Ultrasound in Radiology: From Anatomic, Functional, Molecular Imaging to Drug Delivery and Image-Guided Therapy

Integrated organosilica nanomedicine enables sonoimaging, sonochemistry and antitumor sonodynamic therapy

Sonography with its non-invasive and deep tissue-penetrating characteristics, not only contributes to promising developments in clinical disease diagnosis but also obtains acknowledgments as a prospective therapeutic approach in the field of tumor treatment. However, it remains a challenge for sonography simultaneously to achieve efficient imaging and therapeutic functionality. Here, we present an innovative integrated diagnosis and treatment paradigm by developing the nanomedicine of percarbamide-bromide-mesoporous organosilica spheres (MOS) with RGD peptide modification (PBMR) by loading percarbamide and bromide in MOS which were prepared by a one-step O/W microemulsion method. The PBMR nanomedicine effectively modifies the tumor acoustic environment to improve sonoimaging efficacy and induces sonochemical reactions to enhance the production of reactive oxygen species (ROS) for tumor treatment efficiency under sonography. The combination of PBMR nanomedicine and SDT achieved multiple ROS generation through the controlled sonochemical reactions and significantly boosted the potency of sonodynamic therapy and induced significant tumor regression with non-invasive tissue penetrability and minimizing damage to healthy tissues. Simultaneously, the generation of oxygen gas in the sonochemical process augments ultrasound reflection, resulting in a 4.9-fold increase in imaging grayscale. Our research establishes an effective platform for the synergistic integration of sonoimaging and sonodynamic antitumor therapy, offering a novel approach for precise antitumor treatment in the potential clinical applications.

Multi-angle speed-of-sound imaging with sparse sampling to characterize medical tissue properties

Medical Speed-of-sound (SoS) imaging, which can characterize medical tissue properties better by quantifying their different SoS, is an effective imaging method compared with conventional B-mode ultrasound imaging. As a commonly used diagnostic instrument, a hand-held array probe features convenient and quick inspection. However, artifacts will occur in the single-angle SoS imaging, resulting in indistinguishable tissue boundaries. In order to build a high-quality SoS image, a number of raw data are needed, which will bring difficulties to data storage and processing. Compressed sensing (CS) theory offers theoretical support to the feasibility that a sparse signal can be rebuilt with random but less sampling data. In this study, we proposed an SoS reconstruction method based on CS theory to process signals obtained from a hand-held linear array probe with a passive reflector positioned on the opposite side. The SoS reconstruction method consists of three parts. Firstly, a sparse transform basis is selected appropriately for a sparse representation of the original signal. Then, considering the mathematical principles of SoS imaging, the ray-length matrix is used as a sparse measurement matrix to observe the original signal, which represents the length of the acoustic propagation path. Finally, the orthogonal matching pursuit algorithm is introduced for image reconstruction. The experimental result of the phantom proves that SoS imaging can clearly distinguish tissues that show similar echogenicity in B-mode ultrasound imaging. The simulation and experimental results show that our proposed method holds promising potential for reconstructing precision SoS images with fewer signal samplings, transmission, and storage.

Artificial intelligence-based motion tracking in cancer radiotherapy: A review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Artificial intelligence (AI) has recently demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges, including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review presents the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provides a literature summary on the topic. We will also discuss the limitations of these AI-based studies and propose potential improvements.

Comparison of ultrasound- vs. landmark-guided injections for musculoskeletal pain: an umbrella review

OBJECTIVE

This umbrella review synthesizes systematic reviews and meta-analyses to reach a conclusion concerning the overall effectiveness of ultrasound-guided vs landmark-guided injections for treating musculoskeletal pain.

DESIGN

Umbrella review.

METHODS

PubMed, EMBASE, MEDLINE, and Web of Science were searched for relevant systematic reviews and meta-analyses from inception to March 2024. Critical appraisal, data extraction, and synthesis were performed in accordance with the criteria for conducting an umbrella review.

RESULTS

Seventeen articles, comprising 4 systematic reviews and 13 meta-analyses, were included. Using the AMSTAR2 instrument for quality assessment, 3 articles were rated as high quality, 1 as moderate, 7 as low, and 6 as critically low. Generally, ultrasound-guided injections were found to be more accurate than landmark-guided injections, particularly in the shoulder joint, though the results for pain relief and functional outcomes varied. Ultrasound guidance was notably effective for injections into the bicipital groove, wrist, hip, and knee - yielding greater accuracy and improved pain management. Both ultrasound-guided and landmark-guided techniques showed low incidence of adverse effects.

CONCLUSION

This umbrella review offers an in-depth analysis of the comparative effectiveness of ultrasound-guided and landmark-guided injections across a range of musculoskeletal sites/conditions. The findings suggest that ultrasound-guided is a reliable method.

Regulation of the sonography profession and patient safety: A comparative study between Australia and New Zealand with implications for statutory regulation

INTRODUCTION

The primary aim of statutory regulation in healthcare is patient safety. Few studies examine health professionals' perceptions of statutory regulation and its impact on patient safety. Statutory regulation of sonographers is different in Australia and New Zealand which affords a unique opportunity to compare and contrast regulation and its impact.

METHODS

An interpretive policy analysis investigated how statutory regulation in the sonography profession addresses patient safety in Australia and New Zealand. A framework analysis explored relevant statutory regulatory policy as well as interviews from sonographers.

RESULTS

Four policy documents were included. Thirty-one sonographers in Australia and nine in New Zealand took part in semi-structured interviews. Four themes described statutory regulation and its impact on patient safety: how statutory regulation is implemented in practice to address patient safety; factors contributing to implementation of statutory regulation; impact of statutory regulation on sonographers; and considerations for regulation.

CONCLUSION

Statutory regulation provides a reference point for safe practice but can be non-specific in defining sonographers' roles and outlining strategies that address patient safety. Sonographers' perspectives of how regulation of practice addresses patient safety was mixed. A gap exists in sonographers' understanding of the role of statutory regulation in patient safety.

IMPLICATIONS FOR PRACTICE

Regulatory authorities must consider how to effectively engage and educate both patients and practitioners about their role in patient safety. Practitioners should also take the opportunity to engage in understanding the role of statutory regulation in enhancing patient safety. A broader view of how Fitness-to-Practice in sonography is managed should be considered in light of the findings.

Exploring Extravasation in Cancer Patients

Extravasation, the unintended leakage of intravenously administered substances, poses significant challenges in cancer treatment, particularly during chemotherapy and radiotherapy. This comprehensive review explores the pathophysiology, incidence, risk factors, clinical presentation, diagnosis, prevention strategies, management approaches, complications, and long-term effects of extravasation in cancer patients. It also outlines future directions and research opportunities, including identifying gaps in the current knowledge and proposing areas for further investigation in extravasation prevention and management. Emerging technologies and therapies with the potential to improve extravasation prevention and management in both chemotherapy and radiotherapy are highlighted. Such innovations include advanced vein visualization technologies, smart catheters, targeted drug delivery systems, novel topical treatments, and artificial intelligence-based image analysis. By addressing these aspects, this review not only provides healthcare professionals with insights to enhance patient safety and optimize clinical practice but also underscores the importance of ongoing research and innovation in improving outcomes for cancer patients experiencing extravasation events.

Decoding Tumor Angiogenesis for Therapeutic Advancements: Mechanistic Insights

Tumor angiogenesis, the formation of new blood vessels within the tumor microenvironment, is considered a hallmark of cancer progression and represents a crucial target for therapeutic intervention. The tumor microenvironment is characterized by a complex interplay between proangiogenic and antiangiogenic factors, regulating the vascularization necessary for tumor growth and metastasis. The study of angiogenesis involves a spectrum of techniques, spanning from biomarker assessment to advanced imaging modalities. This comprehensive review aims to provide insights into the molecular intricacies, regulatory dynamics, and clinical implications of tumor angiogenesis. By delving into these aspects, we gain a deeper understanding of the processes driving vascularization in tumors, paving the way for the development of novel and effective antiangiogenic therapies in the fight against cancer.

