Conventional Ultrasound Imaging Research Articles

Imaging performance of portable and conventional ultrasound imaging technologies for ophthalmic applications.

The aim of this study was to evaluate the imaging capabilities of Butterfly iQ with conventional ophthalmic (piezoelectric) ultrasound (COU) for ophthalmic imaging. Custom phantom molds were designed and imaged with Butterfly iQ and COU to compare spatial resolution capabilities. To evaluate the clinical imaging performance of Butterfly iQ and COU, a survey containing pathological conditions from human subjects, imaged with both Butterfly iQ and COU probes, was given to three retina specialists and graded on image detail, resolution, quality, and diagnostic confidence on a ten-point Likert scale. Kruskal-Wallis analysis was performed for survey responses. Butterfly iQ and COU had comparable capabilities for imaging small axial and lateral phantom features (down to 0.1 mm) of high and low acoustic reflectivity. One of three retina specialists demonstrated a statistically significant preference for COU related to resolution, detail, and diagnostic confidence, but the remaining graders showed no significant preference for Butterfly iQ or COU across all sample images presented. The emergence of portable ultrasound probes offers an affordable alternative to COU technologies with comparable qualitative imaging resolution down to 0.1 mm. These findings suggest the value to further study the use of portable ultrasound systems and their utility in routine eye care.

Abstract PO5-19-11: A novel portable breast cancer screening method - initial institutional experience

Abstract Background: Breast ultrasound (US) is a remarkable supplementary tool alongside mammography and magnetic resonance imaging (MRI) for the detection of breast lesions. US imaging is a fast, reliable method to identify and localize breast pathology. Innovative technologies that aim to timely identification and prompt diagnosis of suspicious lesions in breast cancer screening remain a clinical need. ATUSA is a B-type rechargeable US, shaped as a wearable device. It uses a linear array of transducers with water acting as a coupling medium that recreates a 3-dimensional image of the scanned breast. The ATUSA portability makes this device a practical breast screening method during a healthcare office visit. We report the initial feasibility of ATUSA to detect breast lesions in an outpatient setting from a trial in progress. Methodology: Interim prospective data from 35 female patients presenting with a mass of equal or greater than 1 cm in size, with a baseline handheld US image and pathology reports that evidenced a cancer diagnosis or a lesion suspicious of malignancy. Breast cancer types included invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS) and invasive lobular carcinoma (ILC), invasive mammary carcinoma (IMC) with mixed ductal and lobular features and atypical lobular hyperplasia (ALH). ATUSA and baseline handheld reference US images were cross-referenced from June 2022 to June 2023. Basic demographics, clinical characteristics, mammogram and/or MRI, and pathology reports were included in the analysis. Patients were followed up for a 30-day period to note occurrence of any adverse event (AE). Institutional Review Board (IRB) approved this study (protocol H-42616). Results: The 35 patients had a mean age of 57 years (range: 25 – 79), mean BMI 29.6 kg/m2 (range: 18.1 – 49.3). Mammographic breast density was fibroglandular tissue 15 (42.8%), followed by heterogeneously dense (HD) 13 (3.7%), mixed HD and fibroglandular 1 (2.8%), mildly dense 1 (2.8%), extremely dense 2 (5.7%), and extremely fatty 1 (2.8%). Breast sizes were 1 (2.8 %) A cup, 11 (31.4%) B cups, 1 (2.8%) B – C cups, 5 (14.2%) C cups, 1 (2.8%) C – D cups, 13 (37.1%) D cups, and 3 (8.5%) DD cups. The 35 scanned lesions had a diagnosis (compared with final resected pathology) classified as IDC 18 (51.4 %), DCIS 2 (5.7 %), mixed IDC and DCIS 6 (17.1 %), ILC 3 (8.5 %), IMC with mixed features 2 (5.7 %), IMC with DCIS 1 (2.8 %), ALH 1 (2.8 %), and 2 (5.7 %) benign masses. ATUSA successfully identified 17/35 (48.5 %) lesions from this group as malignant, from which 4 were hard to distinguish due to shadowing artifact and 3 were underestimated in size. A total of 15 (42.8 %) malignant lesions were missed by ATUSA. No AEs related to the ATUSA device were reported. Conclusion: The ATUSA provides the means to produce a good quality US image during a clinic visit or where a facility lacks conventional US imaging. When compared to the handheld US, ATUSA can be safely used in the outpatient setting without the need of trained specialists but requires further development and testing to match conventional breast imaging. Table 1. Basic clinical characteristics & demographics Citation Format: Margarita Riojas-Barrett, Jessica Montalvan, Mary Bajomo, Ivan Marin, Quan Dang Nguyen, Karla A Sepulveda, Elizabeth Bonefas, Alastair Thompson, Stacey Carter. A novel portable breast cancer screening method - initial institutional experience [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-19-11.

