Motion Correction in Magnetic Resonance Imaging Using the Signal of Free-Induction-Decay

Magnetic resonance imaging (MRI) is highly susceptible to subject's motion and can significantly degrade image quality. In brain MRI exams, involuntary head movements can affect the sampled k-space data. Such unintended alterations may result in visible image artifacts such as blurring, ghosting and others, and therefore potentially disqualify the image from diagnostic purposes. Methods to characterize motion in order to mitigate its impact on image quality exist and range from MR sequence based techniques to scanner independent tracking systems. Although, many motion detection and correction strategies have been proposed in the past, a universal solution to the problem does not exist yet. The work of this thesis was focused on the exploitation of the motion information from a multi-channel Free Induction Decay Navigator (FID) to develop and to optimize motion detection and correction methods in structural brain MRI. After a short introduction to the motion problem in MRI the fundamental methodology behind FID based motion detection is presented and used in this thesis. Considerable work has already been done in the field of motion correction for MRI that is summarized by reviewing the most recent literature, which allowed to reveal some pitfalls in the present approaches and to demonstrate the motivation behind an FID-based method for motion correction in MRI. The first study was conducted to demonstrate that substantial motion information is contained in the multi-channel FID signal, whereby the FID signal is correlated with motion parameters that were obtained from a highly accurate optical tracking system. This work was able to confirm the theoretical foundations from the Biot-Savart law, however, also revealed that a pure FID-based method is not sufficient to exactly calculate the underlying motion trajectory. It is speculated that scanner and subject related information might lead to a closed form solution, yet it was not possible to derive one due to a high dimensionality of the motion problem. Therefore, two alternative approaches were developed to utilize the FID signal for motion detection and correction in MRI. First, a prospective motion correction strategy for an MP-RAGE sequence is demonstrated, whereby the FID signal is used to trigger a prospective motion correction mechanism. The second alternative approach describes how the FID signal can be used to evaluate the quality of an already acquired image and how the FID signal can be used as an optimizer for an autofocusing based retrospective motion correction technique

Semantic Segmentation in Underwater Ship Inspections: Benchmark and Dataset

In this article, we present the first large-scale data set for underwater ship lifecycle inspection, analysis and condition information (LIACI). It contains 1893 images with pixel annotations for ten object categories: defects, corrosion, paint peel, marine growth, sea chest gratings, overboard valves, propeller, anodes, bilge keel and ship hull. The images have been collected during underwater ship inspections and annotated by human domain experts. We also present a benchmark evaluation of state-of-the-art semantic segmentation approaches based on standard performance metrics. Consequently, we propose to use U-Net with a MobileNetV2 backbone for the segmentation task due to its balanced tradeoff between performance and computational efficiency, which is essential if used for real-time evaluation. Also, we demonstrate its benefits for in-water inspections by providing quantitative evaluations of the inspection findings. With a variety of use cases, the proposed segmentation pipeline and the LIACI data set create new promising opportunities for future research in underwater ship inspections.publishedVersio

Semantic Segmentation in Underwater Ship Inspections: Benchmark and Dataset

In this article, we present the first large-scale data set for underwater ship lifecycle inspection, analysis and condition information (LIACI). It contains 1893 images with pixel annotations for ten object categories: defects, corrosion, paint peel, marine growth, sea chest gratings, overboard valves, propeller, anodes, bilge keel and ship hull. The images have been collected during underwater ship inspections and annotated by human domain experts. We also present a benchmark evaluation of state-of-the-art semantic segmentation approaches based on standard performance metrics. Consequently, we propose to use U-Net with a MobileNetV2 backbone for the segmentation task due to its balanced tradeoff between performance and computational efficiency, which is essential if used for real-time evaluation. Also, we demonstrate its benefits for in-water inspections by providing quantitative evaluations of the inspection findings. With a variety of use cases, the proposed segmentation pipeline and the LIACI data set create new promising opportunities for future research in underwater ship inspections

Multi-label Video Classification for Underwater Ship Inspection

Today ship hull inspection including the examination of the external coating, detection of defects, and other types of external degradation such as corrosion and marine growth is conducted underwater by means of Remotely Operated Vehicles (ROVs). The inspection process consists of a manual video analysis which is a time-consuming and labor-intensive process. To address this, we propose an automatic video analysis system using deep learning and computer vision to improve upon existing methods that only consider spatial information on individual frames in underwater ship hull video inspection. By exploring the benefits of adding temporal information and analyzing frame-based classifiers, we propose a multi-label video classification model that exploits the self-attention mechanism of transformers to capture spatiotemporal attention in consecutive video frames. Our proposed method has demonstrated promising results and can serve as a benchmark for future research and development in underwater video inspection applications.Comment: Accepted to be presented at OCEANS 2023 Limerick conference and will be published by IEE

