Neural Network Kalman Filtering for 3-D Object Tracking From Linear Array Ultrasound Data

Arjas, Arttu; Alles, Erwin J.; Maneas, Efthymios; Arridge, Simon; Desjardins, Adrien E.; Sillanpää, Mikko; Hauptmann, Andreas

doi:10.1109/tuffc.2022.3162097

Cited by 13 publications

(8 citation statements)

References 48 publications

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“…This might lead to the use of kernel dimensions that vary with the ground truth needle tip position, perhaps mirroring spatial variations in the point spread function of needle tips in reconstructed US tracking images. Second, a Kalman filtering approach could be used to improve needle tip position estimates by incorporating data from multiple ultrasonic tracking frames [15], thereby acknowledging continuity of the needle path through tissue. Third, variations in the sound speed and acoustic attenuation of the imaged medium could be included to the training dataset with a view to improve robustness for different tissue structures.…”

Section: Resultsmentioning

confidence: 99%

Enhancement of instrumented ultrasonic tracking images using deep learning

et al. 2022

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Purpose: Instrumented ultrasonic tracking provides needle localisation during ultrasound-guided minimally invasive percutaneous procedures. Here, a post-processing framework based on a convolutional neural network (CNN) is proposed to improve the spatial resolution of ultrasonic tracking images. Methods: The custom ultrasonic tracking system comprised a needle with an integrated fibre-optic ultrasound (US) transmitter and a clinical US probe for receiving those transmissions and for acquiring B-mode US images. For post-processing of tracking images reconstructed from the received fibre-optic US transmissions, a recently-developed framework based on ResNet architecture, trained with a purely synthetic dataset, was employed. A preliminary evaluation of this framework was performed with data acquired from needle insertions in the heart of a fetal sheep in vivo. The axial and lateral spatial resolution of the tracking images were used as performance metrics of the trained network. Results: Application of the CNN yielded improvements in the spatial resolution of the tracking images. In three needle insertions, in which the tip depth ranged from 23.9 to 38.4 mm, the lateral resolution improved from 2.11 to 1.58 mm, and the axial resolution improved from 1.29 to 0.46 mm. Conclusion: The results provide strong indications of the potential of CNNs to improve the spatial resolution of ultrasonic tracking images and thereby to increase the accuracy of needle tip localisation. These improvements could have broad applicability and impact across multiple clinical fields, which could lead to improvements in procedural efficiency and reductions in risk of complications.

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Section: Resultsmentioning

confidence: 99%

Enhancement of instrumented ultrasonic tracking images using deep learning

et al. 2022

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“…For these reasons, further work is required to improve the out-of-plane tracking provided by the UNT system. It is likely that the relative amplitudes of the signals received by the FOH from each aperture of the US probe encode its elevational position, as has been shown in similar work [ 54 ], and some preliminary work has started looking at using machine learning techniques to decode this, as has been demonstrated in the literature [ 55 , 56 , 57 ]. In [ 54 ], a photoacoustic beacon, rather than FOH, is embedded in the surgical instrument, and transmissions from it are received by the imaging probe and used to determine its 3D position.…”

Section: Discussionmentioning

confidence: 99%

Intraoperative Needle Tip Tracking with an Integrated Fibre-Optic Ultrasound Sensor

Baker

Xochicale

Lin

et al. 2022

Sensors

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Ultrasound is an essential tool for guidance of many minimally-invasive surgical and interventional procedures, where accurate placement of the interventional device is critical to avoid adverse events. Needle insertion procedures for anaesthesia, fetal medicine and tumour biopsy are commonly ultrasound-guided, and misplacement of the needle may lead to complications such as nerve damage, organ injury or pregnancy loss. Clear visibility of the needle tip is therefore critical, but visibility is often precluded by tissue heterogeneities or specular reflections from the needle shaft. This paper presents the in vitro and ex vivo accuracy of a new, real-time, ultrasound needle tip tracking system for guidance of fetal interventions. A fibre-optic, Fabry-Pérot interferometer hydrophone is integrated into an intraoperative needle and used to localise the needle tip within a handheld ultrasound field. While previous, related work has been based on research ultrasound systems with bespoke transmission sequences, the new system—developed under the ISO 13485 Medical Devices quality standard—operates as an adjunct to a commercial ultrasound imaging system and therefore provides the image quality expected in the clinic, superimposing a cross-hair onto the ultrasound image at the needle tip position. Tracking accuracy was determined by translating the needle tip to 356 known positions in the ultrasound field of view in a tank of water, and by comparison to manual labelling of the the position of the needle in B-mode US images during an insertion into an ex vivo phantom. In water, the mean distance between tracked and true positions was 0.7 ± 0.4 mm with a mean repeatability of 0.3 ± 0.2 mm. In the tissue phantom, the mean distance between tracked and labelled positions was 1.1 ± 0.7 mm. Tracking performance was found to be independent of needle angle. The study demonstrates the performance and clinical compatibility of ultrasound needle tracking, an essential step towards a first-in-human study.

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“…Finally, an option to overcome the computational burden could be by extending the Kalman filtering framework in combination with learning based methods. For instance by replacing computational expensive parts with a neural network or intertwining model and data-driven components in the algorithm [40], [41]. Nevertheless, the nature of accumulating information between slices by a well chosen angular sampling is the essential part and needs to be carried over to the learning-based setting.…”

Section: Discussionmentioning

confidence: 99%

Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs

Springer¹,

Glielmo²,

Senchukova³

et al. 2022

Preprint

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In industrial applications it is common to scan objects on a moving conveyor belt. If slice-wise 2D computed tomography (CT) measurements of the moving object are obtained we call it a sequential scanning geometry. In this case, each slice on its own does not carry sufficient information to reconstruct a useful tomographic image. Thus, here we propose the use of a Dimension reduced Kalman Filter to accumulate information between slices and allow for sufficiently accurate reconstructions for further assessment of the object. Additionally, we propose to use an unsupervised clustering approach known as Density Peak Advanced, to perform a segmentation and spot density anomalies in the internal structure of the reconstructed objects. We evaluate the method in a proof of concept study for the application of wood log scanning for the industrial sawing process, where the goal is to spot anomalies within the wood log to allow for optimal sawing patterns. Reconstruction and segmentation quality is evaluated from experimental measurement data for various scenarios of severely undersampled X-measurements. Results show clearly that an improvement of reconstruction quality can be obtained by employing the Dimension reduced Kalman Filter allowing to robustly obtain the segmented logs.

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Neural Network Kalman Filtering for 3-D Object Tracking From Linear Array Ultrasound Data

Cited by 13 publications

References 48 publications

Enhancement of instrumented ultrasonic tracking images using deep learning

Enhancement of instrumented ultrasonic tracking images using deep learning

Intraoperative Needle Tip Tracking with an Integrated Fibre-Optic Ultrasound Sensor

Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs

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