Value of conventional ultrasound and shear‑wave elastography in the assessment of mesenteric lymphadenitis in a paediatric population.

The present retrospective study was designed to explore the value of conventional ultrasound (US) and Virtual Touch Tissue Imaging and Quantification (VTIQ) in the assessment of mesenteric lymphadenitis (ML) in a paediatric population. A total of 103 patients with ML and 60 healthy paediatric patients were examined. VTIQ was performed to assess mesenteric lymph node (MLN) stiffness via shear-wave velocity (SWV). Univariate and multivariate logistic regression analyses were conducted to reveal independent variables for the identification of ML. The diagnostic performance of US, and US combined with VTIQ, were compared. All the quantitative VTIQ parameters (including the SWVMean, SWVMax and SWVMin) were significantly greater for MLNs in the control group than for MLNs in the ML group (all P<0.001). The SWV values in the control group were nearly 2-fold greater than that in the ML group. According to the multivariate logistic regression analysis, the longest diameter [odds ratio (OR)=6.042; P=0.046] was revealed to be the strongest independent predictor for ML, followed by the CRP level (OR=2.310; P<0.001) and the SWVMean (OR=0.106; P<0.001). According to the receiver operating characteristic analysis, the area under the curve (AUC) for US combined with VTIQ was 0.890 (95% CI: 0.831-0.949) with a greater sensitivity of 91.26% and a greater specificity of 86.67% than that for US alone (AUC: 0.798; 95% CI: 0.724-0.872; sensitivity: 79.61%; specificity: 80.00%). A significant negative correlation between increased VTIQ parameters and ML was observed. Utilizing VTIQ to assess MLN stiffness offers a non-invasive, convenient, reliable and reproducible approach for identifying mesenteric lymphadenopathy.

Demonstration Study of Reflector-Based Volumetric Speed-of-Sound Imaging With Linear Ultrasound Arrays

Conventional B-mode ultrasound imaging has difficulty in delineating homogeneous soft tissues with similar acoustic impedances, as the reflectivity depends on the acoustic impedance at the interface. As a quantitative imaging biomarker sensitive to alteration of biomechanical properties, speed-of-sound (SoS) holds promising potential for tissue and disease differentiation such as delineation of different breast tissue types with similar acoustic impedance. Compared to two-dimensional (2D) SoS images, three-dimensional (3D) volumetric SoS images achieved through a full-angle ultrasound scan can reveal more intricate morphological structures of tissues; however, they generally require a ring transducer. In this study, we introduce a 3D SoS reconstruction system that utilizes hand-held linear arrays instead. This system employs a passive reflector positioned opposite the linear arrays, serving as an echogenic reference for time-of-flight (ToF) measurements, and a high-definition camera to track the location corresponding to each group of transmit-receive data. To merge these two streams of ToF measurements and location tracking, a voxel-based reconstruction algorithm is implemented. Experimental results with gelatin phantom and ex vivo tissue have demonstrated the stability of our proposed method. Moreover, the results underscore the potential of this system as a complementary diagnostic modality, particularly in the context of diseases such as breast cancer.

Applications of the Photocatalytic and Photoacoustic Properties of Gold Nanorods in Contrast-Enhanced Ultrasound and Photoacoustic Imaging.