Artificial Intelligence Aids Detection of Rotator Cuff Pathology: A Systematic Review

PURPOSE

To determine the model performance of artificial intelligence (AI) in detecting rotator cuff pathology using different imaging modalities and to compare capability with physicians in clinical scenarios.

METHODS

The review followed the PRISMA guidelines and was registered on PROSPERO. The criteria were as follows: 1) studies on the application of AI in detecting rotator cuff pathology using medical images, and 2) studies on smart devices for assisting in diagnosis were excluded. The following data were extracted and recorded: statistical characteristics, input features, AI algorithms used, sample sizes of training and testing sets, and model performance. The data extracted from the included studies were narratively reviewed.

RESULTS

A total of 14 articles, comprising 23,119 patients, met the inclusion and exclusion criteria. The pooled mean age of the patients was 56.7 years, and the female rate was 56.1%. The area under the curve (AUC) of the algorithmic model to detect rotator cuff pathology from ultrasound images, MRI images, and radiographic series ranged from 0.789 to 0.950, 0.844 to 0.943, and 0.820 to 0.830, respectively. Notably, 1 of the studies reported that AI models based on ultrasound images demonstrated a diagnostic performance similar to that of radiologists. Another comparative study demonstrated that AI models using MRI images exhibited greater accuracy and specificity compared to orthopedic surgeons in the diagnosis of rotator cuff pathology, albeit not in sensitivity.

CONCLUSIONS

The detection of rotator cuff pathology has been significantly aided by the exceptional performance of AI models. In particular, these models are equally adept as musculoskeletal radiologists in using ultrasound to diagnose rotator cuff pathology. Furthermore, AI models exhibit statistically superior levels of accuracy and specificity when using MRI to diagnose rotator cuff pathology, albeit with no marked difference in sensitivity, in comparison to orthopaedic surgeons.

LEVEL OF EVIDENCE

Level III, systematic review of Level III studies.

Development and Clinical Application of Artificial Intelligence Assistant System for Rotator Cuff Ultrasound Scanning

OBJECTIVE

We developed an intelligent assistance system for shoulder ultrasound imaging, incorporating deep-learning algorithms to facilitate standard plane recognition and automatic tissue segmentation of the rotator cuff and its surrounding structures. We evaluated the system's performance using a dedicated data set of rotator cuff ultrasound images to assess its feasibility in clinical practice.

METHODS

To fulfill the system's primary functions, we designed a standard plane recognition module based on the ResNet50 network and an automatic tissue segmentation module using the Mask R-CNN model. The modules were trained on carefully curated data sets. The standard plane recognition module automatically identifies a specific standard plane based on the ultrasound image characteristics. The automatic tissue segmentation module effectively delineates and segments anatomical structures within the identified standard plane.

RESULTS

With the use of 59,265 shoulder joint ultrasound images, the standard plane recognition model achieved an impressive recognition accuracy of 94.9% in the test set, with an average precision rate of 96.4%, recall rate of 95.4% and F1 score of 95.9%. The automatic tissue segmentation model, tested on 1886 images, exhibited a commendable average intersection over union value of 96.2%, indicating robustness and accuracy. The model achieved mean intersection over union values exceeding 90.0% for all standard planes, indicating its effectiveness in precisely delineating the anatomical structures.

CONCLUSION

Our shoulder joint musculoskeletal intelligence system swiftly and accurately identifies standard planes and performs automatic tissue segmentation.

Ultrasound-Triggered Amoxicillin Release from Chitosan/Ethylene Glycol Diglycidyl Ether/Amoxicillin Hydrogels Having a Covalently Bonded Network

An antibiotic release system triggered by ultrasound (US) was investigated using chitosan (CS)/ethylene glycol diglycidyl ether (EGDE) hydrogel carriers with amoxicillin (Amox) drug. Different CS concentrations of 1.5, 2, 2.5, and 3 wt % were gelled with EGDE and Amox was entrapped in the hydrogel carrier; the accelerated release was observed as triggered by 43 kHz US exposure at different US output powers ranging from 0 to 35 W. Among these CS hydrogel systems, the degree of accelerated Amox release depended on the CS concentration for the hydrogelation and the matrix with 2 wt % CS exhibited efficient Amox release at 35 W US power with around 19 μg/mL. The drug released with time was fitted with Higuchi and Korsmeyer-Peppas models, and the enhancement was caused by US aiding drug diffusion within the hydrogel matrix by a non-Fickian diffusion mechanism. The US effect on the viscoelasticity of the hydrogel matrix indicated that the matrix became somewhat softened by the US exposure to the dense hydrogels for 2.5 and 3% CS/EGDE, while the degree of softening was slightly marked in the CS/EGDE hydrogels prepared with 1.5 and 2% CS concentration. Such US softening also aided drug diffusion within the hydrogel matrix, suggesting an enhanced Amox release.

Normology: Is it Time to Rethink Point-of-Care Ultrasound Training?

Point-of-care ultrasound (PoCUS) is increasingly being adopted by clinicians to augment their clinical examination and aid procedures. Evidence-based clinical practice guidelines now support many aspects of PoCUS practice. As a result, more and more medical schools are integrating PoCUS into their curricula, creating a significant demand for training resources. All training must ensure that trainees have the appropriate knowledge, skills and behaviour to enable the clinical integration of PoCUS. The transition from supervised to unsupervised practice is an important step in PoCUS, but should not be confused with expertise. At the start of clinical practice, confirming that something is not normal is probably enough, and trainees can subsequently improve their PoCUS diagnostic accuracy at the bedside during clinical rotations. Our hypothesis is that competency can be achieved without the need to scan patients suspected or suffering from the target pathology. This would enable most of the training to be carried out outside the clinical environment, using volunteers in planned sessions. These planned sessions will be able to accommodate a larger number of trainees and may facilitate collaboration between specialties. When patients are scanned in the clinical environment, trainees should have acquired a good level of competence beforehand. In summary, normology principles could help PoCUS trainees, at the start of their training, to acquire the knowledge, skills and behaviour necessary to achieve a level of competency allowing them to proceed to unsupervised practice. Initially confirming whether something is normal or not is enough. Nevertheless, further research should be conducted to support this concept, and its impact on PoCUS teaching in clinical practice.

Dynamic assessment of brain perfusion in a middle cerebral artery occlusion rat model by contrast-enhanced ultrasound imaging: a pilot study

BACKGROUND

The middle cerebral artery occlusion model (MCAo) is a commonly used animal model for cerebral ischemia studies but lacks accessible imaging techniques for the assessment of hemodynamic changes of the model.

PURPOSE

The study aims to explore the value of contrast-enhanced ultrasound (CEUS) in evaluating brain perfusion in the early stages after MCAo surgery.

MATERIAL AND METHODS

In total, 18 adult male Sprague-Dawley rats were subjected to right MCAo using an intraluminal filament model, and CEUS was performed at the three following timepoints: before (T0), immediately after (T1), and 6 h after permanent MCAo (T2). Twelve rats successfully completed the study, and their brains were removed and stained using 2, 3, 5-triphenyltetrazolium chloride (TTC). CEUS video images were visualized offline, and the time-intensity curves (TICs) were analyzed. Different cerebrovascular patterns and manifestations of the contrast enhancement in rat ischemic hemispheres were observed. Semi-quantitative parameters of TICs in ischemic areas (ROIi) and the surrounding normal- or hypo-perfused areas (ROIn) were calculated and compared between T0, T1, and T2, and also between ROIi and ROIn.

RESULTS

A significant correlation was found between the lesion volume (%) determined by TTC and CEUS parameters (r = -0.691, P = 0.013 for peak intensity; r = -0.742, P = 0.006 for area under the curve) at T2. After the same occlusion, there were differences in contrast perfusion in each group.

