracy and image registration error as standard MRI/CT, which is consistent with standard tolerances. This is a critical step toward replacement of CT with US in GYN HDRBT treatments with the MCVC. [ABSTRACT FROM AUTHOR]

Purpose: Accurate recognition of medullary thyroid carcinoma (MTC) is of great importance in medical diagnosis, as MTC is rare but second‐most malignant thyroid cancers with a high case‐fatality ratio.1 But there is a lower recognition rate on distinguishing MTC from other thyroid nodules in ultrasound images, even by experienced experts. This paper introduces the computer‐aided method to tackle the challenge of recognizing MTC from ultrasound images, including limited MTC samples, and ambiguities among MTC, benign nodules, and papillary thyroid carcinoma (PTC). Methods: The recognition of MTC based on large MTC samples of ultrasound images has never been explored, as only one existing work presented a relevant dataset with a limited 21 MTC samples. This study proposes a novel method for primarily differentiating MTC samples from benign nodules and PTC that is the most common thyroid cancer. Our method is a two‐stage schema with two important components including a cascaded coarse‐to‐fine segmentation network and a knowledge‐based classification network. The cascaded coarse‐to‐fine segmentation network incorporates two U‐Net++ networks for improving the segmentation results of thyroid nodules. Meanwhile, our knowledge‐based classification network extracts and fuses semantic features of solid tissues and calcification for better recognizing the segmented nodules from the ultrasound images. In our experiments, dice similarity coefficient (DSC), intersection over union (IoU), precision, recall, and Hausdorff distance (HD) are adopted for evaluating the segmentation results of thyroid nodules, and accuracy, precision, recall, and F1‐score are used for classification evaluation. Results: We present a well‐annotated dataset including samples of 248 MTC, 240 benign nodules, and 239 PTC. For thyroid nodule segmentation, our designed cascaded segmentation network attains values of 0.776 DSC, 0.689 IoU, 0.778 precision, and 0.821 recall, respectively. By incorporating prior knowledge, our method achieves a mean accuracy of 82.1% in classifying thyroid nodules of MTC, PTC, and benign ones. Especially, our method gains the higher performance in recognizing MTC with an accuracy of 86.8%, compared to nearly 70% diagnosis accuracy of experienced doctors. The experimental results on our Fujian Provincial Hospital dataset further validate the efficiency of our proposed method. Conclusions: Our proposed two‐stage method incorporates pipelines of thyroid nodules segmentation and classification of MTC, individually. Quantitative and qualitative results indicate that our proposed model achieves accurate segmentation of thyroid nodules. The results also validate that our learning‐based framework facilitates the recognition of MTC, which gains better classification accuracy than experienced doctors. [ABSTRACT FROM AUTHOR]

Objectives: To explore the feasibility, efficacy, and safety of ultrasound‐targeted microbubbles destruction (UTMD) assisted dual delivery of beta‐amyloid (Aβ) antibody loaded by microbubbles (MBAβ) and neural stem cells (NSCs) on Alzheimer's disease (AD). Methods: Twenty‐seven APP/PS1 double transgenic mice (Tg mice) and 33 wild‐type mice were used. Wild‐type mice were insonated by diagnostic ultrasound with microbubbles (MB) for 5 min to observe the blood brain barrier (BBB) opening. The survival situation of engrafted NSCs crossing the opened BBB mediated by UTMD in Tg mice was evaluated by in vivo imaging system. We further explored the combination therapy effects of UTMD mediated Aβ antibody and NSCs dual delivery. Tg mice in each group were exposed to diagnostic ultrasound for 5 min once a week for four times, with MB, MBAβ, and/or NSCs administration according to groups. Cognition and memory functions were explored by Morris water maze test, Aβ plaques deposition was evaluated by immunohistochemical, and brain‐derived neurotrophic factor (BDNF) and synaptophysin (SYN) expression were detected by western blot and immunofluorescence. Results: BBB was opened mediated by diagnostic ultrasound with MB, and the duration of opening was about 10 h. The transplanted NSCs survived in Tg mice for no more than 72 h. Compared with control group, the Tg mice in combined delivery of NSCs and Aβ antibody by UTMD group improved memory function and spatial learning with shorter latency to find the platform, longer distance traveled, and longer time spent in targeted quadrant, and more crossing times (p < 0.05). Besides, the combination delivery group promoted the clearance of Aβ plaques compared with control group both in hippocampus (p < 0.01) and cortex (p < 0.05). Moreover, the expression of BDNF in combination delivery group was significantly higher than that in control group and ultrasound‐mediated MB group (p < 0.05). No significant change of SYN was observed in each group. Conclusion: UTMD assisted dual delivery of Aβ antibody and NSCs crossing the BBB into AD mice brain could help to clear Aβ plaques, increase the expression of BDNF, and restore the impaired neural function. This finding may offer potential insight into treatment of AD. [ABSTRACT FROM AUTHOR]

Thermal ablation is a form of hyperthermia in which oncologic control can be achieved by briefly inducing elevated temperatures, typically in the range 50–80°C, within a target tissue. Ablation modalities include high intensity focused ultrasound, radiofrequency ablation, microwave ablation, and laser interstitial thermal therapy which are all capable of generating confined zones of tissue destruction, resulting in fewer complications than conventional cancer therapies. Oncologic control is contingent upon achieving predefined coagulation zones; therefore, intraoperative assessment of treatment progress is highly desirable. Consequently, there is a growing interest in the development of ablation monitoring modalities. The first section of this review presents the mechanism of action and common applications of the primary ablation modalities. The following section outlines the state‐of‐the‐art in thermal dosimetry which includes interstitial thermal probes and radiologic imaging. Both the physical mechanism of measurement and clinical or pre‐clinical performance are discussed for each ablation modality. Thermal dosimetry must be coupled with a thermal damage model as outlined in Section 4. These models estimate cell death based on temperature‐time history and are inherently tissue specific. In the absence of a reliable thermal model, the utility of thermal monitoring is greatly reduced. The final section of this review paper covers technologies that have been developed to directly assess tissue conditions. These approaches include visualization of non‐perfused tissue with contrast‐enhanced imaging, assessment of tissue mechanical properties using ultrasound and magnetic resonance elastography, and finally interrogation of tissue optical properties with interstitial probes. In summary, monitoring thermal ablation is critical for consistent clinical success and many promising technologies are under development but an optimal solution has yet to achieve widespread adoption. [ABSTRACT FROM AUTHOR]