The applications of ultrasound imaging are often limited due to low contrast, which arises from the comparable acoustic impedance of normal tissues and disease sites. To improve the low contrast, we propose a contrast agent called gas-generating laser-activatable nanorods for contrast enhancement (GLANCE), which enhances ultrasound imaging contrast in two ways. First, GLANCE absorbs near-infrared lasers and generates nitrogen gas bubbles through the photocatalytic function of gold nanorods and photolysis of azide compounds. These gas bubbles decrease the acoustic impedance and highlight the injection site from the surrounding tissues. Second, GLANCE exhibits photoacoustic properties owing to the gold nanorods that emit photoacoustic signals upon laser irradiation. Additionally, GLANCE offers several benefits for biomedical applications such as nanometer-scale size, adjustable optical absorption, and biocompatibility. These distinctive features of GLANCE would overcome the limitations of conventional ultrasound imaging and facilitate the accurate diagnosis of various diseases.

Reliability of Panoramic Ultrasound in Assessing Rectus Femoris Size, Shape, and Brightness: An Inter-Examiner Study.

Extended field-of-view ultrasound (US) imaging, also known as panoramic US, represents a technical advance that allows for complete visualization of large musculoskeletal structures, which are often limited in conventional 2D US images. Currently, there is no evidence examining whether the experience of examiners influences muscle shape deformations that may arise during the glide of the transducer in panoramic US acquisition. As no studies using panoramic US have analyzed whether two examiners with differing levels of experience might obtain varying scores in size, shape, or brightness during the US assessment of the rectus femoris muscle, our aim was to analyze the inter-examiner reliability of panoramic US imaging acquisition in determining muscle size, shape, and brightness between two examiners. Additionally, we sought to investigate whether the examiners' experience plays a significant role in muscle deformations during imaging acquisition by assessing score differences. Shape (circularity, aspect ratio, and roundness), size (cross-sectional area and perimeter), and brightness (mean echo intensity) were analyzed in 39 volunteers. Intraclass correlation coefficients (ICCs), standard error of measurements (SEM), minimal detectable changes (MDC), and coefficient of absolute errors (CAE%) were calculated. All parameters evaluated showed no significant differences between the two examiners (p > 0.05). Panoramic US proved to be reliable, regardless of examiner experience, as no deformations were observed. Further research is needed to corroborate the validity of panoramic US by comparing this method with gold standard techniques.

COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES

Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods.

Ultrasound Entropy Imaging Based on the Kernel Density Estimation: A New Approach to Hepatic Steatosis Characterization.

In this paper, we present the kernel density estimation (KDE)-based parallelized ultrasound entropy imaging and apply it for hepatic steatosis characterization. A KDE technique was used to estimate the probability density function (PDF) of ultrasound backscattered signals. The estimated PDF was utilized to estimate the Shannon entropy to construct parametric images. In addition, the parallel computation technique was incorporated. Clinical experiments of hepatic steatosis were conducted to validate the feasibility of the proposed method. Seventy-two participants and 204 patients with different grades of hepatic steatosis were included. The experimental results show that the KDE-based entropy parameter correlates with log10 (hepatic fat fractions) measured by magnetic resonance spectroscopy in the 72 participants (Pearson's r = 0.52, p < 0.0001), and its areas under the receiver operating characteristic curves for diagnosing hepatic steatosis grades ≥ mild, ≥moderate, and ≥severe are 0.65, 0.73, and 0.80, respectively, for the 204 patients. The proposed method overcomes the drawbacks of conventional histogram-based ultrasound entropy imaging, including limited dynamic ranges and histogram settings dependence, although the diagnostic performance is slightly worse than conventional histogram-based entropy imaging. The proposed KDE-based parallelized ultrasound entropy imaging technique may be used as a new ultrasound entropy imaging method for hepatic steatosis characterization.

