Ultrasound Localization Research Articles

The value of thoracoscopic ultrasound for the localization of ground-glass opacities with incomplete lung collapse in video-assisted thoracoscopic surgery.

Video-assisted thoracoscopic surgery (VATS) has been widely used for the resection of lung lesions. However, it is difficult to palpate or see small lesions, especially ground-glass opacities (GGOs) during VATS. Thoracoscopic ultrasound has definite value in locating pulmonary parenchymal nodules. However, due to the air in the lung parenchyma, its wide application is limited. This study investigated the value of thoracoscopic ultrasound for the localization of GGOs with incomplete lung collapse in VATS. A retrospective analysis was conducted on patients diagnosed with ground-glass nodules (GGNs) on computed tomography (CT) at Liaoning Province Tumor Hospital from November 2018 to August 2019, who underwent thoracoscopic ultrasound localization and VATS. Screening was conducted for patients who did not achieve complete collapse of the lungs after natural collapse during surgery, the success rate was calculated, and preoperative CT features and thoracoscopic ultrasound features of GGNs were summarized and analyzed. The success rate of GGOs' localization by thoracoscopic ultrasound in incomplete collapse lung was 56.67%. Of all preoperative CT features, only the distance between the nodule and the pleura was statistically different (P=0.001). The closer the GGO was to the pleura, the easier it was to be detected. Thoracoscopic ultrasound could effectively locate GGO in the condition of incomplete lung collapse and not affect the surgical field, especially when the GGO is close to the pleura, which provides an alternative method for GGO localization in patients who cannot achieve complete lung collapse during VATS.

Whole Organ Volumetric Sensing Ultrasound Localization Microscopy for Characterization of Kidney Structure.

Glomeruli are the filtration units of the kidney and their function relies heavily on their microcirculation. Despite its obvious diagnostic importance, an accurate estimation of blood flow in the capillary bundle within glomeruli defies the resolution of conventional imaging modalities. Ultrasound Localization Microscopy (ULM) has demonstrated its ability to image in-vivo deep organs in the body. Recently, the concept of sensing ULM or sULM was introduced to classify individual microbubble behavior based on the expected physiological conditions at the micrometric scale. In the kidney of both rats and humans, it revealed glomerular structures in 2D but was severely limited by planar projection. In this work, we aim to extend sULM in 3D to image the whole organ and in order to perform an accurate characterization of the entire kidney structure. The extension of sULM into the 3D domain allows better localization and more robust tracking. The 3D metrics of velocity and pathway angular shift made glomerular mask possible. This approach facilitated the quantification of glomerular physiological parameter such as an interior traveled distance of approximately 7.5 ±0.6 microns within the glomerulus. This study introduces a technique that characterize the kidney physiology which can serve as a method to facilite pathology assessment. Furthermore, its potential for clinical relevance could serve as a bridge between research and practical application, leading to innovative diagnostics and improved patient care.

Effectiveness of sacral neuromodulation with 3D printing and ultrasound localization for treating neurogenic bladder in patients with pelvic structural anomalies

ObjectiveTo assess the efficacy and safety of Sacral Neuromodulation (SNM) in conjunction with 3D printing and ultrasound localization for treating patients with neurogenic bladder and pelvic structural anomalies. MethodsThis prospective study involved 56 patients diagnosed with neurogenic bladder and pelvic anomalies. They were treated with SNM from January 2022 to December 2023. Patients were split into study and control groups based on the origins of their pelvic anomalies, with each group comprising 28 individuals. The study group underwent preoperative 3D printing and intraoperative ultrasound-assisted puncture, whereas the control group utilized intraoperative X-ray-assisted puncture. Metrics compared included the number of punctures, average puncture duration, intraoperative SNS tuning time, effective initial voltage, postoperative testing duration, phase two conversion rates, and the incidence of bleeding and infection. ResultsThe study group experienced significantly fewer punctures compared to the control group (2.21 ± 0.92 vs 9.29 ± 4.37) (P < 0.01), had a shorter average puncture duration (5.71 ± 2.69 min vs. 28.60 ± 9.69 min) (P < 0.01), and required a lower effective initial voltage (0.87 ± 0.23v vs. 1.38 ± 0.52v) (P < 0.01). X-ray exposure was eliminated in the study group, in contrast to 11.51 ± 4.62 mAs in the control group(P < 0.01). The phase two implantation rates were 67.8 % for the study group and 57.1 % for the control group(P = 0.04). No surgery-related complications occurred in either group. ConclusionCombining preoperative 3D printing with intraoperative ultrasound-assisted puncture significantly improves the accuracy of SNM placement, reduces surgery time, and eliminates X-ray exposure for both doctors and patients. This method is both safe and effective for patients with neurogenic bladder and pelvic anomalies.