Background: The development of clinically meaningful, objective, and quantitative methods for assessing the performance of ultrasound imaging systems represents a continuing area of interest. One approach has been to image phantoms with randomly distributed spherical voids. Purpose: The objectives of this study were: (1) to explore the potential of using relatively high‐volume fraction random spherical void (RSV) phantoms as an approach for quantitatively assessing the performance of ultrasound imaging systems; (2) to identify potential metrics that can be used to provide quantitative assessments of images obtained from relatively high‐volume fraction RSV phantoms; and (3) to demonstrate changes in the quantitative metrics that can occur as image features are degraded. Methods: A series (10 each) of computer‐simulated RSV phantoms with a range of RSV volume fractions (0.05, 0.15, and 0.25) were generated. To determine the number of image planes necessary to provide robust measurements, a series of consecutive planes (ranging from 1 to 150) within each type of simulated phantom were analyzed. The observed circular cross‐section radii histogram distributions (representing the intersection of each plane with the local distribution of spherical voids) were compared with the theoretical histogram distribution. Simulated phantom images were produced by adding speckle and degradation of imaging system performance was modeled by averaging 1 to 9 neighboring planes to represent increasing elevation plane thicknesses. Quantification of the performance of the imaging system was determined by measuring the: (1) mean number of circular cross‐sections detected per image frame; (2) mean fractional area of circular cross‐sections detected per image frame; (3) agreement of observed circular cross‐section radii histogram distribution with the theoretical distribution (Chi‐square statistic); and (4) contrast and contrast‐to‐noise ratio as a function of observed circular cross‐section radius. Results: Results suggest that analyses of a sufficient number of image planes (providing over approximately 3000 total circular cross‐sectional areas) provides excellent agreement between the observed and theoretical histogram distributions (mean Chi‐square < 0.004). For the 0.15 volume fraction series of simulated RSV phantoms, using 150 image plane analyses, phantom images show decreasing mean number of circle cross‐sections detected per frame (31.5 ± 0.3, 28.4 ± 0.3, 28.2 ± 0.3, 26.3 ± 0.3, and 25.3 ± 0.3); decreasing mean fractional area of circle cross‐sections per frame (0.157 ± 0.002, 0.133 ± 0.001, 0.133 ± 0.001, 0.111 ± 0.001, and 0.108 ± 0.001); and a decreasing agreement with the theoretical histogram distribution of radii (Chi‐square values: 0.070 ± 0.004, 0.140 ± 0.005, 0.149 ± 0.007, 0.379 ± 0.011, and 0.518 ± 0.010) for 1, 3, 5, 7, and 9 plane averages, respectively. Contrast and contrast‐to‐noise measurements as a function of observed circular cross‐section radius also demonstrate marked changes with simulated image degradation. Conclusions: Results of this simulation study suggest that analyses of images obtained from relatively high‐density RSV phantoms may offer a promising approach for assessing ultrasound imaging systems. The proposed measurements appear to provide reproducible, robust, quantitative metrics that can be compared with corresponding theoretical values to provide quantifiable, objective metrics of imaging system performance. [ABSTRACT FROM AUTHOR]

Purpose: To present a novel methodical approach to compare visibility of percutaneous needles in ultrasound images. Methods: A motor‐driven rotation platform was used to gradually change the needle angle while capturing image data. Data analysis was automated using block‐matching‐based registration, with a tracking and refinement step. Every 25 frames, a Hough transform was used to improve needle alignments after large rotations. The method was demonstrated by comparing three commercial needles (14G radiofrequency ablation, RFA; 18G Trocar; 22G Chiba) and six prototype needles with different sizes, materials, and surface conditions (polished, sand‐blasted, and kerfed), within polyvinyl alcohol phantom tissue and ex vivo bovine liver models. For each needle and angle, a contrast‐to‐noise ratio (CNR) was determined to quantify visibility. CNR values are presented as a function of needle type and insertion angle. In addition, the normalized area under the (CNR‐angle) curve was used as a summary metric to compare needles. Results: In phantom tissue, the first kerfed needle design had the largest normalized area of visibility and the polished 1 mm diameter stainless steel needle the smallest (0.704 ± 0.199 vs. 0.154 ± 0.027, p < 0.01). In the ex vivo model, the second kerfed needle design had the largest normalized area of visibility, and the sand‐blasted stainless steel needle the smallest (0.470 ± 0.190 vs. 0.127 ± 0.047, p < 0.001). As expected, the analysis showed needle visibility peaks at orthogonal insertion angles. For acute or obtuse angles, needle visibility was similar or reduced. Overall, the variability in needle visibility was considerably higher in livers. Conclusion: The best overall visibility was found with kerfed needles and the commercial RFA needle. The presented methodical approach to quantify ultrasound visibility allows comparisons of (echogenic) needles, as well as other technological innovations aiming to improve ultrasound visibility of percutaneous needles, such as coatings, material treatments, and beam steering approaches. [ABSTRACT FROM AUTHOR]

Purpose: The incidence of thyroid cancer has significantly increased in the last few decades. However, diagnosis of the thyroid nodules is labor and time intensive for radiologists and strongly depends on the personal experience of the radiologists. In this pursuit, the present study envisaged to develop a deep learning‐based computer‐aided diagnosis (CAD) method that enabled the automatic detection and classification of suspicious thyroid nodules in order to reduce the unnecessary fine‐needle aspiration biopsy. Methods: The CAD method consisted of two main parts: detecting the location of thyroid nodules using a multiscale detection network and classifying the detected thyroid nodules by an attention‐based classification network. Results: The performance of the proposed method was evaluated and compared with that of other state‐of‐the‐art deep learning methods and experienced radiologists. The proposed detection method outperformed three other detection architectures (average precision, 82.1% vs. 78.3%, 77.2%, and 74.8%). Moreover, the classification method showed a superior performance compared with four other state‐of‐the‐art classification networks (accuracy, 94.8% vs. 91.2%, 85.0%, 80.8%, and 72.1%) and that by experienced radiologists (mean value of area under the curve, 0.941 vs. 0.833). Conclusions: Our study verified the high efficiency of the proposed detection method. The findings can help improve the diagnostic performance of radiologists. However, the developed CAD system requires more training and evaluation in a large‐population study. [ABSTRACT FROM AUTHOR]