CONCLUSION

This study suggests that CEUS could be an effective imaging tool for studying cerebral ischemia and perfusion in small animals as long as the transcranial acoustic window allows it.

Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

BACKGROUND AND AIM

Ultrasound (US) imaging is increasingly preferred over other more invasive modalities in preclinical studies using animal models. However, this technique has some limitations, mainly related to operator dependence. To overcome some of the current drawbacks, sophisticated data processing models are proposed, in particular artificial intelligence models based on deep learning (DL) networks. This systematic review aims to overview the application of DL algorithms in assisting US analysis of images acquired in in vivo preclinical studies on animal models.

METHODS

A literature search was conducted using the Scopus and PubMed databases. Studies published from January 2012 to November 2022 that developed DL models on US images acquired in preclinical/animal experimental scenarios were eligible for inclusion. This review was conducted according to PRISMA guidelines.

RESULTS

Fifty-six studies were enrolled and classified into five groups based on the anatomical district in which the DL models were used. Sixteen studies focused on the cardiovascular system and fourteen on the abdominal organs. Five studies applied DL networks to images of the musculoskeletal system and eight investigations involved the brain. Thirteen papers, grouped under a miscellaneous category, proposed heterogeneous applications adopting DL systems. Our analysis also highlighted that murine models were the most common animals used in in vivo studies applying DL to US imaging.

CONCLUSION

DL techniques show great potential in terms of US images acquired in preclinical studies using animal models. However, in this scenario, these techniques are still in their early stages, and there is room for improvement, such as sample sizes, data preprocessing, and model interpretability.

Niche preclinical and clinical applications of photoacoustic imaging with endogenous contrast

In the past decade, photoacoustic (PA) imaging has attracted a great deal of popularity as an emergent diagnostic technology owing to its successful demonstration in both preclinical and clinical arenas by various academic and industrial research groups. Such steady growth of PA imaging can mainly be attributed to its salient features, including being non-ionizing, cost-effective, easily deployable, and having sufficient axial, lateral, and temporal resolutions for resolving various tissue characteristics and assessing the therapeutic efficacy. In addition, PA imaging can easily be integrated with the ultrasound imaging systems, the combination of which confers the ability to co-register and cross-reference various features in the structural, functional, and molecular imaging regimes. PA imaging relies on either an endogenous source of contrast (e.g., hemoglobin) or those of an exogenous nature such as nano-sized tunable optical absorbers or dyes that may boost imaging contrast beyond that provided by the endogenous sources. In this review, we discuss the applications of PA imaging with endogenous contrast as they pertain to clinically relevant niches, including tissue characterization, cancer diagnostics/therapies (termed as theranostics), cardiovascular applications, and surgical applications. We believe that PA imaging's role as a facile indicator of several disease-relevant states will continue to expand and evolve as it is adopted by an increasing number of research laboratories and clinics worldwide.

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.

Reliability of Ultrasound Imaging in Examining Integrity and Inflammation in the Dorsalis Pedis Artery

OBJECTIVE

Assessment of small vessel inflammatory diseases such as rheumatoid vasculitis is challenging. Small arteries such as the dorsalis pedis artery (DPA) are difficult to assess for changes in the arterial wall with medical imaging. Ultrasound imaging is a viable tool for examining the integrity and inflammatory changes in the arterial wall; however, no empirical data on its reliability have been described.

METHODS

We measured the intra- and inter-rater reliability of ultrasound measurements across five parameters evaluating arterial integrity of the proximal DPA in participants with and without small vessel disease. We recruited 10 participants with rheumatoid arthritis and 10 healthy controls. Two sonographers using ultrasound independently measured DPA lumen diameter, artery diameter, lumen-to-arterial diameter ratio, arterial Doppler velocity and inflammatory changes in the proximal wall of the DPA. The intraclass correlation coefficient (ICC) was used to evaluate 95% confidence intervals within and between raters. Bland-Altman analyses were used to assess limits of agreement and were compared with minimal clinically important differences (MCID).

RESULTS

Four of five selected parameters were found to have excellent intra- and inter-rater reliability within and between raters (ICC = 0.903-0.996). Acceptable reliability was found for measurement of arterial blood flow velocity within raters (ICC = 0.815-0.909), but not between raters (ICC = 0.634). Standard mean errors in all parameters were within minimal clinically important differences.

CONCLUSION

Ultrasound imaging has been found to be a reliable method of assessment of arterial integrity and inflammation of the proximal DPA in people with small vessel disease. Evaluation of arterial blood flow velocity requires cautious interpretation.

Australian sonographers' perceptions of patient safety in ultrasound imaging: Part 1 - identifying the main safety concerns, a qualitative study

Introduction

Patient safety has been an undervalued component of quality healthcare but is a challenging area of research.Ultrasound is the most common imaging modality in the world. Research on patient safety in ultrasound is generally focused on bioeffects and safe operation of ultrasound equipment. However, other safety issues exist in practice that warrant consideration.This paper forms the first part of a PhD study exploring patient safety in medical diagnostic ultrasound, beyond the notion of bioeffects.The ultimate aim of the study is to inform the final phase of the research study which will consider the next steps in improving the quality and safety of healthcare experienced by patients.

Methods

A qualitative study using semi-structured, one-on-one interviews. A thematic analysis categorised data into codes and generated final themes.

Results

A heterogeneous mix of 31 sonographers, who reflected the profile of the profession in Australia, were interviewed between September 2019 and January 2020. Seven themes emerged from the analysis. These were bioeffects, physical safety, workload, reporting, professionalism, intimate examinations and infection control.

Conclusion

This study presents a comprehensive analysis of sonographers' perceptions of patient safety in ultrasound imaging, not previously available in the literature. Consistent with the literature, patient safety in ultrasound tends to be viewed in technical terms through the potential for bioeffects of tissue damage or physical harm to the patient. However, other patient safety issues have emerged, and while not as well recognised, have the potential to negatively impact on patient safety.

Low-Intensity Focused Ultrasound Neuromodulation for Stroke Recovery: A Novel Deep Brain Stimulation Approach for Neurorehabilitation?

Stroke as the leading cause of adult long-term disability and has a significant impact on patients, society and socio-economics. Non-invasive brain stimulation (NIBS) approaches such as transcranial magnetic stimulation (TMS) or transcranial electrical stimulation (tES) are considered as potential therapeutic options to enhance functional reorganization and augment the effects of neurorehabilitation. However, non-invasive electrical and magnetic stimulation paradigms are limited by their depth focality trade-off function that does not allow to target deep key brain structures critically important for recovery processes. Transcranial ultrasound stimulation (TUS) is an emerging approach for non-invasive deep brain neuromodulation. Using non-ionizing, ultrasonic waves with millimeter-accuracy spatial resolution, excellent steering capacity and long penetration depth, TUS has the potential to serve as a novel non-invasive deep brain stimulation method to establish unprecedented neuromodulation and novel neurorehabilitation protocols. The purpose of the present review is to provide an overview on the current knowledge about the neuromodulatory effects of TUS while discussing the potential of TUS in the field of stroke recovery, with respect to existing NIBS methods. We will address and discuss critically crucial open questions and remaining challenges that need to be addressed before establishing TUS as a new clinical neurorehabilitation approach for motor stroke recovery.

Dynamic Speed of Sound Adaptive Transmission-Reflection Ultrasound Computed Tomography

Ultrasound computed tomography (USCT) can visualize a target with multiple imaging contrasts, which were demonstrated individually previously. Here, to improve the imaging quality, the dynamic speed of sound (SoS) map derived from the transmission USCT will be adapted for the correction of the acoustic speed variation in the reflection USCT. The variable SoS map was firstly restored via the optimized simultaneous algebraic reconstruction technique with the time of flights selected from the transmitted ultrasonic signals. Then, the multi-stencils fast marching method was used to calculate the delay time from each element to the grids in the imaging field of view. Finally, the delay time in conventional constant-speed-assumed delay and sum (DAS) beamforming would be replaced by the practical computed delay time to achieve higher delay accuracy in the reflection USCT. The results from the numerical, phantom, and in vivo experiments show that our approach enables multi-modality imaging, accurate target localization, and precise boundary detection with the full-view fast imaging performance. The proposed method and its implementation are of great value for accurate, fast, and multi-modality USCT imaging, particularly suitable for highly acoustic heterogeneous medium.