Overlapping microbubble localization based on multiscale statistical features for ultrasound super-resolution imaging

Ultrasound localization microscopy enables visualization of the microvasculature through the localization and tracking of isolated microbubbles (MBs). Currently, improving the precision for localizing overlapping MBs at high concentration to decrease the acquisition time remains a challenge. This study introduces multiscale statistical feature localization (MSFL) to address this challenge. First, the overlapping MBs are separated by local energy and blobness filters constructed from the Hessian matrix of texture features and its eigenvalues. Then, multiscale and orientation kernels are modeled to adapt to the appearance of various MBs; their amplitude and gradient convolution response results jointly determine the credibility of the MBs. Finally, precise MB localization is obtained by the weighted average of the local maxima of the response maps at each scale to achieve super-resolution (SR) imaging. The proposed method is evaluated using simulation and in-vivo datasets. Simulation results demonstrate that MSFL can adapt to asymmetric Gaussian distributions and improve overlapping MB localization, with a 9.28 % improvement in localization precision and a 5 % greater Jaccard index compared to the Gaussian fitting method. Additionally, the strong robustness of MSFL under noise interference maintains its ability to locate more MBs, and reconstruct the same saturation vessels with 36 % acquisition time reduction compared to Radial symmetry method. For SR images, MSFL can draw smoother interlaced vessels when more overlapping MBs are present, and quickly reconstructs more saturated in vivo microvasculature with a resolution is 10.5 μm(λ/10). These results validate the advantages of MSFL in achieving precise SR imaging with a short acquisition time.

Advanced deep-tissue imaging and manipulation enabled by biliverdin reductase knockout.

We developed near-infrared (NIR) photoacoustic and fluorescence probes, as well as optogenetic tools from bacteriophytochromes, and enhanced their performance using biliverdin reductase-A knock-out model (Blvra-/-). Blvra-/- elevates endogenous heme-derived biliverdin chromophore for bacteriophytochrome-derived NIR constructs. Consequently, light-controlled transcription with IsPadC-based optogenetic tool improved up to 25-fold compared to wild-type cells, with 100-fold activation in Blvra-/- neurons. In vivo , light-induced insulin production in Blvra-/- reduced blood glucose in diabetes by ∼60%, indicating high potential for optogenetic therapy. Using 3D photoacoustic, ultrasound, and two-photon fluorescence imaging, we overcame depth limitations of recording NIR probes. We achieved simultaneous photoacoustic imaging of DrBphP in neurons and super-resolution ultrasound localization microscopy of blood vessels ∼7 mm deep in the brain, with intact scalp and skull. Two-photon microscopy provided cell-level resolution of miRFP720-expressing neurons ∼2.2 mm deep. Blvra-/- significantly enhances efficacy of biliverdin-dependent NIR systems, making it promising platform for interrogation and manipulation of biological processes.

Capillary-scale Microvessel Imaging with High-frequency Ultrasound Localization Microscopy in Mouse Brain.

Ultrasound localization microscopy is a super-resolution vascular imaging technique which has garnered substantial interest as a tool for small animal neuroimaging, neuroscience research, and the characterization of vascular pathologies. In the pursuit of increasingly high-fidelity reconstructions of microvasculature, there remains several outstanding questions concerning this sub-diffraction imaging technology, including the accurate reconstruction of microvessels approaching the capillary scale and the pragmatic challenges associated with long data acquisition times. In the context of small animal neurovascular imaging, we posit that increasing the ultrasound imaging frequency is a straightforward approach to enable higher concentrations of microbubble contrast agents, thus increasing the likelihood of microvascular/capillary mapping and decreasing the imaging duration. We demonstrate that higher frequency imaging results in improved ULM fidelity and more efficient microbubble localization due to a smaller microbubble point-spread function that is easier to localize, and which can achieve a higher localizable concentration within the same unit volume of tissue. A select example of in vivo capillary-level vascular reconstruction is demonstrated for the highest frequency imaging probe, which has substantial implications for neuroscientists investigating microvascular function in disease states, regulation, and brain development. High frequency ULM yielding a spatial resolution of 7.1μm, as measured by Fourier ring correlation, throughout the entire depth of the brain, highlighting this technology as a highly relevant tool for neuroimaging research.

