A hardware and software system for MRI applications requiring external device data

Isaieva, Karyna; Fauvel, Marc; Weber, Nicolas; Vuissoz, Pierre‐André; Felblinger, Jacques; Oster, Julien; Odille, Freddy

doi:10.1002/mrm.29280

Cited by 5 publications

(10 citation statements)

References 38 publications

(53 reference statements)

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“…Reducing FP has an important implication when synchronizing diagnostic or therapeutic devices which rely on a systolic trigger, and assume the heart to be in a given phase to perform certain tasks. In CMR, triggering data acquisition by a false detection could lead to severe imaging artifacts impairing proper image interpretation [ 117 , 118 ].…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision

Mehri

Calmon²,

Odille

et al. 2023

Sensors

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Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases. Using the proposed approach, high F1-scores of 99.80% and 99.64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99.88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99.39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.

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Section: Discussionmentioning

confidence: 99%

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision

Mehri

Calmon²,

Odille

et al. 2023

Sensors

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“…Therefore, the sensor's indications reflect the respiratory position. The respiratory belt signals were automatically recorded by the signal analyzer and event controller system 16 …”

Section: Methodsmentioning

confidence: 99%

“…The respiratory belt signals were automatically recorded by the signal analyzer and event controller system. 16 Additionally, a real-time radial FLASH sequence 18 (TR = 2.22 ms, TE = 1.46 ms, in-plane resolution = 1.6 mm, slice thickness = 10 mm, 30 frames/s) was run on one breast slice, for direct visualization of breast motion during free breathing.…”

Section: Motion Measurementsmentioning

confidence: 99%

Feasibility of online non‐rigid motion correction for high‐resolution supine breast MRI

Isaieva,

Meullenet,

Vuissoz

et al. 2023

Magnetic Resonance in Med

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PurposeConventional breast MRI is performed in the prone position with a dedicated coil. This allows high‐resolution images without breast motion, but the patient position is inconsistent with that of other breast imaging modalities or interventions. Supine breast MRI may be an interesting alternative, but respiratory motion becomes an issue. Motion correction methods have typically been performed offline, for instance, the corrected images were not directly accessible from the scanner console. In this work, we seek to show the feasibility of a fast, online, motion‐corrected reconstruction integrated into the clinical workflow.MethodsFully sampled T2‐weighted (T2w) and accelerated T1‐weighted (T1w) breast supine MR images were acquired during free‐breathing and were reconstructed using a non‐rigid motion correction technique (generalized reconstruction by inversion of coupled systems). Online reconstruction was implemented using a dedicated system combining the MR raw data and respiratory signals from an external motion sensor. Reconstruction parameters were optimized on a parallel computing platform, and image quality was assessed by objective metrics and by radiologist scoring.ResultsOnline reconstruction time was 2 to 2.5 min. The metrics and the scores related to the motion artifacts significantly improved for both T2w and T1w sequences. The overall quality of T2w images was approaching that of the prone images, whereas the quality of T1w images remained significantly lower.ConclusionThe proposed online algorithm allows a noticeable reduction of motion artifacts and an improvement of the diagnostic quality for supine breast imaging with a clinically acceptable reconstruction time. These findings serve as a starting point for further development aimed at improving the quality of T1w images.

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“…In contrast, in retrospective applications, synchronization may take the form of a common clock that places time tags on incoming data of all types. The community has been active in providing hardware solutions to synchronization issues, 30 as well as standardized formats and interfaces 31,32 …”

Section: Methodsmentioning

confidence: 99%

“…The community has been active in providing hardware solutions to synchronization issues, 30 as well as standardized formats and interfaces. 31,32 In addition to common clocks, common axes may also be needed. Ideally, a transformation matrix may be found between the scanner coordinate system and the coordinate system that most naturally describes the sensor data.…”

Section: Clocks Reference Systems and Scalingmentioning

confidence: 99%

External Hardware and Sensors, for ImprovedMRI

Madore

Hess

Niekerk

et al. 2022

Magnetic Resonance Imaging

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Complex engineered systems are often equipped with suites of sensors and ancillary devices that monitor their performance and maintenance needs. MRI scanners are no different in this regard. Some of the ancillary devices available to support MRI equipment, the ones of particular interest here, have the distinction of actually participating in the image acquisition process itself. Most commonly, such devices are used to monitor physiological motion or variations in the scanner's imaging fields, allowing the imaging and/or reconstruction process to adapt as imaging conditions change. "Classic" examples include electrocardiography (ECG) leads and respiratory bellows to monitor cardiac and respiratory motion, which have been standard equipment in scan rooms since the early days of MRI. Since then, many additional sensors and devices have been proposed to support MRI acquisitions. The main physical properties that they measure may be primarily "mechanical" (eg acceleration, speed, and torque), "acoustic" (sound and ultrasound), "optical" (light and infrared), or "electromagnetic" in nature. A review of these ancillary devices, as currently available in clinical and research settings, is presented here. In our opinion, these devices are not in competition with each other: as long as they provide useful and unique information, do not interfere with each other and are not prohibitively cumbersome to use, they might find their proper place in future suites of sensors. In time, MRI acquisitions will likely include a plurality of complementary signals. A little like the microbiome that provides genetic diversity to organisms, these devices can provide signal diversity to MRI acquisitions and enrich measurements. Machine-learning (ML) algorithms are well suited at combining diverse input signals toward coherent outputs, and they could make use of all such information toward improved MRI capabilities.

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A hardware and software system for MRI applications requiring external device data

Cited by 5 publications

References 38 publications

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision

Feasibility of online non‐rigid motion correction for high‐resolution supine breast MRI

External Hardware and Sensors, for ImprovedMRI

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