Medical imaging training with eye movement modeling examples: A randomized controlled study

PURPOSE

To determine whether ultrasound training in which the expert's eye movements are superimposed to the underlying ultrasound video (eye movement modeling examples; EMMEs) leads to better learner outcomes than traditional eye movement-free instructions.

MATERIALS AND METHODS

106 undergraduate medical students were randomized in two groups; 51 students in the EMME group watched 5-min ultrasound examination videos combined with the eye movements of an expert performing the task. The identical videos without the eye movements were shown to 55 students in the control group. Performance and behavioral parameters were compared prepost interventional using ANOVAs. Additionally, cognitive load, and prior knowledge in anatomy were surveyed.

RESULTS

After training, the EMME group identified more sonoanatomical structures correctly, and completed the tasks faster than the control group. This effect was partly mediated by a reduction of extraneous cognitive load. Participants with greater prior anatomical knowledge benefited the most from the EMME training.

CONCLUSION

Displaying experts' eye movements in medical imaging training appears to be an effective way to foster medical interpretation skills of undergraduate medical students. One underlying mechanism might be that practicing with eye movements reduces cognitive load and helps learners activate their prior knowledge.

Multi-path decoder U-Net: A weakly trained real-time segmentation network for object detection and localization in ultrasound scans

Detecting and localizing an anatomical structure of interest within the field of view of an ultrasound scan is an essential step in many diagnostic and therapeutic procedures. However, ultrasound scans suffer from high levels of variabilities across sonographers and patients, making it challenging for sonographers to accurately identify and locate these structures without extensive experience. Segmentation-based convolutional neural networks (CNNs) have been proposed as a solution to assist sonographers in this task. Despite their accuracy, these networks require pixel-wise annotations for training; an expensive and labor-intensive operation that requires the expertise of an experienced practitioner to identify the precise outline of the structures of interest. This complicates, delays, and increases the cost of network training and deployment. To address this problem, we propose a multi-path decoder U-Net architecture that is trained on bounding box segmentation maps; not requiring pixel-wise annotations. We show that the network can be trained on small training sets, which is the case in medical imaging datasets; reducing the cost and time needed for deployment and use in clinical settings. The multi-path decoder design allows for better training of deeper layers and earlier attention to the target anatomical structures of interest. This architecture offers up to a 7% relative improvement compared to the U-Net architecture in localization and detection performance, with an increase of only 0.75% in the number of parameters. Its performance is on par with, or slightly better than, the more computationally expensive U-Net++, which has 20% more parameters; making the proposed architecture a more computationally efficient alternative for real-time object detection and localization in ultrasound scans.

Multiscale Label-Free Imaging of Fibrillar Collagen in the Tumor Microenvironment

With recent advances in cancer therapeutics, there is a great need for improved imaging methods for characterizing cancer onset and progression in a quantitative and actionable way. Collagen, the most abundant extracellular matrix protein in the tumor microenvironment (and the body in general), plays a multifaceted role, both hindering and promoting cancer invasion and progression. Collagen deposition can defend the tumor with immunosuppressive effects, while aligned collagen fiber structures can enable tumor cell migration, aiding invasion and metastasis. Given the complex role of collagen fiber organization and topology, imaging has been a tool of choice to characterize these changes on multiple spatial scales, from the organ and tumor scale to cellular and subcellular level. Macroscale density already aids in the detection and diagnosis of solid cancers, but progress is being made to integrate finer microscale features into the process. Here we review imaging modalities ranging from optical methods of second harmonic generation (SHG), polarized light microscopy (PLM), and optical coherence tomography (OCT) to the medical imaging approaches of ultrasound and magnetic resonance imaging (MRI). These methods have enabled scientists and clinicians to better understand the impact collagen structure has on the tumor environment, at both the bulk scale (density) and microscale (fibrillar structure) levels. We focus on imaging methods with the potential to both examine the collagen structure in as natural a state as possible and still be clinically amenable, with an emphasis on label-free strategies, exploiting intrinsic optical properties of collagen fibers.

RadFormer: Transformers with global-local attention for interpretable and accurate Gallbladder Cancer detection

We propose a novel deep neural network architecture to learn interpretable representation for medical image analysis. Our architecture generates a global attention for region of interest, and then learns bag of words style deep feature embeddings with local attention. The global, and local feature maps are combined using a contemporary transformer architecture for highly accurate Gallbladder Cancer (GBC) detection from Ultrasound (USG) images. Our experiments indicate that the detection accuracy of our model beats even human radiologists, and advocates its use as the second reader for GBC diagnosis. Bag of words embeddings allow our model to be probed for generating interpretable explanations for GBC detection consistent with the ones reported in medical literature. We show that the proposed model not only helps understand decisions of neural network models but also aids in discovery of new visual features relevant to the diagnosis of GBC. Source-code is available at https://github.com/sbasu276/RadFormer.

The experiences of sonographers with regard to report writing and communicating their findings

BACKGROUND

Sonographers in South Africa are legally allowed to write their own reports; however, they often lack adequate training in providing a well-structured and coherent formal written report.

AIM

The aim of this study was to explore and describe how sonographers in the Gauteng province experience the responsibility of report writing and to develop recommendations that could assist sonographers in the execution of their duty.

SETTING

Focus group discussions (FGDs) with sonographers from private and public hospitals located in Gauteng province were conducted at neutral locations that were convenient for the sonographers.

METHODS

A qualitative phenomenological research design was used for this study. A two-stage sampling approach was employed to recruit information-rich sonographers to partake in this study. Purposeful sampling was used to select sonographers based on their first-hand experience of report writing, followed by snowball sampling which allowed the researcher access to new participants on the recommendation of previous sonographers. Thirteen female sonographers voluntarily participated in the study, and the FGDs continued until data saturation was reached. The views and opinions of the sonographers were analysed using content analysis.

RESULTS

Key findings of this study indicated that sonographers felt unprepared to describe ultrasound findings correctly in order to provide a coherent and well-structured formal written report.

CONCLUSION

Sonographers suggested the use of workshops or further training at higher educational institutions (HEIs) to support sonographers in their report-writing role.

CONTRIBUTION

The experiences identified by sonographers can assist HEIs to provide further training or workshops to support sonographers in communicating their findings effectively.

Recent Advances in Tracking Devices for Biomedical Ultrasound Imaging Applications

With the rapid advancement of tracking technologies, the applications of tracking systems in ultrasound imaging have expanded across a wide range of fields. In this review article, we discuss the basic tracking principles, system components, performance analyses, as well as the main sources of error for popular tracking technologies that are utilized in ultrasound imaging. In light of the growing demand for object tracking, this article explores both the potential and challenges associated with different tracking technologies applied to various ultrasound imaging applications, including freehand 3D ultrasound imaging, ultrasound image fusion, ultrasound-guided intervention and treatment. Recent development in tracking technology has led to increased accuracy and intuitiveness of ultrasound imaging and navigation with less reliance on operator skills, thereby benefiting the medical diagnosis and treatment. Although commercially available tracking systems are capable of achieving sub-millimeter resolution for positional tracking and sub-degree resolution for orientational tracking, such systems are subject to a number of disadvantages, including high costs and time-consuming calibration procedures. While some emerging tracking technologies are still in the research stage, their potentials have been demonstrated in terms of the compactness, light weight, and easy integration with existing standard or portable ultrasound machines.