Super-resolution ultrasound imaging reveals temporal cerebrovascular changes with disease progression in female 5×FAD mouse model of Alzheimer's disease: correlation with pathological impairments

Vascular dysfunction is closely associated with the progression of Alzheimer's disease (AD). A critical research gap exists that no studies have explored the invivo temporal changes of cerebrovascular alterations with AD progression in mouse models, encompassing both structure and flow dynamics at micron-scale resolution across the early, middle, and late stages of the disease. In this study, ultrasound localisation microscopy (ULM) was applied to image the cerebrovascular alterations of the transgenic female 5×FAD mouse model across different stages of disease progression: early (4 months), moderate (7 months), and late (12 months). Age-matched non-transgenic (non-Tg) littermates were used as controls. Immunohistology examinations were performed to evaluate the influence of disease progression on the β-amyloid (Aβ) load and microvascular alterations, including morphological changes and the blood-brain barrier (BBB) leakage. Our findings revealed a significant decline in both vascular density and flow velocity in the retrosplenial cortex of 5×FAD mice at an early stage, which subsequently became more pronounced in the visual cortex and hippocampus as the disease progressed. Additionally, we observed a reduction in vascular length preceding diminished flow velocities in cortical penetrating arterioles during the early stages. The quantification of vascular metrics derived from ULM imaging showed significant correlations with those obtained from vascular histological images. Immunofluorescence staining identified early vascular abnormalities in the retrosplenial cortex. As the disease advanced, there was an exacerbation of Aβ accumulation and BBB disruption in a regionally variable manner. The vascular changes observed through ULM imaging exhibited a negative correlation with amyloid load and were associated with the compromise of the BBB integrity. Through high-resolution, invivo imaging of cerebrovasculature, this study reveals significant spatiotemporal dysfunction in cerebrovascular dynamics accompanying disease progression in a mouse model of AD, enhancing our understanding of its pathophysiology. This study is supported by grants from National Key Research and Development Program of China (2020YFA0908800), National Natural Science Foundation of China (12074269, 82272014, 82327804, 62071310), Shenzhen Basic Science Research (20220808185138001, JCYJ20220818095612027, JCYJ20210324093006017), STI 2030-Major Projects (2021ZD0200500) and Guangdong Natural Science Foundation (2024A1515012591, 2024A1515011342).

Value of ultrathin bronchoscope in improving the endobronchial ultrasound localization rate and diagnosing peripheral pulmonary nodules by cryobiopsy

BackgroundA 3.0-mm ultrathin bronchoscope (UTB) with a 1.7-mm working channel provides better accessibility to peripheral bronchi. A 4.0-mm thin bronchoscope with a larger 2.0-mm working channel facilitates the use of a guide sheath (GS), ensuring repeated sampling from the same location. The 1.1-mm ultrathin cryoprobe has a smaller diameter, overcoming the limitation of the size of biopsy instruments used with UTB. In this study, we compared the endobronchial ultrasound localization rate and diagnostic yield of peripheral lung lesions by cryobiopsy using UTB and thin bronchoscopy combined with GS.MethodsWe retrospectively evaluated 133 patients with peripheral pulmonary lesions with a diameter less than 30 mm who underwent bronchoscopy with either thin bronchoscope or UTB from May 2019 to May 2023. A 3.0-mm UTB combined with rEBUS was used in the UTB group, whereas a 4.0-mm thin bronchoscope combined with rEBUS and GS was used for the thin bronchoscope group. A 1.1-mm ultrathin cryoprobe was used for cryobiopsy in the two groups.ResultsAmong the 133 patients, peripheral pulmonary nodules in 85 subjects were visualized using r-EBUS. The ultrasound localization rate was significantly higher in the UTB group than in the thin bronchoscope group (96.0% vs. 44.6%, respectively; P < 0.001). The diagnostic yield of cryobiopsy specimens from the UTB group was significantly higher compared to the thin bronchoscope group (54.0% vs. 30.1%, respectively; p = 0.006). Univariate analysis demonstrated that the cryobiopsy diagnostic yields of the UTB group were significantly higher for lesions ≤ 20 mm, benign lesions, upper lobe lesions, lesions located lateral one-third from the hilum, and lesions without bronchus sign.ConclusionsUltrathin bronchoscopy combined with cryobiopsy has a superior ultrasound localization rate and diagnostic yield compared to a combination of cryobiopsy and thin bronchoscopy.