Purpose: Cardiovascular disease (CVD) is a leading cause of death worldwide, with coronary artery disease (CAD) accounting for nearly half of all CVD deaths. The current gold standard for CAD diagnosis is catheter coronary angiography (CCA), an invasive, expensive procedure. Computed tomography coronary angiography (CTCA) represents an attractive non‐invasive alternative to CCA, however, CTCA requires gated acquisition of CT data during periods of minimal cardiac motion (quiescent periods) to avoid non‐diagnostic scans. Current gating methods either expose patients to high levels of radiation (retrospective gating) or lead to high rates of non‐diagnostic scans (prospective gating) due to the challenge of predicting cardiac quiescence based on ECG alone. Alternatively, ultrasound (US) imaging has been demonstrated as an effective indicator of cardiac quiescence, however, ultrasound transducers produce prominent streak artifacts that disrupt CTCA scans. In this study, a proof‐of‐concept array transducer with improved CT‐compatibility was developed for utilization in an integrated US‐CTCA system. Methods: Alternative materials were tested radiographically and acoustically to replace the radiopaque acoustic backings utilized in low frequency (1–4 MHz) cardiac US transducers. The results of this testing were used to develop alternative acoustic backings consisting of varying concentrations of aluminum oxide in an epoxy matrix via simulations. On the basis of these simulations, single element test transducers designed to operate at 2.5 MHz were fabricated, and the performance of these devices was characterized via acoustic and radiographic testing with micro‐computed tomography (micro‐CT). Finally, a first proof‐of‐concept cardiac phased array transducer was developed and its US imaging performance was evaluated. Micro‐CT images of the developed US array with improved CT‐compatibility were compared with those of a conventional array. Results: Materials testing with micro‐CT identified an acoustic backing with a measured radiopacity of 1008 HU, more than an order of magnitude lower than that of the acoustic backing (24,000 HU) typically used in cardiac transducers operating in the 1–4 MHz range. When utilized in a simulated transducer design, this acoustic backing yielded a −6‐dB fractional bandwidth of 57%, similar to the 54% bandwidth of the transducer with the radiopaque acoustic backing. The developed 2.5 MHz, single element transducer based on these simulations exhibited a fractional bandwidth of 51% and signal‐to‐noise ratio (SNR) of 14.7 dB. Finally, the array transducer developed with the acoustic backing having decreased radiopacity exhibited a 56% fractional bandwidth and 10.4 dB single channel SNR, with penetration depth >10 cm in phantom and in vivo imaging using the full array. Conclusions: The first attempt at developing a CT‐compatible ultrasound transducer is described. The developed CT‐compatible transducer exhibits improved radiographic compatibility relative to conventional cardiac array transducers with similar SNR, bandwidth, and penetration depth for US imaging, according to phantom and in vivo cardiac imaging. A CT‐compatible US transducer might be used to identify cardiac quiescence and prospectively gate CTCA acquisition, reducing challenges associated with current gating approaches, specifically relatively high rates of non‐diagnostic scans for prospective ECG gating and high radiation dose for retrospective gating. [ABSTRACT FROM AUTHOR]

Purpose: Ultrasound‐targeted microbubble destruction (UTMD) has been widely applied to enhance chemotherapy of tumors, yet few studies have focused on the metastatic potential induced by UTMD. This study aimed to explore the metastasis of VX2 tumors after treatment with UTMD and chemotherapy. Methods: Forty‐four New Zealand rabbits bearing subcutaneous VX2 tumors were enrolled for the treatment of UTMD with chemotherapy. For UTMD, the tumors were insonated using two pulsing protocols of diagnostic ultrasound (DUS, VINNO and ECARE) with a mechanical index (MI) of 0.29–0.33, tone burst of 8.0 cycles, and frequencies of 3–4 MHz. A total dose of 2 ml SonoVue® was injected intermittently during 10‐min UTMD exposure. The combination therapy was treated using doxorubicin (DOX, 2 mg/kg) and DUS, while the tumors treated using DOX only served as the control. Tumor size was measured using the tumor volume formula. Survival time was observed until animal death or the end of the study (120 days). Specific organs (lung, liver, kidney, and brain) were removed for metastatic evaluation. Results: There were no statistical differences in overall metastasis classification and individual organ metastases among all groups (P > 0.05). The tumor growth rate only showed inhibition on the 5th day (P < 0.01). The survival time did not demonstrate any significant difference between UTMD and chemotherapy only (P > 0.05). Conclusions: UTMD using long‐pulse DUS with commercial microbubbles did not pose a risk of metastasis enhancement in DOX chemotherapy. [ABSTRACT FROM AUTHOR]

Purpose: Performing a transrectal ultrasound (TRUS) prostate biopsy is at the heart of the current prostate cancer detection procedure. With today's two‐dimensional (2D) live ultrasound (US) imaging equipment, this task remains complex due to the poor visibility of cancerous tissue on TRUS images and the limited anatomical context available in the 2D TRUS plane. This paper presents a rigid 2D/3DUS registration method for navigated prostate biopsy. This allows continuous localization of the biopsy trajectory during the procedure. Methods: We proposed an organ‐based approach to achieve real‐time rigid registration without the need for any probe localization device. The registration method combines image similarity and geometric proximity of detected features. Additions to our previous work include a multi‐level approach and the use of a rejection rate favouring the best matches. Their aim is to increase the accuracy and time performances. These modifications and their in‐depth evaluation on real clinical cases and comparison to this previous work are described. We performed static and dynamic evaluations along biopsy trajectories on a very large amount of data acquired under uncontrolled routine conditions. The computed transforms are compared to a ground truth obtained either from corresponding manually detected fiducials or from an already evaluated registration method. Results: All results show that the current method outperforms its previous version, both in terms of accuracy (the average error reported here is 12 to 17% smaller depending on the experiment) and processing time (from 20 to 60 times faster compared to the previous implementation). The dynamic registration experiment demonstrates that the method can be successfully used for continuous tracking of the biopsy location w.r.t the prostate at a rate that varies between 5 and 15 Hz. Conclusions: This work shows that on the fly 2D/3DUS registration can be performed very efficiently on biopsy trajectories. This allows us to plan further improvements in prostate navigation and a clinical transfer. [ABSTRACT FROM AUTHOR]

Purpose: Current prostate brachytherapy uses transrectal ultrasound images for implant guidance, where contours of the prostate and organs‐at‐risk are necessary for treatment planning and dose evaluation. This work aims to develop a deep learning‐based method for male pelvic multi‐organ segmentation on transrectal ultrasound images. Methods: We developed an anchor‐free mask convolutional neural network (CNN) that consists of three subnetworks, that is, a backbone, a fully convolutional one‐state object detector (FCOS), and a mask head. The backbone extracts multi‐level and multi‐scale features from an ultrasound (US) image. The FOCS utilizes these features to detect and label (classify) the volume‐of‐interests (VOIs) of organs. In contrast to the design of a previously investigated mask regional CNN (Mask R‐CNN), the FCOS is anchor‐free, which can capture the spatial correlation of multiple organs. The mask head performs segmentation on each detected VOI, where a spatial attention strategy is integrated into the mask head to focus on informative feature elements and suppress noise. For evaluation, we retrospectively investigated 83 prostate cancer patients by fivefold cross‐validation and a hold‐out test. The prostate, bladder, rectum, and urethra were segmented and compared with manual contours using the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), mean surface distance (MSD), center of mass distance (CMD), and volume difference (VD). Results: The proposed method visually outperforms two competing methods, showing better agreement with manual contours and fewer misidentified speckles. In the cross‐validation study, the respective DSC and HD95 results were as follows for each organ: bladder 0.75 ± 0.12, 2.58 ± 0.7 mm; prostate 0.93 ± 0.03, 2.28 ± 0.64 mm; rectum 0.90 ± 0.07, 1.65 ± 0.52 mm; and urethra 0.86 ± 0.07, 1.85 ± 1.71 mm. For the hold‐out tests, the DSC and HD95 results were as follows: bladder 0.76 ± 0.13, 2.93 ± 1.29 mm; prostate 0.94 ± 0.03, 2.27 ± 0.79 mm; rectum 0.92 ± 0.03, 1.90 ± 0.28 mm; and urethra 0.85 ± 0.06, 1.81 ± 0.72 mm. Segmentation was performed in under 5 seconds. Conclusion: The proposed method demonstrated fast and accurate multi‐organ segmentation performance. It can expedite the contouring step of prostate brachytherapy and potentially enable auto‐planning and auto‐evaluation. [ABSTRACT FROM AUTHOR]