Molecular Mechanisms of Rhabdomyolysis-Induced Kidney Injury: From Bench to Bedside

Rhabdomyolysis-induced acute kidney injury (RIAKI) occurs following damage to the muscular sarcolemma sheath, resulting in the leakage of myoglobin and other metabolites that cause kidney damage. Currently, the sole recommended clinical treatment for RIAKI is aggressive fluid resuscitation, but other potential therapies, including pretreatments for those at risk for developing RIAKI, are under investigation. This review outlines the mechanisms and clinical significance of RIAKI, investigational treatments and their specific targets, and the status of ongoing research trials.

An Ultrasound-Responsive Theranostic Cyclodextrin-Loaded Nanoparticle for Multimodal Imaging and Therapy for Atherosclerosis

Atherosclerosis is a major cause of mortality and morbidity worldwide. Left undiagnosed and untreated, atherosclerotic plaques can rupture and cause cardiovascular complications such as myocardial infarction and stroke. Atherosclerotic plaques are composed of lipids, including oxidized low-density lipoproteins and cholesterol crystals, and immune cells, including macrophages. 2-Hydroxypropyl-beta-cyclodextrin (CD) is FDA-approved for capturing, solubilizing, and delivering lipophilic drugs in humans. It is also known to dissolve cholesterol crystals and decrease atherosclerotic plaque size. However, its low retention time necessitates high dosages for successful therapy. This study reports CD delivery via air-trapped polybutylcyanoacrylate nanoparticles (with diameters of 388 ± 34 nm) loaded with CD (CDNPs). The multimodal contrast ability of these nanoparticles after being loaded with IR780 dye in mice is demonstrated using ultrasound and near-infrared imaging. It is shown that CDNPs enhance the cellular uptake of CD in murine cells. In an ApoE^-/- mouse model of atherosclerosis, treatment with CDNPs significantly improves the anti-atherosclerotic efficacy of CD. Ultrasound triggering further improves CD uptake, highlighting that CDNPs can be used for ultrasound imaging and ultrasound-responsive CD delivery. Thus, CDNPs represent a theranostic nanocarrier for potential application in patients with atherosclerosis.

Joint Localization and Classification of Breast Cancer in B-Mode Ultrasound Imaging via Collaborative Learning with Elastography

Convolutional neural networks (CNNs) have been successfully applied in the computer-aided ultrasound diagnosis for breast cancer. Up to now, several CNN-based methods have been proposed. However, most of them consider tumor localization and classification as two separate steps, rather than performing them simultaneously. Besides, they suffer from the limited diagnosis information in the B-mode ultrasound (BUS) images. In this study, we develop a novel network ResNet-GAP that incorporates both localization and classification into a unified procedure. To enhance the performance of ResNet-GAP, we leverage stiffness information in the elastography ultrasound (EUS) modality by collaborative learning in the training stage. Specifically, a dual-channel ResNet-GAP is developed, one channel for BUS and the other for EUS. In each channel, multiple class activity maps (CAMs) are generated using a series of convolutional kernels of different sizes. The multi-scale consistency of the CAMs in both channels are further considered in network optimization. Experiments on 264 patients in this study show that the newly developed ResNet-GAP achieves an accuracy of 88.6%, a sensitivity of 95.3%, a specificity of 84.6%, and an AUC of 93.6% on the classification task, and a 1.0NLF of 87.9% on the localization task, which is better than some state-of-the-art approaches.

Multi-class deep learning segmentation and automated measurements in periodontal sonograms of a porcine model

OBJECTIVES

Ultrasound emerges as a complement to cone-beam computed tomography in dentistry, but struggles with artifacts like reverberation and shadowing. This study seeks to help novice users recognize soft tissue, bone, and crown of a dental sonogram, and automate soft tissue height (STH) measurement using deep learning.

METHODS

In this retrospective study, 627 frames from 111 independent cine loops of mandibular and maxillary premolar and incisors collected from our porcine model (N = 8) were labeled by a reader. 274 premolar sonograms, including data augmentation, were used to train a multi class segmentation model. The model was evaluated against several test sets, including premolar of the same breed (n = 74, Yucatan) and premolar of a different breed (n = 120, Sinclair). We further proposed a rule-based algorithm to automate STH measurements using predicted segmentation masks.

RESULTS

The model reached a Dice similarity coefficient of 90.7±4.39%, 89.4±4.63%, and 83.7±10.5% for soft tissue, bone, and crown segmentation, respectively on the first test set (n = 74), and 90.0±7.16%, 78.6±13.2%, and 62.6±17.7% on the second test set (n = 120). The automated STH measurements have a mean difference (95% confidence interval) of -0.22 mm (-1.4, 0.95), a limit of agreement of 1.2 mm, and a minimum ICC of 0.915 (0.857, 0.948) when compared to expert annotation.

CONCLUSION

This work demonstrates the potential use of deep learning in identifying periodontal structures on sonograms and obtaining diagnostic periodontal dimensions.

Nanomaterials as Ultrasound Theragnostic Tools for Heart Disease Treatment/Diagnosis

A variety of different nanomaterials (NMs) such as microbubbles (MBs), nanobubbles (NBs), nanodroplets (NDs), and silica hollow meso-structures have been tested as ultrasound contrast agents for the detection of heart diseases. The inner part of these NMs is made gaseous to yield an ultrasound contrast, which arises from the difference in acoustic impedance between the interior and exterior of such a structure. Furthermore, to specifically achieve a contrast in the diseased heart region (DHR), NMs can be designed to target this region in essentially three different ways (i.e., passively when NMs are small enough to diffuse through the holes of the vessels supplying the DHR, actively by being associated with a ligand that recognizes a receptor of the DHR, or magnetically by applying a magnetic field orientated in the direction of the DHR on a NM responding to such stimulus). The localization and resolution of ultrasound imaging can be further improved by applying ultrasounds in the DHR, by increasing the ultrasound frequency, or by using harmonic, sub-harmonic, or super-resolution imaging. Local imaging can be achieved with other non-gaseous NMs of metallic composition (i.e., essentially made of Au) by using photoacoustic imaging, thus widening the range of NMs usable for cardiac applications. These contrast agents may also have a therapeutic efficacy by carrying/activating/releasing a heart disease drug, by triggering ultrasound targeted microbubble destruction or enhanced cavitation in the DHR, for example, resulting in thrombolysis or helping to prevent heart transplant rejection.

Classification of rotator cuff tears in ultrasound images using deep learning models

Rotator cuff tears (RCTs) are one of the most common shoulder injuries, which are typically diagnosed using relatively expensive and time-consuming diagnostic imaging tests such as magnetic resonance imaging or computed tomography. Deep learning algorithms are increasingly used to analyze medical images, but they have not been used to identify RCTs with ultrasound images. The aim of this study is to develop an approach to automatically classify RCTs and provide visualization of tear location using ultrasound images and convolutional neural networks (CNNs). The proposed method was developed using transfer learning and fine-tuning with five pre-trained deep models (VGG19, InceptionV3, Xception, ResNet50, and DenseNet121). The Bayesian optimization method was also used to optimize hyperparameters of the CNN models. A total of 194 ultrasound images from Kosin University Gospel Hospital were used to train and test the CNN models by five-fold cross-validation. Among the five models, DenseNet121 demonstrated the best classification performance with 88.2% accuracy, 93.8% sensitivity, 83.6% specificity, and AUC score of 0.832. A gradient-weighted class activation mapping (Grad-CAM) highlighted the sensitive features in the learning process on ultrasound images. The proposed approach demonstrates the feasibility of using deep learning and ultrasound images to assist RCTs' diagnosis.

Acoustic Angiography: Superharmonic Contrast-Enhanced Ultrasound Imaging for Noninvasive Visualization of Microvasculature

Acoustic angiography is a contrast-enhanced ultrasound technique that relies on superharmonic imaging to form high-resolution, three-dimensional maps of the microvasculature. In order to obtain signal separation between tissue and contrast, acoustic angiography has been performed with dual-frequency transducers with nonoverlapping bandwidths. This enables a high contrast-to-tissue ratio, and the choice of a high frequency receiving element provides high resolution. In this chapter, we describe the technology behind acoustic angiography as well as the step-by-step implementation of this contrast enhanced microvascular imaging technique.