Effects of meridian sinew tuina after identifying the treatment area under ultrasound localization combined with greater and third occipital nerve injections in cervicogenic headache: a randomized controlled trial protocol.

Cervicogenic headache (CEH) is a secondary headache characterized by chronic, unilateral headache. Ultrasound-guided injections of the greater occipital nerve (GON) and the third occipital nerve (TON) are effective in the treatment of CEH, as is meridian sinew tuina for the treatment of CEH, but the evidence of clinical efficacy of combining these two therapies is valid. Therefore, we have designed a randomized controlled trial with the aim of investigating the efficacy and safety of ultrasound localization meridian sinew tuina combined with GON and TON injections for the treatment of CEH. In this study, we enroll 60 patients experiencing CEH. The control group receives ultrasound-guided injections of GON and TON. The intervention group is treated with ultrasound localization meridian sinew tuina combined with the injection of GON and TON. Meridian sinew tuina is performed once a day for 30 min for 3 days. The primary observational index includes the Short-Form of McGill Pain Questionnaire (SF-MPQ). The Secondary outcomes include Cervical Range of Motion (ROM) and Medical Infrared Thermography (MIT). MIT is used to measure the change in skin temperature in the area of the patient's meridian sinew tuina treatment of GON and TON before and after the intervention. There are 5 time points assessed as baseline, day 3, day 15, day 30, and day 60. This study proposes to combine ultrasound-guided injections of GON and TON for the treatment of CEH after identifying the treatment area of meridian sinew tuina under ultrasound localization. Meanwhile, MIT is utilized to provide objective evidence of the efficacy of CEH. ChiCTR2300076128.

A Radiological Clip Design Using Ultrasound Identification to Improve Localization.

We demonstrate the use of ultrasound to receive an acoustic signal transmitted from a radiological clip designed from a custom circuit. This signal encodes an identification number and is localized and identified wirelessly by the ultrasound imaging system. We designed and constructed the test platform with a Teensy 4.0 microcontroller core to detect ultrasonic imaging pulses received by a transducer embedded in a phantom, which acted as the radiological clip. Ultrasound identification (USID) signals were generated and transmitted as a result. The phantom and clip were imaged using an ultrasonic array (Philips L7-4) connected to a Verasonics™ Vantage 128 system operating in pulse inversion (PI) mode. Cross-correlations were performed to localize and identify the code sequences in the PI images. USID signals were detected and visualized on B-mode images of the phantoms with up to sub-millimeter localization accuracy. The average detection rate across 30,400 frames of ultrasound data was 98.1%. The USID clip produced identifiable, distinguishable, and localizable signals when imaged. Radiological clips are used to mark breast cancer being treated by neoadjuvant chemotherapy (NAC) via implant in or near treated lesions. As NAC progresses, available marking clips can lose visibility in ultrasound, the imaging modality of choice for monitoring NAC-treated lesions. By transmitting an active signal, more accurate and reliable ultrasound localization of these clips could be achieved and multiple clips with different ID values could be imaged in the same field of view.

RF-ULM: Ultrasound Localization Microscopy Learned From Radio-Frequency Wavefronts.