Purpose: Cardiac boundary segmentation of echocardiographic images is important for cardiac function assessment and disease diagnosis. However, it is challenging to segment cardiac ventricles due to the low contrast‐to‐noise ratio and speckle noise of the echocardiographic images. Manual segmentation is subject to interobserver variability and is too slow for real‐time image‐guided interventions. We aim to develop a deep learning‐based method for automated multi‐structure segmentation of echocardiographic images. Methods: We developed an anchor‐free mask convolutional neural network (CNN), termed Cardiac‐SegNet, which consists of three subnetworks, that is, a backbone, a fully convolutional one‐state object detector (FCOS) head, and a mask head. The backbone extracts multi‐level and multi‐scale features from endocardium image. The FOCS head utilizes these features to detect and label the region‐of‐interests (ROIs) of the segmentation targets. Unlike the traditional mask regional CNN (Mask R‐CNN) method, the FCOS head is anchor‐free and can model the spatial relationship of the targets. The mask head utilizes a spatial attention strategy, which allows the network to highlight salient features to perform segmentation on each detected ROI. For evaluation, we investigated 450 patient datasets by a five‐fold cross‐validation and a hold‐out test. The endocardium (LVEndo) and epicardium (LVEpi) of the left ventricle and left atrium (LA) were segmented and compared with manual contours using the Dice similarity coefficient (DSC), Hausdorff distance (HD), mean absolute distance (MAD), and center‐of‐mass distance (CMD). Results: Compared to U‐Net and Mask R‐CNN, our method achieved higher segmentation accuracy and fewer erroneous speckles. When our method was evaluated on a separate hold‐out dataset at the end diastole (ED) and the end systole (ES) phases, the average DSC were 0.952 and 0.939 at ED and ES for the LVEndo, 0.965 and 0.959 at ED and ES for the LVEpi, and 0.924 and 0.926 at ED and ES for the LA. For patients with a typical image size of 549 × 788 pixels, the proposed method can perform the segmentation within 0.5 s. Conclusion: We proposed a fast and accurate method to segment echocardiographic images using an anchor‐free mask CNN. [ABSTRACT FROM AUTHOR]

Purpose: Despite the physical benefits of protons over conventional photon radiation in cancer treatment, range uncertainties impede the ability to harness the full potential of proton therapy. While monitoring the proton range in vivo could reduce the currently adopted safety margins, a routinely applicable range verification technique is still lacking. Recently, phase‐change nanodroplets were proposed for proton range verification, demonstrating a reproducible relationship between the proton range and generated ultrasound contrast after radiation‐induced vaporization at 25°C. In this study, previous findings are extended with proton irradiations at different temperatures, including the physiological temperature of 37°C, for a novel nanodroplet formulation. Moreover, the potential to modulate the linear energy transfer (LET) threshold for vaporization by varying the degree of superheat is investigated, where the aim is to demonstrate vaporization of nanodroplets directly by primary protons. Methods: Perfluorobutane nanodroplets with a shell made of polyvinyl alcohol (PVA‐PFB) or 10,12‐pentacosadyinoic acid (PCDA‐PFB) were dispersed in polyacrylamide hydrogels and irradiated with 62 MeV passively scattered protons at temperatures of 37°C and 50°C. Nanodroplet transition into echogenic microbubbles was assessed using ultrasound imaging (gray value and attenuation analysis) and optical images. The proton range was measured independently and compared to the generated contrast. Results: Nanodroplet design proved crucial to ensure thermal stability, as PVA‐shelled nanodroplets dramatically outperformed their PCDA‐shelled counterpart. At body temperature, a uniform radiation response proximal to the Bragg peak is attributed to nuclear reaction products interacting with PVA‐PFB nanodroplets, with the 50% drop in ultrasound contrast being 0.17 mm ± 0.20 mm (mean ± standard deviation) in front of the proton range. Also at 50°C, highly reproducible ultrasound contrast profiles were obtained with shifts of −0.74 mm ± 0.09 mm (gray value analysis), −0.86 mm ± 0.04 mm (attenuation analysis) and −0.64 mm ± 0.29 mm (optical analysis). Moreover, a strong contrast enhancement was observed near the Bragg peak, suggesting that nanodroplets were sensitive to primary protons. Conclusions: By varying the degree of superheat of the nanodroplets' core, one can modulate the intensity of the generated ultrasound contrast. Moreover, a submillimeter reproducible relationship between the ultrasound contrast and the proton range was obtained, either indirectly via the visualization of secondary reaction products or directly through the detection of primary protons, depending on the degree of superheat. The potential of PVA‐PFB nanodroplets for in vivo proton range verification was confirmed by observing a reproducible radiation response at physiological temperature, and further studies aim to assess the nanodroplets' performance in a physiological environment. Ultimately, cost‐effective online or offline ultrasound imaging of radiation‐induced nanodroplet vaporization could facilitate the reduction of safety margins in treatment planning and enable adaptive proton therapy. [ABSTRACT FROM AUTHOR]

Purpose: Image‐guided needle biopsy of small, detectable lesions is crucial for early‐stage diagnosis, treatment planning, and management of breast cancer. High‐resolution positron emission mammography (PEM) is a dedicated functional imaging modality that can detect breast cancer independent of breast tissue density, but anatomical context and real‐time needle visualization are not yet available to guide biopsy. We propose a mechatronic guidance system integrating an ultrasound (US)‐guided core‐needle biopsy (CNB) with high‐resolution PEM localization to improve the spatial sampling of breast lesions. This paper presents the benchtop testing and phantom studies to evaluate the accuracy of the system and its constituent components for targeted PEM‐US‐guided biopsy under simulated high‐resolution PEM localization. Methods: A mechatronic guidance system was developed to operate with the Radialis PEM system and a conventional US system. The system includes a user‐operated guidance arm and end‐effector biopsy device, integrating a US transducer and CNB gun, with its needle focused on a remote center of motion (RCM). Custom software modules were developed to track, display, and guide the end‐effector biopsy device. Registration of the mechatronic guidance system to a simulated PEM detector plate was performed using a landmark‐based method. Testing was performed with fiducials positioned in the peripheral and central regions of the simulated detector plate and registration error was quantified. Breast phantom experiments were performed under ideal detection and localization to evaluate for bias in the end‐effector biopsy device. The accuracy of the complete mechatronic guidance system to perform targeted breast biopsy was assessed using breast phantoms with simulated lesions. Three‐dimensional positioning error was quantified, and principal component analysis assessed for directional trends in 3D space within 95% prediction intervals. Targeted breast biopsies with test phantoms were performed and an overall in‐plane needle targeting error was quantified. Results: The mean registration errors were 0.63 mm (N = 44) and 0.73 mm (N = 72) in the peripheral and central regions of the simulated PEM detector plate, respectively. A 3D 95% prediction ellipsoid shows an error volume <2.0 mm in diameter, centered on the mean registration error. Under ideal detection and localization, targets <1.0 mm in diameter can be sampled with 95% confidence. The complete mechatronic guidance system was able to successfully spatially sample simulated breast lesions, 4 mm and 6 mm in diameter and height (N = 20) in known 3D positions in the PEM image coordinate space. The 3D positioning error was 0.85 mm (N = 20) with 0.64 mm in‐plane and 0.44 mm cross‐plane component errors. Targeted breast biopsies resulted in a mean in‐plane needle targeting error of 1.08 mm (N = 15) allowing for targets 1.32 mm in radius to be sampled with 95% confidence. Conclusions: We demonstrated the utility of our mechatronic guidance system for targeted breast biopsy under high‐resolution PEM localization. Breast phantom studies showed the ability to accurately guide, position, and target breast lesions with the accuracy to spatially sample targets <3.0 mm in diameter with 95% confidence. Future work will integrate the developed system with the Radialis PEM system toward combined PEM‐US‐guided breast biopsy. [ABSTRACT FROM AUTHOR]