The Effects of Body Position on Trochanteric Soft Tissue Thickness-Implications for Predictions of Impact Force and Hip Fracture Risk During Lateral Falls

Trochanteric soft tissue thickness (TSTT) is a protective factor against fall-related hip fractures. This study's objectives were to determine: (1) the influence of body posture on TSTT and (2) the downstream effects of TSTT on biomechanical model predictions of fall-related impact force (Ffemur) and hip fracture factor of risk. Ultrasound was used to measure TSTT in 45 community-dwelling older adults in standing, supine, and side-lying positions with hip rotation angles of -25°, 0°, and 25°. Supine TSTT (mean [SD] = 5.57 [2.8] cm) was 29% and 69% greater than in standing and side-lying positions, respectively. The Ffemur based on supine TSTT (3380 [2017] N) was 19% lower than the standing position (4173 [1764] N) and 31% lower than the side-lying position (4908 [1524] N). As factor of risk was directly influenced by Ffemur, the relative effects on fracture risk were similar. While less pronounced (<10%), the effects of hip rotation angle were consistent across TSTT, Ffemur, and factor of risk. Based on the sensitivity of impact models to TSTT, these results highlight the need for a standardized TSTT measurement approach. In addition, the consistent influence of hip rotation on TSTT (and downstream model predictions) support its importance as a factor that may influence fall-related hip fracture risk.

Patient understanding of diagnostic ultrasound examinations in an Australian private radiology clinic

Purpose

To determine patient understanding and expectations of ultrasound imaging and its safety, sources of examination information and levels of examination anxiety.

Methods

1070 consecutive patients presenting to a private, regional radiology practice in New South Wales, Australia between 16^th May and 14^th June 2019, were invited to complete a survey while waiting for their non-interventional ultrasound examination. The survey included questions on demographic characteristics, knowledge such as examination risks and safety, ultrasound expectations such as dissemination of results, sources of examination information and levels of participant anxiety. Data were analysed using Kruskal-Wallis tests (SPSS v. 25) to determine statistical associations between participant demographic factors, knowledge, anxiety level and the main source of examination information.

Results

A total of 215/1070 (20%) patients voluntarily participated in the study. Most were female (74%, 159/215), with a median (range) age of 55 (18-92) years. Participant's median (range) score of ultrasound imaging knowledge was 3 (0-5) scored out of six and indicated uncertainty regarding the ultrasound procedure, result dissemination and ultrasound safety. Participants with previous personal experience had significantly higher levels of ultrasound imaging knowledge than those whose primary information source was their referring doctor (P = 0.0005) or the internet (P = 0.026). Significantly higher levels of ultrasound examination knowledge were also associated with lower self-reported levels of anxiety (P = 0.002).

Conclusion

Participants had incomplete ultrasound examination knowledge, including misconceptions regarding safety and result dissemination, some of which could be clarified by the sonographer at the beginning of the ultrasound examination.

Use of Intertransmission Coherence for Haze Artifact Suppression in Cardiovascular Synthetic Aperture Ultrasound Imaging

Synthetic aperture (SA) beamforming is a principal technology of modern medical ultrasound imaging. In that the use of focused transmission provides superior signal-to-noise ratio (SNR) and is promising for cardiovascular diagnosis at the maximum imaging depth of about 160 mm. But there is a pitfall in increasing the frame rate to more than 80 frames per second (frames/s) without image degradation by the haze artifact produced when the transmit foci (SA virtual sources) placed within the imaging field. We hypothesize that the source of this artifact is a grating lobe caused by coarse (decimated) multiple transmission and manifesting in the low brightness region in the accelerated-frame-rate images. We propose an intertransmission coherence factor (ITCF) method suppressing haze artifacts caused by coarse-pitch multiple transmission. The method is expected to suppress the image blurring because the SA grating lobe signal is less coherent than the main lobe signals. We evaluated an ITCF algorithm for suppressing the grating artifact when the transmission pitch is up to four times larger than the normal pitch (equivalent to 160 frames/s). In in-vitro and in-vivo experiments, we demonstrated the strong relation of haze artifact with the grating lobe due to the coarse-pitch transmission. Then, we confirmed that the ITCF method suppresses the haze artifact of a human heart by 15 dB while preserving the spatial resolution. The ITCF method combined with focused transmission SA beamforming is a valid method for getting cardiovascular ultrasound B-mode images without making a compromise in the trade-off relationship between the frame rate and the SNR.

A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve Detection

Ultrasound scanning is essential in several medical diagnostic and therapeutic applications. It is used to visualize and analyze anatomical features and structures that influence treatment plans. However, it is both labor intensive, and its effectiveness is operator dependent. Real-time accurate and robust automatic detection and tracking of anatomical structures while scanning would significantly impact diagnostic and therapeutic procedures to be consistent and efficient. In this paper, we propose a deep learning framework to automatically detect and track a specific anatomical target structure in ultrasound scans. Our framework is designed to be accurate and robust across subjects and imaging devices, to operate in real-time, and to not require a large training set. It maintains a localization precision and recall higher than 90% when trained on training sets that are as small as 20% in size of the original training set. The framework backbone is a weakly trained segmentation neural network based on U-Net. We tested the framework on two different ultrasound datasets with the aim to detect and track the Vagus nerve, where it outperformed current state-of-the-art real-time object detection networks.Clinical Relevance-The proposed approach provides an accurate method to detect and localize target anatomical structures in real-time, assisting sonographers during ultrasound scanning sessions by reducing diagnostic and detection errors, and expediting the duration of scanning sessions.

Detecting and Monitoring Hydrogels with Medical Imaging

Hydrogels, water-swollen polymer networks, are being applied to numerous biomedical applications, such as drug delivery and tissue engineering, due to their potential tunable rheologic properties, injectability into tissues, and encapsulation and release of therapeutics. Despite their promise, it is challenging to assess their properties in vivo and crucial information such as hydrogel retention at the site of administration and in situ degradation kinetics are often lacking. To address this, technologies to evaluate and track hydrogels in vivo with various imaging techniques have been developed in recent years, including hydrogels functionalized with contrast generating material that can be imaged with methods such as X-ray computed tomography (CT), magnetic resonance imaging (MRI), optical imaging, and nuclear imaging systems. In this review, we will discuss emerging approaches to label hydrogels for imaging, review the advantages and limitations of these imaging techniques, and highlight examples where such techniques have been implemented in biomedical applications.

The use of high frequency skin ultrasound in non-melanoma skin cancer

Ultrasound (US) or sonography is the most common diagnostic non-invasive imaging method, which is estimated to account for more than a third of all diagnostic procedures used in medical practice. Traditionally, US frequencies used in diagnostic radiology range from 1 MHz to approximately 16 MHz. Visualization of skin morphology requires US frequencies from 20 to 100 MHz, and is called a "high frequency ultrasound" (HFUS). HFUS has been explored in normal skin and in various skin pathologies. It has been found useful in differentiation diagnosis of infiltrative (perivascular) and exudative inflammation, hypertrophic or normotrophic scars, keloids, and in skin atrophy. Moreover, it has been used to support diagnosis and management of skin tumors. In skin oncology, HFUS can provide essential information on tumors' size and margins, particularly the depth of invasion. Such information are crucial in deciding on the form of treatment (e.g., photodynamic therapy, radiotherapy, brachytherapy), computed tomography (CT)-based planning treatment (e.g., superficial brachytherapy), and in skin monitoring after treatment, in addition to clinical examination.