First-in-human diagnostic study of hepatic steatosis with computed ultrasound tomography in echo mode

BackgroundNon-alcoholic fatty liver disease is rapidly emerging as the leading global cause of chronic liver disease. Efficient disease management requires low-cost, non-invasive techniques for diagnosing hepatic steatosis accurately. Here, we propose quantifying liver speed of sound (SoS) with computed ultrasound tomography in echo mode (CUTE), a recently developed ultrasound imaging modality adapted to clinical pulse-echo systems. CUTE reconstructs the spatial distribution of SoS by measuring local echo phase shifts when probing tissue at varying steering angles in transmission and reception.MethodsIn this first-in-human phase II diagnostic study, we evaluated the liver of 22 healthy volunteers and 22 steatotic patients. We used conventional B-mode ultrasound images and controlled attenuation parameter (CAP) to diagnose the presence (CAP≥ 280 dB/m) or absence (CAP < 248 dB/m) of steatosis in the liver. A fully integrated convex-probe CUTE implementation was developed on the ultrasound system to estimate liver SoS. We investigated its diagnostic value via the receiver operating characteristic (ROC) analysis and correlation to CAP measurements.ResultsWe show that liver CUTE-SoS estimates correlate strongly (r = −0.84, p = 8.27 × 10−13) with CAP values and have 90.9% (95% confidence interval: 84–100%) sensitivity and 95.5% (81–100%) specificity for differentiating between normal and steatotic livers (area under the ROC curve: 0.93–1.0).ConclusionsOur results demonstrate that liver CUTE-SoS is a promising quantitative biomarker for diagnosing liver steatosis. This is a necessary first step towards establishing CUTE as a new quantitative add-on to diagnostic ultrasound that can potentially be as versatile as conventional ultrasound imaging.

Value of inversion imaging to diagnosis in differentiating malignant from benign breast masses

BackgroundWe aimed to evaluate the added value of inversion imaging in differentiating between benign and malignant breast masses when combined with the Breast Imaging Reporting and Data System (BI-RADS).MethodsA total of 364 patients with 367 breast masses (151 benign and 216 malignant) who underwent conventional ultrasound and inversion imaging prior to breast surgery were included. A 5-point inversion score (IS) scale was proposed based on the masses’ internal echogenicity and distribution characteristics in the inversion images. The combination of IS and BI-RADS was compared with BI-RADS alone to evaluate the value of inversion imaging for breast mass diagnosis. The diagnostic performance of the BI-RADS and its combination with IS for breast masses were analyzed using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).ResultsThe IS for malignant breast masses (3.96 ± 0.77) was significantly higher than benign masses (2.58 ± 0.98) (P < 0.001). The sensitivity, specificity, accuracy, PPV, and NPV of BI-RADS were 86.1%, 81.5%, 84.2%, 86.9%, and 80.4%, respectively, and an AUC was 0.909. By compared with BI-RADS, 72 breast masses were downgraded from suspected malignancy to benign, and 6 masses were upgraded from benign to suspected malignancy. Thus, the specificity was increased from 81.5 to 84.8%, it allows 72 benign masses avoid biopsy.ConclusionThe combination of inversion imaging with BI-RADS can effectively improve the diagnostic efficacy of breast masses, and inversion imaging could help benign masses avoid biopsy.

A Data-Driven Approach for Estimating Temperature Variations Based on B-mode Ultrasound Images and Changes in Backscattered Energy.

Thermal treatments that use ultrasound devices as a tool have as a key point the temperature control to be applied in a specific region of the patient's body. This kind of procedure requires caution because the wrong regulation can either limit the treatment or aggravate an existing injury. Therefore, determining the temperature in a region of interest in real-time is a subject of high interest. Although this is still an open problem, in the field of ultrasound analysis, the use of machine learning as a tool for both imaging and automated diagnostics are application trends. In this work, a data-driven approach is proposed to address the problem of estimating the temperature in regions of a B-mode ultrasound image as a supervised learning problem. The proposal consists in presenting a novel data modeling for the problem that includes information retrieved from conventional B-mode ultrasound images and a parametric image built based on changes in backscattered energy (CBE). Then, we compare the performance of classic models in the literature. The computational results presented that, in a simulated scenario, the proposed approach that a Gradient Boosting model would be able to estimate the temperature with a mean absolute error of around 0.5°C, which is acceptable in practical environments both in physiotherapic treatments and high intensity focused ultrasound (HIFU).

A theragnostic HIFU transducer and system for inherently registered imaging and therapy.