Purpose: The combination of laser and ultrasound can significantly improve the efficiency of thrombolysis through an enhanced cavitation effect. We developed a fiber optics‐based laser‐ultrasound thrombolysis device and tested the feasibility and efficiency of this technology for restoring blood flow in an in vitro blood clot model. Methods: An in vitro blood flow‐clot model was setup, and then an endovascular laser thrombolysis system was combined with high‐intensity focused ultrasound to remove the clot. The laser and ultrasound pulses were synchronized and delivered to the blood clot concurrently. The laser pulses of 532 nm were delivered to the blood clot endovascularly through an optical fiber, whereas the ultrasound pulses of 0.5 MHz were applied noninvasively to the same region. Effectiveness of thrombolysis was evaluated by the ability to restore blood flow, which was monitored by ultrasound Doppler. Results: As laser powers increased, the ultrasound threshold pressures for effective thrombolysis decreased. For laser fluence levels of 0, 2, and 4 mJ/cm2, the average negative ultrasound threshold pressures were 1.26 ± 0.114, 1.05 ± 0.181, and 0.59 ± 0.074 MPa, respectively. The periods of time needed to achieve effective thrombolysis were measured at 0.8, 2, and 4 mJ/cm2 laser fluence levels and 0.42, 0.70, and 0.98 MPa negative ultrasound pressures. In general, thrombolysis could be achieved more rapidly with higher laser powers or ultrasound pressures. Conclusions: Effective thrombolysis can be achieved by combining endovascular laser with noninvasive ultrasound at relatively low power and pressure levels, which can potentially improve both the treatment efficiency and safety. [ABSTRACT FROM AUTHOR]

Introduction: Ultrasound (US) guidance of the prostate has long been conducted using a transabdominal (TA) approach. More recently, a transperineal (TP) approach has been made available for image guidance. Our aim was to determine if both methods produced similar alignments within the same patients. Materials and methods: We utilized two clinical US image guidance (IG) systems (Elekta Clarity and Best BAT). The B‐mode Acquisition and Targeting USIG system is a bi‐planar, so‐called 2.5D USIG system, that is acquired TA. Clarity is a 3D US system that generates a volumetric 3D US data set and US‐derived IG contours that are coregistered to the planning CT images. The probe is oriented in the sagittal plane against the perineum (TP). After positioning the patient for treatment using the TP USIG, we maintained the position defined by Clarity tracking and then acquired a TA‐based USIG. The two US‐based methods of localizing the prostate (TA vs TP) were compared via Bland–Altman (BA) statistical analysis to determine if there was alignment agreement between methods. Results: The BA test for all 101 patients, 2093 fractions resulted in 95% confidence intervals (upper and lower limits of the BA test) of 0.6 mm in LR, 0.9 mm in AP and 1.0 mm in SI. The bias between the two systems was calculated as 0.03, 0.02, and 0.03 mm in LR, AP, and SI. Conclusions: Both systems resulted in statistically equivalent targeting positions for the prostate. Because of the unique intrafraction, real‐time motion tracking capability of the TP system, this solution represents a unique extension to the previously reported clinical benefits of a TA approach by providing assurance of the prostate remaining in the treatment field during beam‐on. [ABSTRACT FROM AUTHOR]

Purpose: FLASH radiotherapy (FLASH‐RT) is a novel irradiation modality with ultra‐high dose rates (>40 Gy/s) that have shown tremendous promise for its ability to enhance normal tissue sparing while maintaining comparable tumor cell eradication toconventional radiotherapy (CONV‐RT). Due to its extremely high dose rates, clinical translation of FLASH‐RT is hampered by risky delivery and current limitations in dosimetric devices, which cannot accurately measure, in real time, dose at deeper tissue. This work aims to investigate ionizing radiation acoustic imaging (iRAI) as a promising image‐guidance modality for real‐time deep tissue dose measurements during FLASH‐RT. The underlying hypothesis is that iRAI can enable mapping of dose deposition with respect to surrounding tissue with a single linear accelerator (linac) pulse precision in real time. In this work, the relationship between iRAI signal response and deposited dose was investigated as well as the feasibility of using a proof‐of‐concept dual‐modality imaging system of ultrasound and iRAI for treatment beam co‐localization with respect to underlying anatomy. Methods: Two experimental setups were used to study the feasibility of iRAI for FLASH‐RT using 6 MeV electrons from a modified Varian Clinac. First, experiments were conducted using a single element focused transducer to take a series of point measurements in a gelatin phantom, which was compared with independent dose measurements using GAFchromic film. Secondly, an ultrasound and iRAI dual‐modality imaging system utilizing a phased array transducer was used to take coregistered two‐dimensional (2D) iRAI signal amplitude images as well as ultrasound B‐mode images, to map the dose deposition with respect to surrounding anatomy in an ex vivo rabbit liver model with a single linac pulse precision. Results: Using a single element transducer, iRAI measurements showed a highly linear relationship between the iRAI signal amplitude and the linac dose per pulse (r2 = 0.9998) with a repeatability precision of 1% and a dose resolution error <2.5% in a homogenous phantom when compared to GAFchromic film dose measurements. These phantom results were used to develop a calibration curve between the iRAI signal response and the delivered dose per pulse. Subsequently, a normalized depth dose curve was generated that agreed with film measurements with an RMSE of 0.0243, using correction factors to account for deviations in measurement conditions with respect to calibration. Experiments on the ex‐vivo rabbit liver model demonstrated that a 2D iRAI image could be generated successfully from a single linac pulse, which was fused with the B‐mode ultrasound image to provide information about the beam position with respect to surrounding anatomy in real time. Conclusion: This work demonstrates the potential of using iRAI for real‐time deep tissue dosimetry in FLASH‐RT. Our results show that iRAI signals are linear with dose and can accurately map the delivered radiation dose with respect to soft tissue anatomy. With its ability to measure dose for individual linac pulses at any location within surrounding soft tissue while identifying where that dose is being delivered anatomically in real time, iRAI can be an indispensable tool to enable safe and efficient clinical translation of FLASH‐RT. [ABSTRACT FROM AUTHOR]

