Analysis of the Influence of Different Settings of Scan Sequence Parameters on Vibration and Noise Generated in the Open-Air MRI Scanning Area

Přibil, Jiří; Přibilová, Anna; Frollo, Ivan

doi:10.3390/s19194198

Cited by 6 publications

(10 citation statements)

References 25 publications

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“…The possible solution to this issue may be cooperation with some medical center (in Bratislava, Brno, Vienna, and so on) having a certificate for the work with patients. Both of the MRI devices working with low magnetic field located at our institute can be used for non-clinical and non-medical research only [ 18 , 19 , 20 ].…”

Section: Discussion Of Obtained Resultsmentioning

confidence: 99%

“…This stress causes tensions in vocal cords and negative modification of suprasegmental and spectral features of the speech signal, so it can bring about errors and inaccuracy in the calculation of 3D models of the human vocal tract. In correspondence with previous research [ 18 , 19 , 20 ], the main long-term aim of our investigations is to find a methodology and recommendations for the minimization of negative physiological and psychological stress as the secondary undesirable effect of scanning inside the low-field MRI device. The final task is to find proper settings of MR scan parameters (such as TE and TR times, type of sequence, number and thickness of slices, and so on) that minimize generated vibration and acoustic noise, decrease the stress factor induced on an examined person, and eventually increase the accuracy of the created 3D vocal tract models.…”

Section: Introductionmentioning

confidence: 94%

“… Preliminary experiments consisting of a situation when the tested person sitting on a chair by a table hears the MRI noise through headphones in the Load phases. This noise was recorded from inside the MRI scanner during execution of the MR scan sequence having the most vibration energy (3D SSF with TE = 10 ms, TR = 40 ms, sagittal orientation, 3D-phases = 24, N ACC = 4 [ 19 , 20 ]). In the silent, Relax phases, the normal room ambient noise is present without wearing headphones.…”

Section: Description Of Performed Experimentsmentioning

confidence: 99%

“…Practical measurement experiments were realized on the MRI equipment E-scan Opera by Esaote S.p.A. [ 23 ] located at the Institute of Measurement Science in Bratislava. This open-air MRI device works with a stationary magnetic field of 0.178 T. For backward compatibility with our previous experiments [ 18 , 19 , 20 ], the high-resolution SE-HF scan sequence with TE = 18 ms, TR = 400 ms, and sagittal orientation was applied.…”

Section: Description Of Performed Experimentsmentioning

confidence: 99%

“…The Easy Pulse V1.1 module together with the power bank supply and the audio mixer were located outside the shielding cage [ 23 ] of the MRI equipment in the control room near the operating console. The position of the tested person was chosen in such a way that the head was placed in the RF scan coil between the upper and lower gradient coils of the MRI device [ 18 , 19 , 20 , 23 ] to maximize the noise and vibration effect on the examined person.…”

Section: Description Of Performed Experimentsmentioning

confidence: 99%

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First-Step PPG Signal Analysis for Evaluation of Stress Induced during Scanning in the Open-Air MRI Device

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Frollo

2020

Sensors

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The paper describes first-step experiments with parallel measurement of cardiovascular parameters using a photoplethysmographic optical sensor and standard portable blood pressure monitors in different situations of body relaxation and stimulation. Changes in the human cardiovascular system are mainly manifested by differences in the Oliva–Roztocil index, the instantaneous heart rate, and variations in blood pressure. In the auxiliary experiments, different physiological and psychological stimuli were applied to test whether relaxation and activation phases produce different measured parameters suitable for further statistical analysis and processing. The principal investigation is aimed at analysis of vibration and acoustic noise impact on a physiological and psychological state of a person lying inside the low-field open-air magnetic resonance imager (MRI). The obtained results will be used to analyze, quantify, and suppress a possible stress factor that has an impact on the speech signal recorded during scanning in the MRI device in the research aimed at 3D modeling of the human vocal tract.

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Section: Discussion Of Obtained Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Section: Description Of Performed Experimentsmentioning

confidence: 99%

Section: Description Of Performed Experimentsmentioning

confidence: 99%

Section: Description Of Performed Experimentsmentioning

confidence: 99%

See 3 more Smart Citations

First-Step PPG Signal Analysis for Evaluation of Stress Induced during Scanning in the Open-Air MRI Device

Přibil

Přibilová

Frollo

2020

Sensors

Self Cite

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MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

Sushentsev

Rundo

Blyuss

et al. 2021

Sci Rep

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Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years, highlighting the unmet clinical need for robust baseline risk-stratification tools that enable timely and accurate prediction of tumour progression. In this proof-of-concept study, we sought to investigate the added value of MRI-derived radiomic features to standard-of-care clinical parameters for improving baseline prediction of PCa progression in AS patients. Tumour T2-weighted imaging (T2WI) and apparent diffusion coefficient radiomic features were extracted, with rigorous calibration and pre-processing methods applied to select the most robust features for predictive modelling. Following leave-one-out cross-validation, the addition of T2WI-derived radiomic features to clinical variables alone improved the area under the ROC curve for predicting progression from 0.61 (95% confidence interval [CI] 0.481–0.743) to 0.75 (95% CI 0.64–0.86). These exploratory findings demonstrate the potential benefit of MRI-derived radiomics to add incremental benefit to clinical data only models in the baseline prediction of PCa progression on AS, paving the way for future multicentre studies validating the proposed model and evaluating its impact on clinical outcomes.

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Image biomarkers and explainable AI: handcrafted features versus deep learned features

Rundo,

Militello

2024

Eur Radiol Exp

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Feature extraction and selection from medical data are the basis of radiomics and image biomarker discovery for various architectures, including convolutional neural networks (CNNs). We herein describe the typical radiomics steps and the components of a CNN for both deep feature extraction and end-to-end approaches. We discuss the curse of dimensionality, along with dimensionality reduction techniques. Despite the outstanding performance of deep learning (DL) approaches, the use of handcrafted features instead of deep learned features needs to be considered for each specific study. Dataset size is a key factor: large-scale datasets with low sample diversity could lead to overfitting; limited sample sizes can provide unstable models. The dataset must be representative of all the “facets” of the clinical phenomenon/disease investigated. The access to high-performance computational resources from graphics processing units is another key factor, especially for the training phase of deep architectures. The advantages of multi-institutional federated/collaborative learning are described. When large language models are used, high stability is needed to avoid catastrophic forgetting in complex domain-specific tasks. We highlight that non-DL approaches provide model explainability superior to that provided by DL approaches. To implement explainability, the need for explainable AI arises, also through post hoc mechanisms. Relevance statement This work aims to provide the key concepts for processing the imaging features to extract reliable and robust image biomarkers. Key Points The key concepts for processing the imaging features to extract reliable and robust image biomarkers are provided. The main differences between radiomics and representation learning approaches are highlighted. The advantages and disadvantages of handcrafted versus learned features are given without losing sight of the clinical purpose of artificial intelligence models. Graphical Abstract

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Analysis of the Influence of Different Settings of Scan Sequence Parameters on Vibration and Noise Generated in the Open-Air MRI Scanning Area

Cited by 6 publications

References 25 publications

First-Step PPG Signal Analysis for Evaluation of Stress Induced during Scanning in the Open-Air MRI Device

First-Step PPG Signal Analysis for Evaluation of Stress Induced during Scanning in the Open-Air MRI Device

MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

Image biomarkers and explainable AI: handcrafted features versus deep learned features

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