Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography

Majerek, Dariusz; Rymarczyk, Tomasz; Wójcik, Dariusz; Kozłowski, Edward; Rzemieniak, Magdalena; Gudowski, Janusz; Gauda, Konrad

doi:10.3390/en14227549

Cited by 11 publications

(3 citation statements)

References 36 publications

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“…Ultrasound transmission-based tomographic measurements are used to detect localised crystalline suspensions and monitor the reactive crystallisation of the calcium carbonate process. Other works proposed the use of the reflection measurements to detect the localised forming suspensions by incorporating machine learning techniques [52]. In the investigated crystallisation experiments, the initial concentration of calcium chloride is 1.6 g /L; the feed flow rate is 27 ml/min.…”

Section: Crystallisation Imagingmentioning

confidence: 99%

A 4-D Ultrasound Tomography for Industrial Process Reactors Investigation

Koulountzios

Rymarczyk²,

Soleimani

2022

IEEE Trans. Instrum. Meas.

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A volumetric ultrasound tomography (UST) system and method are established for industrial process applications. A twoplane ring-array UST system is developed for 3D imaging of the process under test. Such a 3D system allows capturing axial variations, which is not possible in 2D or 2.5D imaging. A ray-voxel intersection method is used to create the sensitivity matrix needed for the 3D or 4D image reconstruction. Acquiring and processing time series data lead to 4D imaging, generating dynamical volumetric image by using a time correlative total variation (TV) algorithm. The 3D forward model using the ray propagation model offers a computationally efficient tool for modelling the measurement process in UST and is used for image reconstruction. In combination with the advanced 4D TV algorithm, high-quality information is gained from time and space. At first, the 3D imaging methodology was tested and verified using static objects. Secondly, 4D imaging was investigated by using a moving rod in an experimental tank. The system was then implanted to carry out dynamical process monitoring, imaging 4D crystallisation process. Finally, the results are evaluated using quantitative image evaluation in 3D mode and process dynamics in 4D imaging mode.

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Section: Crystallisation Imagingmentioning

confidence: 99%

A 4-D Ultrasound Tomography for Industrial Process Reactors Investigation

Koulountzios

Rymarczyk²,

Soleimani

2022

IEEE Trans. Instrum. Meas.

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“…Nowadays, ultrasonic measurement methods are well developed and give great results, e.g. in medicine or industry, where ultrasonic probes with multiple signal transducers [5][6][7][8][9][10]. These imaging methods require large amounts of data to be processed.…”

Section: Introductionmentioning

confidence: 99%

Ultrasound tomography enhancement by signal feature extraction with modular machine learning method

Baran,

Majerek,

Szyszka

et al. 2024

PLoS ONE

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Robust and reliable diagnostic methods are desired in various types of industries. This article presents a novel approach to object detection in industrial or general ultrasound tomography. The key idea is to analyze the time-dependent ultrasonic signal recorded by three independent transducers of an experimental system. It focuses on finding common or related characteristics of these signals using custom-designed deep neural network models. In principle, models use convolution layers to extract common features of signals, which are passed to dense layers responsible for predicting the number of objects or their locations and sizes. Predicting the number and properties of objects are characterized by a high value of the coefficient of determination R2 = 99.8% and R2 = 98.4%, respectively. The proposed solution can result in a reliable and low-cost method of object detection for various industry sectors.

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“…It is readily available, and common, and is widely used in diagnosis and inspection. Of the numerous computed tomography methods based on physical phenomena such as electrical resistivity tomography (ERT) [4], electrical capacitance tomography (ECT) [3], electrical impedance tomography (EIT) [2], radio wave tomography and localization [5], X-ray tomography [6] or magnetism tomography [7], ultrasound tomography (UST) [8] is a commonly used method in medicine and industry [9,10]. UST is an imaging technique that exploits the properties of acoustic wave transmittance through tissues and materials [11].…”

Section: Introductionmentioning

confidence: 99%

Energy Reduction with Super-Resolution Convolutional Neural Network for Ultrasound Tomography

et al. 2023

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This study addresses the issue of energy optimization by investigating solutions for the reduction of energy consumption in the diagnostics and monitoring of technological processes. The implementation of advanced process control is identified as a key approach for achieving energy savings and improving product quality, process efficiency, and production flexibility. The goal of this research is to develop a cost-effective system with a minimal number of ultrasound sensors, thus reducing the energy consumption of the overall system. To accomplish this, a novel method for obtaining high-resolution reconstruction in transmission ultrasound tomography (t-UST) is proposed. The method involves utilizing a convolutional neural network to take low-resolution measurements as input and output high-resolution sinograms that are used for tomography image reconstruction. This approach allows for the construction of a super-resolution sinogram by utilizing information hidden in the low-resolution measurement. The model is trained on simulation data and validated on real measurement data. The results of this technique demonstrate significant improvement compared to state-of-the-art methods. The study also highlights that UST measurements contain more information than previously thought, and this hidden information can be extracted and utilized with the use of machine learning techniques to further improve image quality and object recognition.

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Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography

Cited by 11 publications

References 36 publications

A 4-D Ultrasound Tomography for Industrial Process Reactors Investigation

A 4-D Ultrasound Tomography for Industrial Process Reactors Investigation

Ultrasound tomography enhancement by signal feature extraction with modular machine learning method

Energy Reduction with Super-Resolution Convolutional Neural Network for Ultrasound Tomography

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