In Ultrasound Localization Microscopy (ULM), achieving high-resolution images relies on the precise localization of contrast agent particles across a series of beamformed frames. However, our study uncovers an enormous potential: The process of delay-and-sum beamforming leads to an irreversible reduction of Radio-Frequency (RF) channel data, while its implications for localization remain largely unexplored. The rich contextual information embedded within RF wavefronts, including their hyperbolic shape and phase, offers great promise for guiding Deep Neural Networks (DNNs) in challenging localization scenarios. To fully exploit this data, we propose to directly localize scatterers in RF channel data. Our approach involves a custom super-resolution DNN using learned feature channel shuffling, non-maximum suppression, and a semi-global convolutional block for reliable and accurate wavefront localization. Additionally, we introduce a geometric point transformation that facilitates seamless mapping to the B-mode coordinate space. To understand the impact of beamforming on ULM, we validate the effectiveness of our method by conducting an extensive comparison with State-Of-The-Art (SOTA) techniques. We present the inaugural in vivo results from a wavefront-localizing DNN, highlighting its real-world practicality. Our findings show that RF-ULM bridges the domain shift between synthetic and real datasets, offering a considerable advantage in terms of precision and complexity. To enable the broader research community to benefit from our findings, our code and the associated SOTA methods are made available at https://github.com/hahnec/rf-ulm.

TC-ULM: Temporal Correlation-based Ultrasound Localization Microscopy by Deep Learning

Abstract Ultrasound Localization Microscopy (ULM) transcends the limitations of acoustic diffraction by pinpointing minuscule microbubbles (MBs), enabling detailed visualization of microvasculature at resolutions finer than the wavelength. Current approaches in ULM, inclusive of both conventional and deep learning techniques, predominantly analyze data from the ultrasound frame plane (x-y). This leaves the valuable information in the time dimension (t), especially between consecutive frames, largely untapped. This paper introduces a novel approach, Temporal Correlation ULM (TC-ULM), which harnesses temporal correlations within the y-t plane for enhanced ULM imaging. By extracting the variations of MBs over time through a sophisticated deep learning model, TC-ULM effectively reconstructs ULM images with superior quality. Empirical evidence from numerical simulations and in vivo experiments reveals that MBs exhibit a discernible pattern of continuity and local linkage across adjacent frames in the y-t plane. This key finding allows for a significant reduction in the number of frames needed for ULM, without compromising the spatial resolution. Thus, TC-ULM emerges as an innovative technique that not only retains image quality but also potentially expedites the ULM imaging process, marking a notable shift from traditional x-y plane processing methods.

Ultrasound localization microscopy based microvasculature evaluation of spinal cord injury with varying severities

Abstract The vasculature plays an important role in spinal cord function. Traumatic spinal cord injury (SCI) often results in a vascular disruption after primary insult, following secondary injury in neighbouring tissue. However, standard angiography methods such as digital subtraction angiography (DSA) still have difficulties in microvasculature imaging, especially for spinal cord with small size and non-rigid motion. The ultrasound localization microscopy (ULM) disrupts the compromise between penetration and resolution and provides super-resolution images of microvasculature. In this study, the applicability of the ULM for in vivo SCI evaluation was investigated on rat models with varying severities (different compression time durations correspond to mild and severe injury). Ultrasonic parameters including vessel saturation and blood velocity were measured via ULM. Basso, Beattie, Bresnahan (BBB) locomotion test was applied for injury severity validation and comparison with ultrasonic measurements.

Vessel recovery using ultrasound localisation microscopy: An in silico comparative study between minimum variance and delay-and-sum beamformers

The use of particle localisation and tracking algorithms on Contrast Enhanced Ultrasound (CEUS) or other ultrasound mode image data containing sparse microbubble (MB) populations, can produce super-resolved vascularization maps. Typically such data stem from conventional delay and sum (DAS) beamforming that is used widely in ultrasound imaging modes. Recently, adaptive beamforming has shown significant improvement in spatial resolution, but its value to super-resolution image analysis approaches is not fully understood. The in silico study here evaluates the performance of combining minimum variance beamformers (MV BF), established to provide improved lateral resolution, compared to DAS BFs with single particle detection. The isolated effect of a range of simplified image-affecting factors such as flow profile, pulse length, noise, vessel separations and data availability is considered. The study aims to assess the vessel recovery performance using the different beamformers and investigate the link with MB detection and localisation. The MV BF was shown to provide improved microvessel position accuracy compared to conventional DAS BFs. In particular, vessel separations between 0.3–4 λ provided superior localisation uncertainty with the MV. In addition, for a separation of 0.36λ, vessel recovery was achieved with both methods but the use of MV eliminated artifacts that appear as additional vessels. These results were found to be linked to improved MB detection and localisation for the MV BF, which is proposed as suitable for testing in Ultrasound Localisation Microscopy (ULM) imaging using patient data.