Purpose: Computer‐aided diagnosis (CAD) systems assist in solving subjective diagnosis problems that typically rely on personal experience. A CAD system has been developed to differentiate malignant thyroid nodules from benign thyroid nodules in ultrasound images based on deep learning methods. The diagnostic performance was compared between the CAD system and the experienced attending radiologists. Methods: The ultrasound image dataset for training the CAD system included 651 malignant nodules and 386 benign nodules while the database for testing included 422 malignant nodules and 128 benign nodules. All the nodules were confirmed by pathology results. In the proposed CAD system, a support vector machine (SVM) is used for classification and fused features which combined the deep features extracted by a convolutional neural network (CNN) with the hand‐crafted features such as the histogram of oriented gradient (HOG), local binary patterns (LBP), and scale invariant feature transform (SIFT) were obtained. The optimal feature subset was formed by selecting these fused features based on the maximum class separation distance and used as the training sample for the SVM. Results: The accuracy, sensitivity, and specificity of the CAD system were 92.5%, 96.4%, and 83.1%, respectively, which were higher than those of the experienced attending radiologists. The areas under the ROC curves of the CAD system and the attending radiologists were 0.881 and 0.819, respectively. Conclusions: The CAD system for thyroid nodules exhibited a better diagnostic performance than experienced attending radiologists. The CAD system could be a reliable supplementary tool to diagnose thyroid nodules using ultrasonography. Macroscopic features in ultrasound images, such as the margins and shape of thyroid nodules, could influence the diagnostic efficiency of the CAD system. [ABSTRACT FROM AUTHOR]

Purpose: Rupture of an arterosclerotic plaque in the carotid artery is a major cause of stroke. Biomechanical analysis of plaques is under development aiming to aid the clinician in the assessment of plaque vulnerability. Patient‐specific three‐dimensional (3D) geometry assessment of the carotid artery, including the bifurcation, is required as input for these biomechanical models. This requires a high‐resolution, 3D, noninvasive imaging modality such as ultrasound (US). In this study, a high‐resolution two‐dimensional (2D) linear array in combination with a magnetic probe tracking device and automatic segmentation method was used to assess the geometry of the carotid artery. The advantages of using this system over a 3D ultrasound probe are its higher resolution (spatial and temporal) and its larger field of view. Methods: A slow sweep (v = ± 5 mm/s) was made over the subject's neck so that the full geometry of the bifurcated geometry of the carotid artery is captured. An automated segmentation pipeline was developed. First, the Star‐Kalman method was used to approximate the center and size of the vessels for every frame. Images were filtered with a Gaussian high‐pass filter before conversion into the 2D monogenic signals, and multiscale asymmetry features were extracted from these data, enhancing low lateral wall‐lumen contrast. These images, in combination with the initial ellipse contours, were used for an active deformable contour model to segment the vessel lumen. To segment the lumen–plaque boundary, Otsu's automatic thresholding method was used. Distension of the wall due to the change in blood pressure was removed using a filter approach. Finally, the contours were converted into a 3D hexahedral mesh for a patient‐specific solid mechanics model of the complete arterial wall. Results: The method was tested on 19 healthy volunteers and on 3 patients. The results were compared to manual segmentation performed by three experienced observers. Results showed an average Hausdorff distance of 0.86 mm and an average similarity index of 0.91 for the common carotid artery (CCA) and 0.88 for the internal and external carotid artery. For the total algorithm, the success rate was 89%, in 4 out of 38 datasets the ICA and ECA were not sufficient visible in the US images. Accurate 3D hexahedral meshes were successfully generated from the segmented images. Conclusions: With this method, a subject‐specific biomechanical model can be constructed directly from a hand‐held 2D US measurement, within 10 min, with a minimal user input. The performance of the proposed segmentation algorithm is comparable to or better than algorithms previously described in literature. Moreover, the algorithm is able to segment the CCA, ICA, and ECA including the carotid bifurcation in transverse B‐mode images in both healthy and diseased arteries. [ABSTRACT FROM AUTHOR]

Purpose: Deformable registration of ultrasound (US) and contrast enhanced computed tomography (CECT) images are essential for quantitative comparison of ablation boundaries and dimensions determined using these modalities. This comparison is essential as stiffness‐based imaging using US has become popular and offers a nonionizing and cost‐effective imaging modality for monitoring minimally invasive microwave ablation procedures. A sensible manual registration method is presented that performs the required CT‐US image registration. Methods: The two‐dimensional (2D) virtual CT image plane that corresponds to the clinical US B‐mode was obtained by "virtually slicing" the 3D CT volume along the plane containing non‐anatomical landmarks, namely points along the microwave ablation antenna. The initial slice plane was generated using the vector acquired by rotating the normal vector of the transverse (i.e., xz) plane along the angle subtended by the antenna. This plane was then further rotated along the ablation antenna and shifted along with the direction of normal vector to obtain similar anatomical structures, such as the liver surface and vasculature that is visualized on both the CT virtual slice and US B‐mode images on 20 patients. Finally, an affine transformation was estimated using anatomic and non‐anatomic landmarks to account for distortion between the colocated CT virtual slice and US B‐mode image resulting in a final registered CT virtual slice. Registration accuracy was measured by estimating the Euclidean distance between corresponding registered points on CT and US B‐mode images. Results: Mean and SD of the affine transformed registration error was 1.85 ± 2.14 (mm), computed from 20 coregistered data sets. Conclusions: Our results demonstrate the ability to obtain 2D virtual CT slices that are registered to clinical US B‐mode images. The use of both anatomical and non‐anatomical landmarks result in accurate registration useful for validating ablative margins and comparison to electrode displacement elastography based images. [ABSTRACT FROM AUTHOR]

Purpose: As a crucial step in accessing the kidney in several minimally invasive interventions, percutaneous renal access (PRA) practicality and safety may be improved through the fusion of computed tomography (CT) and ultrasound (US) data. This work aims to assess the potential of a surface‐based registration technique and establish an optimal US acquisition protocol to fuse two‐dimensional (2D) US and CT data for image‐guided PRA. Methods: Ten porcine kidney phantoms with fiducial markers were imaged using CT and three‐dimensional (3D) US. Both images were manually segmented and aligned. In a virtual environment, 2D contours were extracted by slicing the 3D US kidney surfaces and using usual PRA US‐guided views, while the 3D CT kidney surfaces were transformed to simulate positional variability. Surface‐based registration was performed using two methods of the iterative closest point algorithm (point‐to‐point, ICP1; and point‐to‐plane, ICP2), while four acquisition variants were studied: (a) use of single‐plane (transverse, SPT; or longitudinal, SPL) vs bi‐plane views (BP); (b) use of different kidney's coverage ranges acquired by a probe's sweep; (c) influence of sweep movements; and (d) influence of the spacing between consecutive slices acquired for a specific coverage range. Results: BP view showed the best performance (TRE = 2.26 mm) when ICP2 method, a wide kidney coverage range (20°, with slices spaced by 5°), and a large sweep along the central longitudinal view were used, showing a statistically similar performance (P = 0.097) to a full 3D US surface registration (TRE = 2.28 mm). Conclusions: An optimal 2D US acquisition protocol was evaluated. Surface‐based registration, using multiple slices and specific sweep movements and views, is here suggested as a valid strategy for intraoperative image fusion using CT and US data, having the potential to be applied to other image modalities and/or interventions. [ABSTRACT FROM AUTHOR]

