Publications by authors named "Lopata R"

Photoacoustic imaging (PAI) is a developing image modality that benefits from light-matter interaction and low acoustic attenuation to provide functional information on tissue composition at relatively large depths. Several studies have reported the potential of dichroism-sensitive photoacoustic (DS-PA) imaging to expand PAI capabilities by obtaining morphological information of tissue regarding anisotropy and predominant orientation. However, most of these studies have limited their analysis to superficial scanning of samples, where fluence effects are negligible.

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Photoacoustic imaging creates light-induced ultrasonic signals to provide valuable information on internal body structures and tissue morphology non-invasively. A multi-aperture photoacoustic imaging (MP-PAI) system is an improvement over conventional photoacoustic imaging (PAI) systems in terms of resolution, contrast, and field of view. Previously, a prototype MP-PAI system was introduced based on multiple capacitive micromachined ultrasound transducers (CMUTs) with shared channels, such that each element in a CMUT shares its channel with its counterpart in other CMUTs.

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Currently, abdominal aortic aneurysms (AAAs) are treated based on the diameter of the aorta, however, a more robust patient-specific marker is needed. The mean thickness of the wall is a potential indicator for AAA rupture risk, which varies significantly within and between patients. So far, regional thickness has not been used in previous rupture risk analysis studies, since it is challenging to measure in CT, MRI, and non-invasive ultrasound (US).

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This study demonstrates high volume rate bistatic 3-D vascular strain imaging, to overcome well-known challenges caused by the anisotropic resolution and contrast inherent to ultrasound imaging. Approach. Using two synchronized 32x32 element matrix arrays (3.

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Image-based patient-specific rupture risk analysis for abdominal aortic aneurysms (AAAs) has shown considerable promise. However, clinical translation has been hampered by the use of invasive and costly imaging modalities. Despite being a promising alternative, ultrasound (US) makes a full analysis, including intraluminal thrombus (ILT), not trivial.

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Article Synopsis
  • This study focuses on improving the segmentation of abdominal aortic aneurysms (AAAs) and intraluminal thrombus (ILT) in ultrasound (US) images using a deep learning model called nnU-Net, due to previous challenges with low ILT-blood contrast in US imaging.
  • The nnU-Net model was trained on both ultrasound data and computed tomography (CT) data, with the CT-based training yielding the best results in accurately identifying AAA structures, outperforming traditional methods in metrics like DICE index and Hausdorff distance.
  • Despite the improved segmentation accuracy, the study noted that visibility issues at the lumen-ILT interface limit broader applicability, suggesting that future improvements
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Time-resolved three-dimensional ultrasound (3D + t US) is a promising imaging modality for monitoring abdominal aortic aneurysms (AAAs), providing their 3D geometry and motion. The lateral contrast of US is poor, a well-documented drawback which multi-perspective (MP) imaging could resolve. This study aims to show the feasibility of in vivo multi-perspective 3D + t ultrasound imaging of AAAs for improving the image contrast and displacement accuracy.

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Aims: Urbanization is related to non-communicable diseases such as congestive heart failure (CHF). Understanding the influence of diverse living environments on physiological variables such as heart rate variability (HRV) in patients with chronic cardiac disease may contribute to more effective lifestyle advice and telerehabilitation strategies. This study explores how machine learning (ML) models can predict HRV metrics, which measure autonomic nervous system responses to environmental attributes in uncontrolled real-world settings.

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Article Synopsis
  • Radiation-induced acoustics (RIA) could enhance imaging and dosimetry in radiology, but high noise levels necessitate averaging, leading to more radiation exposure for patients.
  • This paper introduces a discrete wavelet transform (DWT) filtering method to effectively denoise RIA signals, reducing the amount of averaging needed significantly—1000 times less for low-energy X-rays and 32 times less for high-energy X-rays.
  • The DWT approach showed robust performance across various radiation sources and matched low-pass filter results, paving the way for better clinical application of radiation-based acoustic imaging.
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Background: Personalized modeling is a promising tool to improve abdominal aortic aneurysm (AAA) rupture risk assessment. Computed tomography (CT) and quantitative flow (Q-flow) magnetic resonance imaging (MRI) are widely regarded as the gold standard for acquiring patient-specific geometry and velocity profiles, respectively. However, their frequent utilization is hindered by various drawbacks.

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Abdominal aortic aneurysms (AAAs) are rupture-prone dilatations of the aorta. In current clinical practice, the maximal diameter of AAAs is monitored with 2D ultrasound to estimate their rupture risk. Recent studies have shown that 3-dimensional and mechanical AAA parameters might be better predictors for aneurysm growth and rupture than the diameter.

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In this paper we introduce multi-aperture ultrasound imaging and elastography of the abdominal aorta. Monitoring of the geometry and growth of abdominal aortic aneurysms (AAA) is paramount for risk stratification and intervention planning. However, such an assessment is limited by the lateral lumen-wall contrast and resolution of conventional ultrasound.