An adaptive spatiotemporal filter for ultrasound localization microscopy based on density canopy clustering

Ultrasound Localization Microscopy (ULM) facilitates structural and hemodynamic imaging of microvessels with a resolution of tens of micrometers. In ULM, the extraction of effective microbubble signals is crucial for image quality. Singular Value Decomposition (SVD) is currently the most prevalent method for microbubble signal extraction in ULM. Most existing ULM studies employ a fixed SVD filter threshold using empirical values which will lead to imaging quality degradation due to the insufficient separation of blood signals. In this study, we propose an adaptive and non-threshold SVD filter based on canopy-density clustering, termed DCC-SVD. This filter automatically classifies the components of the SVD based on the density of their spatiotemporal features, eliminating the need for parameter selection. In in vitro tube phantom, DCC-SVD demonstrated its ability to adaptive separation of blood and bubble signal at varying microbubble concentrations and flow rates. We compared the proposed DCC-SVD method with the Block-match 3D (BM3D) filter and a classical adaptive method called spatial similarity matrix (SSM), using concentration-variable in vivo rat brain data, as well as open-source rat kidney and mouse tumor datasets. The proposed DCC-SVD improved the global spatial resolution by approximately 4 μm from 30.39 μm to 26.02 μm. It also captured vessel structure absent in images obtained by other methods and yielded a smoother vessel intensity profile, making it a promising spatiotemporal filter for ULM imaging.

ULTRA-SR Challenge: Assessment of Ultrasound Localization and TRacking Algorithms for Super-Resolution Imaging.

With the widespread interest and uptake of super-resolution ultrasound (SRUS) through localization and tracking of microbubbles, also known as ultrasound localization microscopy (ULM), many localization and tracking algorithms have been developed. ULM can image many centimeters into tissue in-vivo and track microvascular flow non-invasively with sub-diffraction resolution. In a significant community effort, we organized a challenge, Ultrasound Localization and TRacking Algorithms for Super-Resolution (ULTRA-SR). The aims of this paper are threefold: to describe the challenge organization, data generation, and winning algorithms; to present the metrics and methods for evaluating challenge entrants; and to report results and findings of the evaluation. Realistic ultrasound datasets containing microvascular flow for different clinical ultrasound frequencies were simulated, using vascular flow physics, acoustic field simulation and nonlinear bubble dynamics simulation. Based on these datasets, 38 submissions from 24 research groups were evaluated against ground truth using an evaluation framework with six metrics, three for localization and three for tracking. In-vivo mouse brain and human lymph node data were also provided, and performance assessed by an expert panel. Winning algorithms are described and discussed. The publicly available data with ground truth and the defined metrics for both localization and tracking present a valuable resource for researchers to benchmark algorithms and software, identify optimized methods/software for their data, and provide insight into the current limits of the field. In conclusion, Ultra-SR challenge has provided benchmarking data and tools as well as direct comparison and insights for a number of the state-of-the art localization and tracking algorithms.

Nonlinear simulation for contrast ultrasound imaging.

Contrast ultrasound (CUS) has received much interest because of its sensitivity enhancement for blood flow imaging. However, there is still a lack of nonlinear simulation method for CUS, as conventional simulators cannot deal with the microbubble acoustic nonlinearity. In this paper, a nonlinear simulation method of CUS is developed based on a combination strategy of the k-space pseudospectral method and Rayleigh-Plesset Marmottant model. Different contrast pulse sequence strategies as well as the radial modulation imaging are simulated and compared using the proposed method. For blood flow imaging, simulations under different scenarios such as power Doppler and ultrasound localization microscopy are also carried out. Furthermore, a face-to-face comparison is performed between simulations and phantom experiments to validate the proposed method.