Purpose: An intraluminal coronary stent is a metal scaffold deployed in a stenotic artery during percutaneous coronary intervention (PCI). In order to have an effective deployment, a stent should be optimally placed with regard to anatomical structures such as bifurcations and stenoses. Intravascular ultrasound (IVUS) is a catheter‐based imaging technique generally used for PCI guiding and assessing the correct placement of the stent. A novel approach that automatically detects the boundaries and the position of the stent along the IVUS pullback is presented. Such a technique aims at optimizing the stent deployment. Methods: The method requires the identification of the stable frames of the sequence and the reliable detection of stent struts. Using these data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multiscale quantization of the signal to symbols using the Symbolic Aggregate approXimation algorithm. Results: The technique was extensively validated on a set of 103 IVUS of sequences of in vivo coronary arteries containing metallic and bioabsorbable stents acquired through an international multicentric collaboration across five clinical centers. The method was able to detect the stent position with an overall F‐measure of 86.4%, a Jaccard index score of 75% and a mean distance of 2.5 mm from manually annotated stent boundaries, and in bioabsorbable stents with an overall F‐measure of 88.6%, a Jaccard score of 77.7 and a mean distance of 1.5 mm from manually annotated stent boundaries. Additionally, a map indicating the distance between the lumen and the stent along the pullback is created in order to show the angular sectors of the sequence in which the malapposition is present. Conclusions: Results obtained comparing the automatic results vs the manual annotation of two observers shows that the method approaches the interobserver variability. Similar performances are obtained on both metallic and bioabsorbable stents, showing the flexibility and robustness of the method. [ABSTRACT FROM AUTHOR]

Purpose: The motion of the common carotid artery tissue layers along the vessel axis during the cardiac cycle, observed in ultrasound imaging, is associated with the presence of established cardiovascular risk factors. However, the vast majority of the (semi‐)automatic methods devised to measure this so‐called "longitudinal kinetics" phenomenon are based on the tracking of a single point, thus failing to capture the overall — and potentially inhomogeneous — motion of the entire arterial wall. The aim of this work is to introduce a motion tracking a framework to simultaneously extract the temporal trajectory of a large collection of points (several hundred) horizontally aligned and spanning the entire exploitable width of the image, thus providing a dense motion field. Method: The only action required from the user is to indicate the left and right borders of the region to be processed. A previously validated contour segmentation method is used to position one point in the arterial wall in each column of the image. Between two consecutive frames, the radial motion of each point is predetermined by the position of the segmentation contours. The longitudinal motion, which is the main focus of the present work, is determined in two steps. First, a series of independent block matching operations are carried out for all the tracked points. Here, the displacement of each point is not determined yet, instead the similarity map is stored. Then, an original dynamic‐programming approach is exploited to regularize the collection of similarity maps and estimate the globally optimal motion over the entire vessel wall. Sixty‐two atherosclerotic participants at high cardiovascular risk were involved in this study. Method training and validation was performed with 20 and 42 participants, respectively. The amplitude‐independent index σX was introduced to quantitate the motion inhomogeneity across the length of the artery. Results: A dense displacement field, describing the longitudinal motion of the carotid far wall over time, was extracted from all participants. For each cine‐loop, the method was evaluated against manual reference tracings performed on three local points, and showed a good accuracy, with an average absolute error (± STD) of 150 (±163) μm. It also demonstrated an overall greater robustness compared to a previously validated method based on single‐point motion tracking. For all the 62 participants, the analyzed region had, in average, a width of 24.2 mm, involving the simultaneous tracking of 357 points along 151 temporal frames, and requiring a total computational time of 68 s. Analyzing the inhomogeneity of the carotid artery motion showed a strong correlation between σX and the presence of coronary artery disease (β‐coefficient = 0.586, P = 0.003). Conclusions: To the best of our knowledge, this is the first time that a method is specifically proposed to assess the dense motion field corresponding to the longitudinal kinetics of the carotid far wall. This approach has potential to evaluate the homogeneity (or lack thereof) of the wall dynamics. The proposed method has promising performances to improve the analysis of arterial longitudinal motion and the understanding of the underlying patho‐physiological parameters. [ABSTRACT FROM AUTHOR]

Purpose: Compensation for respiratory motion is important during abdominal cancer treatments. In this work we report the results of the 2015 MICCAI Challenge on Liver Ultrasound Tracking and extend the 2D results to relate them to clinical relevance in form of reducing treatment margins and hence sparing healthy tissues, while maintaining full duty cycle. Methods: We describe methodologies for estimating and temporally predicting respiratory liver motion from continuous ultrasound imaging, used during ultrasound‐guided radiation therapy. Furthermore, we investigated the trade‐off between tracking accuracy and runtime in combination with temporal prediction strategies and their impact on treatment margins. Results: Based on 2D ultrasound sequences from 39 volunteers, a mean tracking accuracy of 0.9 mm was achieved when combining the results from the 4 challenge submissions (1.2 to 3.3 mm). The two submissions for the 3D sequences from 14 volunteers provided mean accuracies of 1.7 and 1.8 mm. In combination with temporal prediction, using the faster (41 vs 228 ms) but less accurate (1.4 vs 0.9 mm) tracking method resulted in substantially reduced treatment margins (70% vs 39%) in contrast to mid‐ventilation margins, as it avoided non‐linear temporal prediction by keeping the treatment system latency low (150 vs 400 ms). Acceleration of the best tracking method would improve the margin reduction to 75%. Conclusions: Liver motion estimation and prediction during free‐breathing from 2D ultrasound images can substantially reduce the in‐plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non‐linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath‐hold and gated approaches, and increase treatment efficiency and safety. [ABSTRACT FROM AUTHOR]

Purpose: The ability to monitor intrafractional tumor motion is essential for radiation therapy of thoracic and abdominal tumors. This study aims to develop a method to track lung tumor motion using intrafractional continuous ultrasound (US) and periodic cone‐beam projection images (CBPI). Methods: Time‐sequenced b‐mode US and CBPI data were extracted from the data acquired with the Clarity® and XVI platforms on an Elekta linac, respectively. The data were synchronized through a video capture card (VCE‐PRO, IMPERX Inc.) which was triggered by the XVI system. In this way, a system was configured to allow real‐time acquisition of the diaphragm position synchronized with periodic acquisition of the lung tumor position. Feasibility of the system was demonstrated by acquiring synchronized data on an in‐house motion platform with embedded spheres of different materials and US images of the diaphragm on 5 volunteers of various body sizes. Finally, ultrasound b‐mode images and CBPI were also acquired simultaneously from 3 lung cancer patients. Results: Diaphragm motion monitoring under free breathing (FB) was successful with intracostal US imaging. We observed that diaphragm visualization decreased with the increase in the body size of the volunteer. The US system was able to track the motion as small as 2 mm in the phantom. The intrafractional CBPI acquired during VMAT delivery was successfully synchronized with US acquisition in a phantom study. Collected patient data showed a significant correlation between diaphragm motion, an internal surrogate monitored by US, and the tumor motion in superior–inferior (SI) direction monitored by XVI (P ˂ 0.0001). Conclusions: The feasibility of real‐time lung tumor motion tracking in SI direction with continuous ultrasound and periodic CBPI was demonstrated. The real‐time estimation of the target position from the two streams for lung cancer patients would enable respiration gating or tracking during SBRT. [ABSTRACT FROM AUTHOR]

