| 1. |
- Augustin, Xenia, et al.
(författare)
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Estimating Mean Speed-of-Sound from Sequence-Dependent Geometric Disparities
- 2021
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Ingår i: INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS 2021). - : Institute of Electrical and Electronics Engineers (IEEE). - 9780738112091
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Konferensbidrag (refereegranskat)abstract
- In ultrasound beamforming, focusing time delays are typically computed with a spatially constant speed-of-sound (SoS) assumption. A mismatch between beamforming and true medium SoS then leads to aberration artifacts. Other imaging techniques such as spatially-resolved SoS reconstruction using tomographic techniques also rely on a good SoS estimate for initial beamforming. In this work, we exploit spatially-varying geometric disparities in the transmit and receive paths of multiple sequences for estimating a mean medium SoS. We use images from diverging waves beamformed with an assumed SoS, and propose a model fitting method for estimating the SoS offset. We demonstrate the effectiveness of our proposed method for tomographic SoS reconstruction. With corrected beamforming SoS, the reconstruction accuracy on simulated data was improved by 63% and 29%, respectively, for an initial SoS over- and under-estimation of 1.5%. We further demonstrate our proposed method on a breast phantom, indicating substantial improvement in contrast-to-noise ratio for local SoS mapping.
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| 2. |
- Bezek, Can Deniz, et al.
(författare)
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Analytical Estimation of Beamforming Speed-of-Sound Using Transmission Geometry
- 2023
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Ingår i: Ultrasonics. - : Elsevier. - 0041-624X .- 1874-9968. ; 134
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Tidskriftsartikel (refereegranskat)abstract
- Most ultrasound imaging techniques necessitate the fundamental step of converting temporal signals received from transducer elements into a spatial echogenecity map. This beamforming (BF) step requires the knowledge of speed-of-sound (SoS) value in the imaged medium. An incorrect assumption of BF SoS leads to aberration artifacts, not only deteriorating the quality and resolution of conventional brightness mode (B-mode) images, hence limiting their clinical usability, but also impairing other ultrasound modalities such as elastography and spatial SoS reconstructions, which rely on faithfully beamformed images as their input. In this work, we propose an analytical method for estimating BF SoS. We show that pixel-wise relative shifts between frames beamformed with an assumed SoS is a function of geometric disparities of the transmission paths and the error in such SoS assumption. Using this relation, we devise an analytical model, the closed form solution of which yields the difference between the assumed and the true SoS in the medium. Based on this, we correct the BF SoS, which can also be applied iteratively. Both in simulations and experiments, lateral B-mode resolution is shown to be improved by ≈ 25% compared to that with an initial SoS assumption error of 3.3% (50 m/s), while localization artifacts from beamforming are also corrected. After 5 iterations, our method achieves BF SoS errors of under 0.6 m/s in simulations. Residual time-delay errors in beamforming 32 numerical phantoms are shown to reduce down to 0.07 µs, with average improvements of up to 21 folds compared to initial inaccurate assumptions. We additionally show the utility of the proposed method in imaging local SoS maps, where using our correction method reduces reconstruction root-mean-square errors substantially, down to their lower-bound with actual BF SoS.
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| 3. |
- Bezek, Can Deniz, et al.
(författare)
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Global Speed-of-Sound Prediction Using Transmission Geometry
- 2022
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Ingår i: Proceedings of the 2022 IEEE International Ultrasonics Symposium (IUS). - : IEEE. - 9781665466578 - 9781665478137 ; , s. 1-4
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Konferensbidrag (refereegranskat)abstract
- Most ultrasound (US) imaging techniques usespatially-constant speed-of-sound (SoS) values for beamforming.Having a discrepancy between the actual and used SoS valueleads to aberration artifacts, e.g., reducing the image resolution,which may affect diagnostic usability. Accuracy and quality ofdifferent US imaging modalities, such as tomographic reconstruc-tion of local SoS maps, also depend on a good initial beamformingSoS. In this work, we develop an analytical method for estimatingmean SoS in an imaged medium. We show that the relative shiftsbetween beamformed frames depend on the SoS offset and thegeometric disparities in transmission paths. Using this relation,we estimate a correction factor and hence a corrected mean SoSin the medium. We evaluated our proposed method on a set ofnumerical simulations, demonstrating its utility both for globalSoS prediction and for local SoS tomographic reconstruction.For our evaluation dataset, for an initial SoS under- and over-assumption of 5% the medium SoS, our method is able to predictthe actual mean SoS within 0.3% accuracy. For the tomographicreconstruction of local SoS maps, the reconstruction accuracy isimproved on average by 78.5% and 87%, respectively, comparedto an initial SoS under- and over-assumption of 5%.Index Terms—Beamforming, aberration correction.
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| 4. |
- Chen, Boqi, et al.
(författare)
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Generative appearance replay for continual unsupervised domain adaptation
- 2023
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Ingår i: Medical Image Analysis. - : Elsevier. - 1361-8415 .- 1361-8423. ; 89, s. 102924-102924
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on three datasets with different organs and modalities, where it substantially outperforms existing techniques. Our code is available at: https://github.com/histocartography/generative-appearance-replay.Previous article in issue
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| 5. |
- Chintada, Bhaskara Rao, et al.