In vivo ultrasound localization microscopy for high-density microbubbles

Ultrasound Localization Microscopy (ULM) surpasses the constraints imposed by acoustic diffraction, achieving sub-wavelength resolution visualization of microvasculature through the precise localization of minute microbubbles (MBs). Nonetheless, the analysis of densely populated regions with overlapping MB point spread responses introduces significant localization errors, limiting the use of technique to low-concentration conditions. This raises a trade-off issue between localization efficiency and MB density. In this work, we present a new deep learning framework that combines Transformer and U-Net architectures, termed ULM-TransUNet. As a non-linear model, it is able to learn the complex data patterns of overlapping MBs in dense conditions for accurate localization. To evaluate the performance of ULM-TransUNet, a series of numerical simulations and in vivo experiments are carried out. Numerical simulation results indicate that ULM-TransUNet achieves high-quality ULM imaging, with improvements of 21.93 % in detection rate, 17.36 % in detection precision, and 20.53 % in detection sensitivity, compared to previous state-of-the-art deep learning (DL) method (e.g., ULM-UNet). For the in vivo experiments, ULM-TransUNet achieves the highest spatial resolution (9.4 μm) and rapid inference speed (26.04 ms/frame). Furthermore, it consistently detects more small vessels and resolves closely spaced vessels more effectively. The outcomes of this work imply that ULM-TransUNet can potentially enhance the microvascular imaging performance on high-density MB conditions.

High-frequency ultrasound-assisted Mohs micrographic surgery for the treatment of dermatofibrosarcoma protuberans

BackgroundDermatofibrosarcoma protuberans (DFSP) is a superficial sarcoma characterized by infiltrative growth with tentacle-like borders. Mohs micrographic surgery (MMS) is the preferred treatment option for DFSP. However, the imprecise boundary localization in MMS leads to an increased number of Mohs layers required and a longer surgery time. High-frequency ultrasound has excellent tissue recognition capability for DFSP, allowing for precise boundary marking. Materials and MethodsIn this study, we retrospectively analyzed 14 cases of DFSP treated with MMS using preoperative ultrasound localization and three-dimensional reconstruction at Xiangya Hospital over the past 5 years. We also reviewed previous studies on MMS for DFSP treatment. ResultsIt was found that the average number of Mohs layers for patients after preoperative ultrasound localization was 1.57, ranging from 1 to 3, which was less than the previously reported 1.86 layers, ranging from 1 to 12. This effectively reduced the number of Mohs layers required. ConclusionsBy utilizing preoperative high-frequency ultrasound to determine the boundaries and depth of DFSP, the number of Mohs layers can be effectively reduced, leading to less workload for pathological examination, shorter operation time, and reduced surgical risks for patients. Ultrasound imaging data can be used for three-dimensional reconstruction, enabling less experienced Mohs surgeons to have a visual understanding of the morphology and extent of infiltration of the lesions. This aids in developing optimal surgical plans, smoothing the learning curve, and promoting the wider adoption of MMS.

Efficient Microbubble Trajectory Tracking in Ultrasound Localization Microscopy Using a Gated Recurrent Unit-Based Multitasking Temporal Neural Network.

Ultrasound Localization Microscopy (ULM), an emerging medical imaging technique, effectively resolves the classical trade-off between resolution and penetration inherent in traditional ultrasound imaging, opening up new avenues for noninvasive observation of the microvascular system. However, traditional microbubble tracking methods encounter various practical challenges. These methods typically entail multiple processing stages, including intricate steps like pairwise correlation and trajectory optimization, rendering real-time applications unfeasible. Furthermore, existing deep learning-based tracking techniques neglect the temporal aspects of microbubble motion, leading to ineffective modeling of their dynamic behavior. To address these limitations, this study introduces a novel approach called the Gated Recurrent Unit (GRU)-based Multitasking Temporal Neural Network (GRU-MT). GRU-MT is designed to simultaneously handle microbubble trajectory tracking and trajectory optimization tasks. Additionally, we enhance the nonlinear motion model initially proposed by Piepenbrock et al. to better encapsulate the nonlinear motion characteristics of microbubbles, thereby improving trajectory tracking accuracy. In this study, we perform a series of experiments involving network layer substitutions to systematically evaluate the performance of various temporal neural networks, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), GRU, Transformer, and its bidirectional counterparts, on the microbubble trajectory tracking task. Concurrently, the proposed method undergoes qualitative and quantitative comparisons with traditional microbubble tracking techniques. The experimental results demonstrate that GRU-MT exhibits superior nonlinear modeling capabilities and robustness, both in simulation and in vivo dataset. Additionally, it achieves reduced trajectory tracking errors in shorter time intervals, underscoring its potential for efficient microbubble trajectory tracking. Model code is open-sourced at https://github.com/zyt-Lib/GRU-MT.