Electromechanical Wave Imaging With Machine Learning for Automated Isochrone Generation

Standard Electromechanical Wave Imaging isochrone generation relies on manual selection of zero-crossing (ZC) locations on incremental strain curves for a number of pixels in the segmented myocardium for each echocardiographic view and patient. When considering large populations, this becomes a time-consuming process, that can be limited by inter-observer variability and operator bias. In this study, we developed and optimized an automated ZC selection algorithm, towards a faster more robust isochrone generation approach. The algorithm either relies on heuristic-based baselines or machine learning classifiers. Manually generated isochrones, previously validated against 3D intracardiac mapping, were considered as ground truth during training and performance evaluation steps. The machine learning models applied herein for the first time were: i) logistic regression; ii) support vector machine (SVM); and iii) Random Forest. The SVM and Random Forest classifiers successfully identified accessory pathways in Wolff-Parkinson-White patients, characterized sinus rhythm in humans, and localized the pacing electrode location in left ventricular paced canines on the resulting isochrones. Nevertheless, the best performing classifier was proven to be Random Forest with a precision rising from 89.5% to 97%, obtained with the voting approach that sets a probability threshold upon ZC candidate selection. Furthermore, the predictivity was not dependent on the type of testing dataset it was applied to, contrary to SVM that exhibited a 5% drop in precision on the canine testing dataset. Finally, these findings indicate that a machine learning approach can reduce user variability and considerably decrease the durations required for isochrone generation, while preserving accurate activation patterns.

Self-supervised learning for accelerated 3D high-resolution ultrasound imaging

PURPOSE

Ultrasound (US) imaging has been widely used in diagnosis, image-guided intervention, and therapy, where high-quality three-dimensional (3D) images are highly desired from sparsely acquired two-dimensional (2D) images. This study aims to develop a deep learning-based algorithm to reconstruct high-resolution (HR) 3D US images only reliant on the acquired sparsely distributed 2D images.

METHODS

We propose a self-supervised learning framework using cycle-consistent generative adversarial network (cycleGAN), where two independent cycleGAN models are trained with paired original US images and two sets of low-resolution (LR) US images, respectively. The two sets of LR US images are obtained through down-sampling the original US images along the two axes, respectively. In US imaging, in-plane spatial resolution is generally much higher than through-plane resolution. By learning the mapping from down-sampled in-plane LR images to original HR US images, cycleGAN can generate through-plane HR images from original sparely distributed 2D images. Finally, HR 3D US images are reconstructed by combining the generated 2D images from the two cycleGAN models.

RESULTS

The proposed method was assessed on two different datasets. One is automatic breast ultrasound (ABUS) images from 70 breast cancer patients, the other is collected from 45 prostate cancer patients. By applying a spatial resolution enhancement factor of 3 to the breast cases, our proposed method achieved the mean absolute error (MAE) value of 0.90 ± 0.15, the peak signal-to-noise ratio (PSNR) value of 37.88 ± 0.88 dB, and the visual information fidelity (VIF) value of 0.69 ± 0.01, which significantly outperforms bicubic interpolation. Similar performances have been achieved using the enhancement factor of 5 in these breast cases and using the enhancement factors of 5 and 10 in the prostate cases.

CONCLUSIONS

We have proposed and investigated a new deep learning-based algorithm for reconstructing HR 3D US images from sparely acquired 2D images. Significant improvement on through-plane resolution has been achieved by only using the acquired 2D images without any external atlas images. Its self-supervision capability could accelerate HR US imaging.

Therapeutic efficacy and cardioprotection of nucleolin-targeted doxorubicin-loaded ultrasound nanobubbles in treating triple-negative breast cancer

Targeted lipid nanobubbles as theranostic ultrasound molecular probes with both targeted contrast-enhanced ultrasound molecular imaging and synergistic treatment capabilities are expected to overcome severe challenges in the diagnosis and treatment of refractory triple-negative breast cancer (TNBC). In this study, AS1411 aptamer-functionalised nucleolin-targeted doxorubicin-loaded lipid nanobubbles (AS1411-DOX-NBs) were constructed, and their physicochemical properties as well as anti-tumour and cardioprotective efficacies were systematically tested and evaluated. The results showed that AS1411-DOX-NBs can carry and maintain the physicochemical and pharmacodynamic properties of doxorubicin (DOX) and show stronger tumour cell-killing abilityin vitroby increasing the active uptake of drugs. AS1411-DOX-NBs also significantly inhibited the growth of TNBC xenografts while maintaining the weight and health of the mice. Echocardiography and pathological examination further confirmed that AS1411-DOX-NBs effectively caused tumour tissue apoptosis and necrosis while reducing DOX-induced cardiotoxicity. The AS1411-DOX-NBs constructed in this study enable both targeted contrast-enhanced ultrasound molecular imaging and synergistic therapeutic efficacy and can be used as safe and efficient theranostic ultrasound molecular probes for the diagnosis and treatment of TNBC.

Applications of Micro/Nanotechnology in Ultrasound-based Drug Delivery and Therapy for Tumor

Ultrasound has been broadly used in biomedicine for both tumor diagnosis as well as therapy. The applications of recent developments in micro/nanotechnology promote the development of ultrasound-based biomedicine, especially in the field of ultrasound-based drug delivery and tumor therapy. Ultrasound can activate nano-sized drug delivery systems by different mechanisms for ultrasound- triggered on-demand drug release targeted only at the tumor sites. Ultrasound Targeted Microbubble Destruction (UTMD) technology can not only increase the permeability of vasculature and cell membrane via sonoporation effect but also achieve in situ conversion of microbubbles into nanoparticles to promote cellular uptake and therapeutic efficacy. Furthermore, High Intensity Focused Ultrasound (HIFU), or Sonodynamic Therapy (SDT), is considered to be one of the most promising and representative non-invasive treatment for cancer. However, their application in the treatment process is still limited due to their critical treatment efficiency issues. Fortunately, recently developed micro/nanotechnology offer an opportunity to solve these problems, thus improving the therapeutic effect of cancer. This review summarizes and discusses the recent developments in the design of micro- and nano- materials for ultrasound-based biomedicine applications.

Characterization of interventional photoacoustic imaging (iPAI) capabilities in biological tissues

BACKGROUND

Interventional photoacoustic imaging (iPAI) could improve ultrasound-guided minimally invasive procedures by enabling high precision needle steering, target detection, and molecular and physiologic tissue assessment. However, iPAI capabilities including visualization field, imaging depth, and spatial resolution are not well understood in biological tissues commonly encountered in clinical practice. Therefore, the potential clinical utility of iPAI remains unclear. We aim to experimentally determine iPAI capabilities in a variety of biological tissues, to assess its potential for clinical translation.

METHODS

We constructed an iPAI system capable of simultaneous real-time ultrasound (US) and photoacoustic imaging. This system delivers light directly into tissues using optical fiber integrated into a 16-gauge needle and detects photoacoustic signals with an external linear array ultrasound probe. iPAI's geometric visualization field, maximum imaging depth, and spatial resolution were experimentally determined in fat, muscle, kidney, and liver tissues by processing photoacoustic signal intensities of reference targets placed circumferentially around the fiber tip. The maximum detection depths of blood and indocyanine green (ICG), important common endogenous and exogenous contrast agents, respectively, were estimated in each tissue type by comparing their signal intensities with the reference target signal.

RESULTS

iPAI could be performed in real-time concurrently with US and achieved a nearly spherical visualization field centering around the optical fiber tip in all tissues. Maximum imaging depths from the fiber tip were 54.1 ± 1.3, 50.0 ± 1.5, 32.7 ± 1.1, and 16.9 ± 1.3 mm in fat, muscle, kidney, and liver tissues, respectively. Calculated maximum detection depths for blood were 41.5 ± 3.0, 39.5 ± 2.1, 24.4 ± 4.0, and 8.6 ± 2.0 mm and detection depths for ICG at 0.05  mg/mL concentration were 46.6 ± 2.5, 42.6 ± 1.4, 28.2 ± 3.9, and 12.1 ± 1.5 mm in fat, muscle, kidney, and liver, respectively. Sub-100μ m axial resolution and submillimeter lateral resolution were achieved in all tissues, and resolution did not significantly vary with distance from the fiber tip.