One big challenge with high intensity focused ultrasound (HIFU) is the difficulty in accurate prediction of focal location due to the complex wave propagation in heterogeneous medium even with imaging guidance. This study aims to overcome this by combining therapy and imaging guidance with one single HIFU transducer using the vibro-acoustography (VA) strategy. Based on the VA imaging method, a HIFU transducer consisting of 8 transmitting elements was proposed for therapy planning, treatment and evaluation. Inherent registration between the therapy and imaging created unique spatial consistence in HIFU transducer's focal region in the above three procedures. Performance of this imaging modality was first evaluated through in-vitro phantoms. In-vitro and ex-vivo experiments were then designed to demonstrate the proposed dual-mode system's ability in conducting accurate thermal ablation. Point spread function of the HIFU-converted imaging system had a full wave half maximum of about 1.2 mm in both directions at a transmitting frequency of 1.2 MHz, which outperformed the conventional ultrasound imaging (3.15 MHz) in in-vitro situation. Image contrast was also tested on the in-vitro phantom. Various geometric patterns could be accurately 'burned out' on the testing objects by the proposed system both in vitro and ex vivo. Implementation of imaging and therapy with one HIFU transducer in this manner is feasible and it has potential as a novel strategy for addressing the long-standing problem in the HIFU therapy, possibly pushing this non-invasive technique forward towards wider clinical applications.

Risk stratification model for incidentally detected gallbladder polyps: A multicentre study

PurposeWe aimed to develop a 4-level risk stratification model using a scoring system based on conventional ultrasound to improve the diagnosis of gallbladder polyp. MethodPatients with histopathologically confirmed gallbladder polyps were consecutively recruited from three medical centres. Conventional ultrasound findings and clinical characteristics were acquired prior to cholecystectomy. Risk factors for neoplastic and malignant polyps were used to build a risk stratification system via interobserver agreement and multivariate logistic regression analysis. The model was retrospectively trained using 264 pre-surgical samples and prospectively validated using 106 pre-surgical samples. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and malignant polyp rate. ResultsIn total, 370 patients (mean age, 51.68 ± 14.41 years, 156 men) were enrolled in this study. Size (≥12 mm), shape (oblate or round), single, vascularity, gallbladder stone or sludge were considered risk factors for neoplastic polyps. Size (≥14 mm), shape (oblate), single, disrupted gallbladder wall, and gallbladder stone or sludge were risk factors for malignant polyps (all p < 0.05). In the scoring system, the sensitivity, specificity, and AUC of score ≥ 9 in diagnosing neoplastic polyps were 0.766, 0.788, and 0.876 respectively; and the sensitivity, specificity, and AUC of score ≥ 15 in diagnosing malignant polyps were 0.844, 0.926, and 0.949 respectively. In our model, the malignancy rates at the four levels were 0 % (0/24), 1.28 % (2/156), 9.26 % (5/54), and 70.37 % (38/54), respectively. ConclusionsThe 4-level risk stratification model based on conventional ultrasound imaging showed excellent performance in classifying gallbladder polyps.

Deep learning radiomics for prediction of axillary lymph node metastasis in patients with clinical stage T1-2 breast cancer.

This study investigates whether deep learning radiomics of conventional ultrasound images can predict preoperative axillary lymph node (ALN) status in patients with clinical stages T1-2 breast cancer (BC). This study retrospectively analyzed the preoperative ultrasound data of 892 patients with BC, who were classified into training (n=535), validation (n=178), and test (n=179) cohorts. Linear combinations of the selected features were weighted by their coefficients to obtain the predicted score. Then, deep learning radiomic features were extracted from the ultrasound images to evaluate the ALN status. Receiver-operating characteristic curves were drawn, followed by the calculation of the area under the curve (AUC) to assess the accuracy of the prediction model in predicting axillary lymph node metastasis (ALNM) in the three cohorts. Deep learning radiomics combined with radiomics and clinical parameters was the optimal diagnostic predictor of the ALN status in the absence and presence of ALNM, with the AUC of 0.920 (95% confidence interval: 0.872 and 0.968, respectively). Additionally, this combination could also differentiate low-load ALNM [N + (1-2)] from heavy-load ALNM with ≥3 positive nodes [N + (≥3)] in the test cohort, with the AUC of 0.819 (95% confidence interval: 0.568 and 1.00, respectively). Conclusively, deep learning radiomics of ultrasound images is a non-invasive approach to predicting preoperative ALNM in BC.