Vibration Sensors for Detecting Critical Events: A Case Study in Ferrosilicon Production

The mining and metal processing industries are undergoing a transformation through digitization, with sensors and data analysis playing a crucial role in modernization and increased efficiency. Vibration sensors are particularly important in monitoring production infrastructure in metal processing plants. This paper presents the installation of vibration sensors in an actual industrial environment and the results of spectral vibration data analysis. The study demonstrates that vibration sensors can be installed in challenging environments such as metal processing plants and that analyzing vibration patterns can provide valuable insights into predicting machine failures and different machine states. By utilizing dimensionality reduction and dominant frequency observation, we analyzed vibration data and identified patterns that are indicative of potential machine states and critical events that reduce production throughput. This information can be used to improve maintenance, minimize downtime, and ultimately enhance the production process’s overall efficiency. This study highlights the importance of digitization and data analysis in the mining and metal processing industries, particularly the capability not only to predict critical events before they impact production throughput and take action accordingly but also to identify machine states for legacy equipment and be part of retrofitting strategies.publishedVersio

Prospective head motion correction using FID-guided on-demand image navigators

PURPOSE: We suggest a motion correction concept that employs free-induction-decay (FID) navigator signals to continuously monitor motion and to guide the acquisition of image navigators for prospective motion correction following motion detection. METHODS: Motion causes out-of-range signal changes in FID time series that, and in this approach, initiate the acquisition of an image navigator. Co-registration of the image navigator to a reference provides rigid-body-motion parameters to facilitate prospective motion correction. Both FID and image navigator are integrated into a prototype magnetization-prepared rapid gradient-echo (MPRAGE) sequence. The performance of the method is investigated using image quality metrics and the consistency of brain volume measurements. RESULTS: Ten healthy subjects were scanned (a) while performing head movements (nodding, shaking, and moving in z-direction) and (b) to assess the co-registration performance. Mean absolute errors of 0.27 +/- 0.38 mm and 0.19 +/- 0.24 degrees for translation and rotation parameters were measured. Image quality was qualitatively improved after correction. Significant improvements were observed in automated image quality measures and for most quantitative brain volume computations after correction. CONCLUSION: The presented method provides high sensitivity to detect head motion while minimizing the time invested in acquiring navigator images. Limits of this implementation arise from temporal resolution to detect motion, false-positive alarms, and registration accuracy

Head motion measurement and correction using FID navigators

Purpose: To develop a novel framework for rapid, intrinsic head motion measurement in MRI using FID navigators (FIDnavs) from a multichannel head coil array.Methods: FIDnavs encode substantial rigid-body motion information; however, current implementations require patient-specific training with external tracking data to extract quantitative positional changes. In this work, a forward model of FIDnav signals was calibrated using simulated movement of a reference image within a model of the spatial coil sensitivities. A FIDnav module was inserted into a nonselective 3D FLASH sequence, and rigid-body motion parameters were retrospectively estimated every readout time using nonlinear optimization to solve the inverse problem posed by the measured FIDnavs. This approach was tested in simulated data and in 7 volunteers, scanned at 3T with a 32-channel head coil array, performing a series of directed motion paradigms.Results: FIDnav motion estimates achieved mean absolute errors of 0.34 +/- 0.49 mm and 0.52 +/- 0.61 degrees across all subjects and scans, relative to ground-truth motion measurements provided by an electromagnetic tracking system. Retrospective correction with FIDnav motion estimates resulted in substantial improvements in quantitative image quality metrics across all scans with intentional head motion.Conclusions: Quantitative rigid-body motion information can be effectively estimated using the proposed FIDnav-based approach, which represents a practical method for retrospective motion compensation in less cooperative patient populations

Data quality issues for vibration sensors: a case study in ferrosilicon production

Digitisation in the mining and metal processing industries plays a key role in their modernisation. Production processes are more and more supported by a variety of sensors that produce large amounts of data that meant to provide insights into the performance of production infrastructures. In the metal processing industry vibration sensors are essential in the monitoring of the production infrastructure. In this position paper we present the installation of vibration sensors in a real industrial environment and discuss the data quality issues we encountered while using such sensors.publishedVersio