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High frame rate ultrasound (US) imaging techniques in 3D are promising tools for capturing abdominal aortic aneurysms (AAAs) over time, however, with the limited number of channel-to-element connections current footprints are small, which limits the field of view. Moreover, the maximal steering angle of the ultrasound beams in transmit and the maximal receptance angle in receive are insufficient for capturing the curvy shape of the AAA. Therefore, an approach is needed towards large arrays.

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Spectral photoacoustic imaging (sPAI) is an emerging modality that allows real-time, non-invasive, and radiation-free assessment of tissue, benefiting from their optical contrast. sPAI is ideal for morphology assessment in arterial plaques, where plaque composition provides relevant information on plaque progression and its vulnerability. However, since sPAI is affected by spectral coloring, general spectroscopy unmixing techniques cannot provide reliable identification of such complicated sample composition.

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In multiaperture ultrasound, several ultrasound probes with different insonification angles are combined to increase the field of view and angular coverage of image structures. A full reconstruction incorporating all possible combinations of transmitting and receiving probes has been shown to improve resolution, contrast, and angular coverage beyond what can be achieved by the registration of single images from different probes. A major challenge in multiaperture imaging is the correct determination of relative probe locations.

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Cardiovascular strain imaging is continually improving due to ongoing advances in ultrasound acquisition and data processing techniques. The phantoms used for validation of new methods are often burdensome to make and lack flexibility to vary mechanical and acoustic properties. Simulations of US imaging provide an alternative with the required flexibility and ground truth strain data.

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The heterogeneity of progression of abdominal aortic aneurysms (AAAs) is not well understood. This study investigates which geometrical and mechanical factors, determined using time-resolved 3D ultrasound (3D + t US), correlate with increased growth of the aneurysm. The AAA diameter, volume, wall curvature, distensibility, and compliance in the maximal diameter region were determined automatically from 3D + t echograms of 167 patients.

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In the last decade, many advances have been made in high frame rate 3-D ultrasound imaging, including more flexible acquisition systems, transmit (TX) sequences, and transducer arrays. Compounding multiangle transmits of diverging waves has shown to be fast and effective for 2-D matrix arrays, where heterogeneity between transmits is key in optimizing the image quality. However, the anisotropy in contrast and resolution remains a drawback that cannot be overcome with a single transducer.

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To improve the assessment of carotid plaque vulnerability, a comprehensive characterization of their composition is paramount. Multispectral photoacoustic imaging (MSPAI) can provide plaque composition based on their absorption spectra. However, although various spectral unmixing methods have been developed to characterize different tissue constituents, plaque analysis remains a challenge since its composition is highly complex and diverse.

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The rising demand on high frame rate ultrasound imaging applications necessitates the development of fast algorithms for plane wave image reconstruction. We introduce a new class of plane wave reconstructions that relies on a relation between receive data and image data in the Radon domain. This relation is derived for arbitrary dimensions and validated on multiple two-dimensional plane wave data sets.

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Abdominal ultrasound image quality is hampered by phase aberration, that is mainly caused by the large speed-of-sound (SoS) differences between fat and muscle tissue in the abdominal wall. The mismatch between the assumed and actual SoS distribution introduces general blurring of the ultrasound images, and acoustic refraction can lead to geometric distortion of the imaged features. Large aperture imaging or dual-transducer imaging can improve abdominal imaging at deep locations by providing increased contrast and resolution.

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Despite the notable recent developments in the field of remote photoplethysmography (rPPG), extracting a reliable pulse rate variability (PRV) signal still remains a challenge. In this study, eight image-based photoplethysmography (iPPG) extraction methods (GRD, AGRD, PCA, ICA, LE, SPE, CHROM, and POS) were compared in terms of pulse rate (PR) and PRV features. The algorithms were made robust for motion and illumination artifacts by using ad hoc pre- and postprocessing steps.

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To improve abdominal aortic aneurysm (AAA) rupture risk assessment, a large, longitudinal study on AAA hemodynamics and biomechanics is necessary, using personalized fluid-structure interaction (FSI) modeling. 3-dimensional, time-resolved ultrasound (3D+t US) is the preferred image modality to obtain the patient-specific AAA geometry for such a study, since it is safe, affordable and provides temporal information. However, the 3D+t US field-of-view (FOV) is limited and therefore often fails to capture the inlet and aorto-iliac bifurcation geometry.

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Rupture risk estimation of abdominal aortic aneurysm (AAA) patients is currently based on the maximum diameter of the AAA. Mechanical properties that characterize the mechanical state of the vessel may serve as a better rupture risk predictor. Non-electrocardiogram-gated (non-ECG-gated) freehand 2D ultrasound imaging is a fast approach from which a reconstructed volumetric image of the aorta can be obtained.

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Ultrasound (US) imaging is a medical imaging modality that uses the reflection of sound in the range of 2-18 MHz to image internal body structures. In US, the frequency bandwidth (BW) is directly associated with image resolution. BW is a property of the transducer and more bandwidth comes at a higher cost.

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