(författare)
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Spectral Ultrasound Imaging of Speed-of-Sound and Attenuation Using an Acoustic Mirror
- 2022
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Ingår i: Frontiers in Physics. - : Frontiers Media S.A.. - 2296-424X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Speed-of-sound and attenuation of ultrasound waves vary in the tissues. There exist methods in the literature that allow for spatially reconstructing the distribution of group speed-of-sound (SoS) and frequency-dependent ultrasound attenuation (UA) using reflections from an acoustic mirror positioned at a known distance from the transducer. These methods utilize a conventional ultrasound transducer operating in pulse-echo mode and a calibration protocol with measurements in water. In this study, we introduce a novel method for reconstructing local SoS and UA maps as a function of acoustic frequency through Fourier-domain analysis and by fitting linear and power-law dependency models in closed form. Frequency-dependent SoS and UA together characterize the tissue comprehensively in spectral domain within the utilized transducer bandwidth. In simulations, our proposed methods are shown to yield low reconstruction error: 0.01 dB/cm.MHz(y) for attenuation coefficient and 0.05 for the frequency exponent. For tissue-mimicking phantoms and ex-vivo bovine muscle samples, a high reconstruction contrast was achieved. Attenuation exponents in a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.3 and 0.6 on average. Linear dispersion of SoS in a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.3 and 4.0 m/s.MHz on average. These findings were reproducible when the inclusion and substrate materials were exchanged. Bulk loss modulus in the bovine muscle sample was computed to be approximately 4 times the bulk loss modulus in the gelatin-cellulose mixture. Such frequency-dependent characteristics of SoS and UA, and bulk loss modulus may therefore differentiate tissues as potential diagnostic biomarkers.
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| 6. |
- Chintada, Bhaskara R, et al.
(författare)
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Time Of Arrival Delineation In Echo Traces For Reflection Ultrasound Tomography
- 2021
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Konferensbidrag (refereegranskat)abstract
- Ultrasound Computed Tomography (USCT) is an imaging method to map acoustic properties in soft tissues, e.g., for the diagnosis of breast cancer. A group of USCT methods rely on a passive reflector behind the imaged tissue, and they function by delineating such reflector in echo traces, e.g., to infer time-of-flight measurements for reconstructing local speed-of-sound maps. In this work, we study various echo features and delineation methods to robustly identify reflector profiles in echos. We compared and evaluated the methods on a multi-static data set of a realistic breast phantom. Based on our results, a RANSAC based outlier removal followed by an active contours based delineation using a new “edge” feature we propose that detects the first arrival times of echo performs robustly even in complex media; in particular 2.1 times superior to alternative approaches at locations where diffraction effects are prominent.
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| 7. |
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| 8. |
- Gomariz, Alvaro, et al.
(författare)
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Modality attention and sampling enables deep learning with heterogeneous marker combinations in fluorescence microscopy
- 2021
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Ingår i: Nature Machine Intelligence. - : Springer Nature. - 2522-5839. ; 3:9, s. 799-811
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Tidskriftsartikel (refereegranskat)abstract
- Fluorescence microscopy allows for a detailed inspection of cells, cellular networks and anatomical landmarks by staining with a variety of carefully selected markers visualized as colour channels. Quantitative characterization of structures in acquired images often relies on automatic image analysis methods. Despite the success of deep learning methods in other vision applications, their potential for fluorescence image analysis remains underexploited. One reason lies in the considerable workload required to train accurate models, which are normally specific for a given combination of markers and therefore applicable to a very restricted number of experimental settings. We herein propose ‘marker sampling and excite’—a neural network approach with a modality sampling strategy and a novel attention module that together enable (1) flexible training with heterogeneous datasets with combinations of markers and (2) successful utility of learned models on arbitrary subsets of markers prospectively. We show that our single neural network solution performs comparably to an upper bound scenario in which an ensemble of many networks is naively trained for each possible marker combination separately. We also demonstrate the feasibility of this framework in high-throughput biological analysis by revising a recent quantitative characterization of bone-marrow vasculature in three-dimensional confocal microscopy datasets and further confirm the validity of our approach on another substantially different dataset of microvessels in foetal liver tissues. Not only can our work substantially ameliorate the use of deep learning in fluorescence microscopy analysis, but it can also be utilized in other fields with incomplete data acquisitions and missing modalities.
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| 9. |
- Gomariz, Alvaro, et al.
(författare)
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Probabilistic spatial analysis in quantitative microscopy with uncertainty-aware cell detection using deep Bayesian regression
- 2022
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Ingår i: Science Advances. - : American Association for the Advancement of Science (AAAS). - 2375-2548. ; 8:5
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Tidskriftsartikel (refereegranskat)abstract
- The investigation of biological systems with three-dimensional microscopy demands automatic cell identification methods that not only are accurate but also can imply the uncertainty in their predictions. The use of deep learning to regress density maps is a popular successful approach for extracting cell coordinates from local peaks in a postprocessing step, which then, however, hinders any meaningful probabilistic output. We propose a framework that can operate on large microscopy images and output probabilistic predictions (i) by integrating deep Bayesian learning for the regression of uncertainty-aware density maps, where peak detection algorithms generate cell proposals, and (ii) by learning a mapping from prediction proposals to a probabilistic space that accurately represents the chances of a successful prediction. Using these calibrated predictions, we propose a probabilistic spatial analysis with Monte Carlo sampling. We demonstrate this in a bone marrow dataset, where our proposed methods reveal spatial patterns that are otherwise undetectable.
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| 10. |
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