In Vivo Ultrasound Molecular Imaging in the Evaluation of Complex Ovarian Masses: A Practical Guide to Correlation with Ex Vivo Immunohistochemistry.

Ovarian cancer is the fifth leading cause of cancer-related deaths in women and the most lethal gynecologic cancer. It is curable when discovered at an early stage, but usually remains asymptomatic until advanced stages. It is crucial to diagnose the disease before it metastasizes to distant organs for optimal patient management. Conventional transvaginal ultrasound imaging offers limited sensitivity and specificity in the ovarian cancer detection. With molecularly targeted ligands addressing targets, such as kinase insert domain receptor (KDR), attached to contrast microbubbles, ultrasound molecular imaging (USMI) can be used to detect, characterize and monitor ovarian cancer at a molecular level. In this article, the authors propose a standardized protocol is proposed for the accurate correlation between in- vivo transvaginal KDR-targeted USMI and ex vivo histology and immunohistochemistry in clinical translational studies. The detailed procedures of in vivo USMI and ex vivo immunohistochemistry are described for four molecular markers, CD31 and KDR with a focus on how to enable the accurate correlation between in vivo imaging findings and ex vivo expression of the molecular markers, even if not the entire tumor could can be imaged by USMI, which is not an uncommon scenario in clinical translational studies. This work aims to enhance the workflow and the accuracy of characterization of ovarian masses on transvaginal USMI using histology and immunohistochemistry as reference standards, which involves sonographers, radiologists, surgeons, and pathologists in a highly collaborative research effort of USMI in cancer.

Comparison of the diagnostic value of contrast-enhanced ultrasound combined with conventional ultrasound versus magnetic resonance imaging in malignant non-mass breast lesions.

To compare the diagnostic value of contrast-enhanced ultrasound (CEUS)+conventional ultrasound vs MRI for malignant non-mass breast lesions (NMLs). A total of 109 NMLs detected by conventional ultrasound and examined by both CEUS and MRI were retrospectively analysed. The characteristics of NMLs in CEUS and MRI were noted, and agreement between the two modalities was analysed. Sensitivity, specificity, positive-predictive value (PPV), negative-predictive value (NPV), and area under the curve (AUC) of the two methods for diagnosing malignant NMLs were calculated in the overall sample and subgroups of different sizes(<10 mm, 10-20 mm, >20 mm). A total of 66 NMLs detected by conventional ultrasound showed non-mass enhancement in MRI. Agreement between ultrasound and MRI was 60.6%. Probability of malignancy was higher when there was agreement between the two modalities. In the overall group, the sensitivity, specificity, PPV, and NPV of the two methods were 91.3%, 71.4%, 60%, 93.4% and 100%, 50.4%, 59.7%, 100%, respectively. The diagnostic performance of CEUS+conventional ultrasound was better than that of MRI (AUC: 0.825 vs 0.762, p = 0.043). The specificity of both methods decreased as lesion size increased, but sensitivity did not change. There was no significant difference between the AUCs of the two methods in the size subgroups (p > 0.05). The diagnostic performance of CEUS+conventional ultrasound may be better than that of MRI for NMLs detected by conventional ultrasound. However, the specificity of both methods decrease significantly as lesion size increases. This is the first study to compare the diagnostic performance of CEUS+conventional ultrasound vs that of MRI for malignant NMLs detected by conventional ultrasound. While CEUS+conventional ultrasound appears to be superior to MRI, subgroup analysis suggests that diagnostic performance is poorer for larger NMLs.

An integrated radiomics nomogram based on conventional ultrasound improves discriminability between fibroadenoma and pure mucinous carcinoma in breast.