CONCLUSIONS

Interventional photoacoustic imaging (iPAI) allows real-time visualization of a circumferential volume of tissue around an optical fiber tip, with submillimeter spatial resolution and tissue-dependent imaging depth. Our data strongly support further development of clinical iPAI systems as they could improve needle steering, target detection, and molecular and physiologic tissue assessment during minimally invasive procedures.

The role of point of care ultrasound in radiology department: update and prospective. A statement of Italian college ultrasound

Ultrasound, in its new point-of-care conception, has been called the stethoscope of the future. Goal-directed bedside ultrasound examination, performed by a healthcare provider to answer a specific diagnostic question or guide an invasive procedure, is currently revolutionizing medical practice. It is used by various specialties in multiple clinical contexts for procedural, diagnostic, and screening applications. Point-of-care ultrasound is also a strategic technique in clinical radiology; it brings the radiologist closer to the patient (in interaction and understanding) like interventional and angiography procedures and, as an integrative imaging modality, is a vital radiological tool for decision-making in many situations. In this commentary, we present our observations on the use of ultrasound, in a sincere appeal to refrain from omitting ultrasound as a diagnostic technique in this era of deep professional change, in which radiologists must return to being a true clinical semiologist.

Visualization of Microvascular Angiogenesis Using Dual-Frequency Contrast-Enhanced Acoustic Angiography: A Review

Cancerous tumor growth is associated with the development of tortuous, chaotic microvasculature, and this aberrant microvascular morphology can act as a biomarker of malignant disease. Acoustic angiography is a contrast-enhanced ultrasound technique that relies on superharmonic imaging to form high-resolution 3-D maps of the microvasculature. To date, acoustic angiography has been performed with dual-element transducers that can achieve high contrast-to-tissue ratio and resolution in pre-clinical small animal models. In this review, we first describe the development of acoustic angiography, including the principle, transducer design, and optimization of superharmonic imaging techniques. We then detail several preclinical applications of this microvascular imaging method, as well as the current and future development of acoustic angiography as a pre-clinical and clinical diagnostic tool.

Evaluation of low-intensity pulsed ultrasound on doxorubicin delivery in 2D and 3D cancer cell cultures

The aim of our study was to evaluate the influence of low-intensity pulsed US on the delivery of doxorubicin (DOX) into MDA-MB-231 triple-negative breast cancer and A549 non-small cell lung cancer cell 2D and 3D cultures. US with pulse repetition frequency of 10 Hz and 1 MHz center frequency was generated with peak negative pressure of 0.5 MPa and 50% duty cycle. SonoVue microbubbles were used. Spheroids were formed using 3D Bioprinting method. DOX delivery in 2D and 3D cultures was assessed using fluorescence microscopy. US without the addition of microbubbles did not enhance the penetration of DOX into monolayer-cultured cells and tumor spheroids. In the presence of microbubbles US improved the delivery of DOX into the edge end middle zones of A549 and MDA-MB-231 spheroids. Application of low-intensity pulsed US in combination with microbubbles may be a promising approach to enhance the delivery of DOX into tumor spheroids.

Spleen-left kidney ratio and liver-right kidney ratio: novel measures of normal liver, spleen, and kidney dimensions in Southwestern Nigerian children

PURPOSE

Figures can sometimes be difficult to handle when considering whether the ultrasound dimensions of the liver, spleen, and kidneys of children are within normal limits in a typical clinical setting. Therefore, it is imperative to think of a simple measure that can be easily adopted when the question of whether these organs are enlarged or reduced in size is to be answered by a sonologist. We hypothesize that the liver-right kidney ratio and spleen-left kidney ratio are constant, regardless of age and sex among children, provided that the reference organ is not diseased.

METHODS

This observational, cross-sectional study was carried out in public primary schools in the Ogbomoso metropolis. A total of 1000 apparently healthy children aged 5-13 were subjected to abdominal sonography from July 2016 to December 2016. The length of the liver, spleen, and both kidneys as well as the anthropometric parameters of each subject were obtained and correlated. Data were analyzed using SPSS version 20.

RESULTS

The SPL:LKL (spleen to left kidney length) ratio varies with age, and this is statistically significant in female subjects (p = 0.042), but not in males (p = 0.360). The RLL:RKL (right lobe of the liver to right kidney length) ratio of 1.19 ± 0.18 does not significantly vary with age or sex (p = 0.337).

CONCLUSION

The SPL:LKL ratio of 1.11 ± 0.18 can be only used as a measure of normality of organ dimensions in males. On the contrary, the RLL:RKL of 1.19 ± 0.18 can be used in all children, regardless of sex, provided that the reference organ is normal.

A randomized controlled trial of Chinese traditional medicine Dachengqi Decoction in the treatment of postoperative intestinal function recovery

BACKGROUND

Intestinal dysfunction is not conducive to the recovery of patients after surgery. It is particularly important to restore the intestinal function as soon as possible. In recent years, ultrasonic drug penetration therapy as a new type of non-invasive therapy has been used to solve this problem, but its efficacy has not been confirmed.

METHODS

Single-centre, parallel, randomized controlled clinical trial in China that included 184 patients undergoing laparoscopic gastrointestinal surgery. Ninety-one participants were randomly assigned to low-frequency ultrasound and electric pulses for transdermal drug delivery with Dachengqi Decoction (DCQD) (intervention group), and 90 were assigned to the control group after laparoscopic gastrointestinal surgery. The primary outcome was time to first flatus after surgery (by patient's subjective feeling). Secondary outcomes assessed the recovery time of bowel movement, time of the first defecation, postoperative gastrointestinal complications (e.g., nausea, vomit, and bloating), days of hospitalization and treatment costs.

RESULTS

Of 184 patients, 181 (98.4%) completed the trial. The sociodemographic characteristics and efficiency data were comparable in the two groups at baseline. The intervention group had a shorter mean time of bowel movement recovery than the control group [29.4 h (IQR, 22.0-35.0 h) vs. 33.7 h (IQR, 24.0-40.0 h; P=0.005)] and a shorter mean time to first flatus after surgery [35.8 h (IQR, 23.1-46.6 h) vs. 46.7 h (IQR, 25.9-61.3 h; P=0.012)]. Postoperative gastrointestinal reactions (e.g., nausea, vomit, and bloating) occurred in 28.6% in the intervention group and 43.3% in the control group (P=0.038). Two patients in the intervention group had electrical tingling sensations, and one patient had a skin rash during the treatment. There were no significant differences in the occurrence rates of AEs or SAE, days of hospitalization and treatment costs between the two groups.

CONCLUSIONS

Low-frequency ultrasound and electric pulses for transdermal drug delivery with DCQD can shorten the time of bowel movement recovery and accelerate first anal exhaust after laparoscopic gastrointestinal surgery.

TRAIL REGISTRATION

Chictr.org.cn Identifier: ChiCTR-IPR-17013630.

Pulsatile Drug Delivery System Triggered by Acoustic Radiation Force

Since biological systems exhibit a circadian rhythm (24-hour cycle), they are susceptible to the timing of drug administration. Indeed, several disorders require a therapy that synchronizes with the onset of symptoms. A targeted therapy with spatially and temporally precise controlled drug release can guarantee a considerable gain in terms of efficacy and safety of the treatment compared to traditional pharmacological methods, especially for chronotherapeutic disorders. This paper presents a proof of concept of an innovative pulsatile drug delivery system remotely triggered by the acoustic radiation force of ultrasound. The device consists of a case, in which a drug-loaded gel can be embedded, and a sliding top that can be moved on demand by the application of an acoustic stimulus, thus enabling drug release. Results demonstrate for the first time that ultrasound acoustic radiation force (up to 0.1 N) can be used for an efficient pulsatile drug delivery (up to 20 μg of drug released for each shot).