Purpose: As a noninvasive and nonionizing radiation, ultrasound can be focused remotely, transferring acoustic energy deep in the body, thereby addressing the penetration depth barrier of the light‐based therapies. In cancer therapy, the effectiveness of ultrasound can be enhanced by utilizing nanomaterials that exhibit sonosensitizing properties called as nanosonosensitizers. The gold nanoparticle (AuNP) has been recently presented as a potent nanosonosensitizer with the potential to simultaneously enhance both the thermal and mechanical interactions of ultrasound with the tissue of the human body. Accordingly, this paper attempts to evaluate the in vivo antitumor efficiency of ultrasound in combination with AuNP. Methods: BALB/c mice‐bearing CT26 colorectal tumor model was intraperitoneally injected with AuNPs and then subjected to ultrasound irradiation (1 MHz; 2 W/cm2; 10 min) for three sessions. Furthermore, [18F]FDG (2‐deoxy‐2‐[18F]fluoro‐ d‐glucose) positron‐emission tomography (PET) imaging was performed and the radiomic features from different feature categorizes were extracted to quantify the tumors’ phenotype. Results: The tumors were dramatically shrunk and the mice appeared healthy over 21 days of study span without the evidence of relapse. The animals treated with AuNP + ultrasound exhibited an obvious decline in tumor metabolic parameters such as standard uptake value (SUV), total lesion glycolysis (TLG), and metabolic tumor volume (MTV) compared to other treatment groups. Conclusion: These findings support the use of AuNP as a potent sonosensitizing agent with the potential to use the thermal and mechanical effects of ultrasound so as to cause damage to the focused tumor site, resulting in an improved antitumor efficacy. [ABSTRACT FROM AUTHOR]

Purpose: Permanent breast seed implantation (PBSI) is a promising radiotherapy technique for early‐stage breast cancer, completed in a single visit by permanently implanting 103Pd seeds using needles inserted through a template and guided by two‐dimensional (2D) ultrasound (US). However, operator dependence has been highlighted as a limitation of this procedure. Consequently, we propose and have developed an intraoperative guidance system using three‐dimensional (3D) US and an instrumented mechanical arm to provide intraoperative 3D imaging and needle template tracking. Methods: A mechatronic 3D US scanner reconstructs a 3D image from 150 2D images. A tracked mechanical arm mounted to the scanner locates four fiducial points on the template, registering the template to the 3D image. 3D reconstruction was validated for linear and volumetric measurement accuracy using phantoms of known geometry. In vivo breast US image quality was evaluated in a healthy volunteer. The encoded arm was calibrated and validated using a jig with divots at known locations relative to the scanner and the scanner registered to the 3D US image using intersecting strings in a fluid‐filled test jig. Template registration accuracy was assessed using a machined test jig. Tracking accuracy was assessed in a liquid medium by comparing tracked and imaged needle tip positions. Finally, the system was used to guide a mock procedure in a patient‐specific phantom and micro‐CT imaging used to evaluate its accuracy. Results: Geometric validation showed median distances within ±1.1% of expected values and volumetric validation showed differences of ≤4.1%. Tracking arm point measurements showed an average error of 0.43 mm and 3D US volume registration showed target registration error ≤0.9 mm. Mean template registration accuracy in each axis of translation/rotation was ≤1.3 mm/1.0°. Mean needle‐targeting error was 2.5 mm and 1.6° for needle tips and trajectories, respectively. Mean needle tip and angular errors of the phantom procedure were 2.1 mm and 2.6°. Modeled seed displacement of the phantom procedure showed mean error of 2.6 mm and a maximum of 3.8 mm. Conclusions: A 3D US guidance system for PBSI has been developed. Benchtop performance and image quality in volunteer scans are satisfactory. A phantom PBSI procedure was successfully delivered using the system with maximum seed error within dosimetric benchmarks (<5 mm). Translation of the device into the clinic is forthcoming. [ABSTRACT FROM AUTHOR]

Purpose: Magnetic resonance‐guided focused ultrasound (MRgFUS) is emerging as a treatment alternative for osteoid osteoma and painful bone metastases. This study describes a new simulation platform that predicts the distribution of heat generated by MRgFUS when applied to bone tissue. Methods: Calculation of the temperature distribution was performed using two mathematical models. The first determined the propagation and absorption of acoustic energy through each medium, and this was performed using a multilayered approximation of the Rayleigh integral method. The ultrasound energy distribution derived from these equations could then be converted to heat energy, and the second mathematical model would then use the heat generated to determine the final temperature distribution using a finite‐difference time‐domain application of Pennes’ bio‐heat transfer equation. Anatomical surface geometry was generated using a modified version of a mesh‐based semiautomatic segmentation algorithm, and both the acoustic and thermodynamic models were calculated using a parallelized algorithm running on a graphics processing unit (GPU) to greatly accelerate computation time. A series of seven porcine experiments were performed to validate the model, comparing simulated temperatures to MR thermometry and assessing spatial, temporal, and maximum temperature accuracy in the soft tissue. Results: The parallelized algorithm performed acoustic and thermodynamic calculations on grids of over 108 voxels in under 30 s for a simulated 20 s of heating and 40 s of cooling, with a maximum time per calculated voxel of less than 0.3 μs. Accuracy was assessed by comparing the soft tissue thermometry to the simulation in the soft tissue adjacent to bone using four metrics. The maximum temperature difference between the simulation and thermometry in a region of interest around the bone was measured to be 5.43 ± 3.51°C average absolute difference and a percentage difference of 16.7%. The difference in heating location resulted in a total root‐mean‐square error of 4.21 ± 1.43 mm. The total size of the ablated tissue calculated from the thermal dose approximation in the simulation was, on average, 67.6% smaller than measured from the thermometry. The cooldown was much faster in the simulation, where it decreased by 14.22 ± 4.10°C more than the thermometry in 40 s after sonication ended. Conclusions: The use of a Rayleigh‐based acoustic model combined with a discretized bio‐heat transfer model provided a rapid three‐dimensional calculation of the temperature distribution through bone and soft tissue during MRgFUS application, and the parallelized GPU algorithm provided the computational speed that would be necessary for an intraoperative treatment planning software platform. [ABSTRACT FROM AUTHOR]