To evaluate the ability of integrated radiomics nomogram based on ultrasound images to distinguish between breast fibroadenoma (FA) and pure mucinous carcinoma (P-MC). One hundred seventy patients with FA or P-MC (120 in the training set and 50 in the test set) with definite pathological confirmation were retrospectively enrolled. Four hundred sixty-four radiomics features were extracted from conventional ultrasound (CUS) images, and radiomics score (Radscore) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different models were developed by a support vector machine (SVM), and the diagnostic performance of the different models was assessed and validated. A comparison of the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) was performed to evaluate the incremental value of the different models. Finally, 11 radiomics features were selected, and then Radscore was developed based on them, which was higher in P-MC in both cohorts. In the test group, the clinic + CUS + radiomics (Clin + CUS + Radscore) model achieved a significantly higher area under the curve (AUC) value (AUC = 0.86, 95% CI, 0.733-0.942) when compared with the clinic + radiomics (Clin + Radscore) (AUC = 0.76, 95% CI, 0.618-0.869, P > 0.05), clinic + CUS (Clin + CUS) (AUC = 0.76, 95% CI, 0.618-0.869, P< 0.05), Clin (AUC = 0.74, 95% CI, 0.600-0.854, P< 0.05), and Radscore (AUC = 0.64, 95% CI, 0.492-0.771, P< 0.05) models, respectively. The calibration curve and DCA also suggested excellent clinical value of the combined nomogram. The combined Clin + CUS + Radscore model may help improve the differentiation of FA from P-MC.

Estimators of tissue absorption parameters power-law prefactor and power-law exponent from medical ultrasonic images

Abstract Ultrasound is a mechanical wave propagating in tissue which is influenced in its propagation behavior by the locally prevailing acousto-mechanical conditions. By suitable processing of the back-scattered signals received by the ultrasound transducer, tissue parameters such as local bulk modulus, mass density, speed of sound, isotropic scattering coefficient, and also the locally acting tissue absorption can be inferred. A discipline that has received increasing attention in the medical ultrasonic imaging discipline and its scientific publications in recent years is quantitative ultrasound (QUS) which tries to estimate with great accuracy these local acting tissue parameters. In this paper we analyze different algorithms for estimation of high spatial resolution tissue absorption parameters. On the one hand, there is a simple absorption estimator based on the evaluation of the quotient of the power density spectra calculated for different depth regions (spectral-log-difference estimator), which, however, assumes a linearly with frequency increasing absorption, this is contrasted with an estimator which also allows to estimate a polynomial increase of the absorption with frequency (method-of-moments estimator). Since a closed-form solution cannot be given for this, a maximum-likelihood estimator for which there is always an estimate that can be computed numerically efficiently is developed. The results, tissue attenuation, are presented as a color-coded overlay on conventional B-mode ultrasound images showing only morphology.

Emerging Role of Quantitative Ultrasound-Based Imaging Techniques for Characterizing Rotator Cuff Tears: A Scoping Review

Rotator cuff myosteatosis following cuff tears is very common and one of the most important prognostic factors in clinical management. Quantitative ultrasound-based imaging techniques (QUBIT) are frequently used along with magnetic resonance imaging (MRI) to evaluate rotator cuff fatty degeneration. However, the examination of rotator cuff tissue integrity by QUBIT is lacking a standardized imaging protocol and procedural methodologies. In this scoping review, we synthesized the current state of QUBIT against the reference imaging modalities in patients with rotator cuff tears. The literature search was extracted from 963 studies, with 22 studies included in the final review in accordance with the preferred reporting items for systematic reviews and meta-analyses extensions for scoping reviews. The selected studies included human participants and focused on measuring at least one prognostic or diagnostic factor using ultrasonography-based imaging with reference to MRI. The findings suggest both conventional B-mode ultrasound and shear wave elastography imaging were comparable to MRI-based imaging techniques for the evaluation of fatty infiltration and rotator cuff tear characterization. This review establishes guidelines for reporting shoulder-specific QUBIT aimed at developing a standardized imaging protocol. The objective was to enhance the diagnostic and prognostic capabilities of QUBIT in the clinical setting.

Artificial intelligence - based ultrasound elastography for disease evaluation -a narrative review.

Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed.