The effect of intercarpal arthrodeses on wrist kinematics during radial and ulnar deviation: a cadaveric study using four-dimensional CT.

In this cadaveric study, we report quantitative dynamic four-dimensional CT evaluation of the effect on wrist kinematics of three intercarpal arthrodeses during radial and ulnar deviation. In five wrists, we successively performed scaphocapitate, four-corner and two-corner fusions. Four-dimensional CT examinations were performed prior to dissection and after each arthrodesis. The lunocapitate gap, posterior lunocapitate angle, radiolunate radial gap, radiolunate ulnar gap and radiolunate angle were assessed. After scaphocapitate arthrodesis, in radial deviation, we noted midcarpal diastasis and dorsal displacement of the capitate. In ulnar deviation, there was correction of that incongruence. After four-corner and two-corner fusions, in radial deviation, we noted radial radiolunate impingement and ulnar radiolunate incongruence. In ulnar deviation, after two-corner fusion, ulnar radiolunate impingement and radial radiolunate incongruence were present contrary to four-corner fusion. Our findings confirm that the constant radiocarpal and midcarpal congruence during radioulnar deviation in normal wrists is no longer possible with intercarpal kinematic modifications after these arthrodeses.

Monitoring Methodology for an AI Tool for Breast Cancer Screening Deployed in Clinical Centers.

We propose a methodology for monitoring an artificial intelligence (AI) tool for breast cancer screening when deployed in clinical centers. An AI trained to detect suspicious regions of interest in the four views of a mammogram and to characterize their level of suspicion with a score ranging from one (low suspicion) to ten (high suspicion of malignancy) was deployed in four radiological centers across the US. Results were collected between April 2021 and December 2022, resulting in a dataset of 36,581 AI records. To assess the behavior of the AI, its score distribution in each center was compared to a reference distribution obtained in silico using the Pearson correlation coefficient (PCC) between each center AI score distribution and the reference. The estimated PCCs were 0.998 [min: 0.993, max: 0.999] for center US-1, 0.975 [min: 0.923, max: 0.986] for US-2, 0.995 [min: 0.972, max: 0.998] for US-3 and 0.994 [min: 0.962, max: 0.982] for US-4. These values show that the AI behaved as expected. Low PCC values could be used to trigger an alert, which would facilitate the detection of software malfunctions. This methodology can help create new indicators to improve monitoring of software deployed in hospitals.

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

PURPOSE: Numerous MRI applications require data from external devices. Such devices are often independent of the MRI system, so synchronizing these data with the MRI data is often tedious and limited to offline use. In this work, a hardware and software system is proposed for acquiring data from external devices during MR imaging, for use online (in real-time) or offline. METHODS: The hardware includes a set of external devices - electrocardiography (ECG) devices, respiration sensors, microphone, electronics of the MR system etc. - using various channels for data transmission (analog, digital, optical fibers), all connected to a server through a universal serial bus (USB) hub. The software is based on a flexible client-server architecture, allowing real-time processing pipelines to be configured and executed. Communication protocols and data formats are proposed, in particular for transferring the external device data to an open-source reconstruction software (Gadgetron), for online image reconstruction using external physiological data. The system performance is evaluated in terms of accuracy of the recorded signals and delays involved in the real-time processing tasks. Its flexibility is shown with various applications. RESULTS: The real-time system had low delays and jitters (on the order of 1 ms). Example MRI applications using external devices included: prospectively gated cardiac cine imaging, multi-modal acquisition of the vocal tract (image, sound, and respiration) and online image reconstruction with nonrigid motion correction. CONCLUSION: The performance of the system and its versatile architecture make it suitable for a wide range of MRI applications requiring online or offline use of external device data.

Evaluation of occupational exposure to static magnetic field in MRI sites based on body pose estimation and SMF analytical computation.

This paper tackles the problem of estimating exposure to static magnetic field (SMF) in magnetic resonance imaging (MRI) sites using a non-invasive approach. The proposed approach relies on a vision-based system to detect people's body parts and on a mathematical model to compute SMF exposure. A multi-view camera system was used to capture the MRI room, and a vision-based system was applied to detect body parts. The detected localization was then fed into a mathematical model to compute SMF exposure. In this study, we focused on exposure at the neck due to two main reasons. First, according to regulations, the limit of exposure at head and trunk for MR workers is higher than that for the general public. Second, it was easier to attach a dosimeter at the neck to perform measurements, which allowed a quantitative evaluation of our approach. This approach was applied to two scenarios simulating the daily movements of medical workers for which dosimeter measurements were also recorded. The results indicated that the proposed approach predicted occupational SMF exposure with reasonable accuracy compared with the dosimeter measurements. The proposed approach is a simple safe working procedure to estimate the exposure of MR workers at different parts of the body without wearing any marker detection. It can be applied to reduce occupational SMF exposure, without changes in workers' performances. For that reason, our non-invasive proposed method can be used as a simple safety tool to estimate occupational SMF exposure in MR sites. Bioelectromagnetics. 39:503-515, 2018.© 2018 Wiley Periodicals, Inc.

A novel MR-compatible sensor to assess active medical device safety: stimulation monitoring, rectified radio frequency pulses, and gradient-induced voltage measurements.

PURPOSE: To evaluate the function of an active implantable medical device (AIMD) during magnetic resonance imaging (MRI) scans. The induced voltages caused by the switching of magnetic field gradients and rectified radio frequency (RF) pulse were measured, along with the AIMD stimulations. MATERIALS AND METHODS: An MRI-compatible voltage probe with a bandwidth of 0-40 kHz was designed. Measurements were carried out both on the bench with an overvoltage protection circuit commonly used for AIMD and with a pacemaker during MRI scans on a 1.5 T (64 MHz) MR scanner. RESULTS: The sensor exhibits a measurement range of ± 15 V with an amplitude resolution of 7 mV and a temporal resolution of 10 µs. Rectification was measured on the bench with the overvoltage protection circuit. Linear proportionality was confirmed between the induced voltage and the magnetic field gradient slew rate. The pacemaker pacing was recorded successfully during MRI scans. CONCLUSION: The characteristics of this low-frequency voltage probe allow its use with extreme RF transmission power and magnetic field gradient positioning for MR safety test of AIMD during MRI scans.

Calibration and non-orthogonality correction of three-axis Hall sensors for the monitoring of MRI workers' exposure to static magnetic fields.

A Magnetic Resonance Imaging (MRI) scanner uses three different electromagnetic fields (EMF) to produce body images: a static permanent magnetic field (MF), several pulsed magnetic gradients, and a radiofrequency pulse. As a result, any occupation that includes an MRI exposes workers to a strong MF. The World Health Organization has now given the monitoring of occupational EMF exposure a high priority. One design for a low-cost, compact MF exposure monitor (« MR exposimeter ¼) uses a set of three orthogonally assembled Hall sensors. However, at such a strong EMF exposure intensity, the non-linearity and non-orthogonality (misalignment between the three Hall sensors) have an impact on the accuracy of EMF measurement. Therefore, a sensor characterization was performed in order to link Hall-effect output voltage to MF intensity. The sensor was then calibrated using an orthogonalization matrix and an offset vector. For each sensor configuration, the matrix and vector parameters were optimized with a calibration set generated by the movement of a three-axis sensor inside homogeneous MF areas. Once calibrated, the sensor was tested at different MF intensities and returned accuracy improvements. This calibration procedure was tested on synthetic data and performed on experimental data. The calibration parameters can be easily reused by the user, and their stability could be used as a quality control sensor. Finally, real-time monitoring test for static MF exposure was completed and validated on an MRI worker during a typical working day. Bioelectromagnetics. 39:108-119, 2018. © 2018 Wiley Periodicals, Inc.

MRI 'EXPOSIMETRY': HOW TO ANALYZE, COMPARE AND REPRESENT WORKER EXPOSURE TO STATIC MAGNETIC FIELD?

Worker exposure to electromagnetic fields (EMF) is a growing concern of international commissions. A European directive from 2013 (2013/35/EU) recommend to estimate or measure EMF exposure of all exposed workers. Magnetic resonance imaging (MRI) workers are specially concerned by this point because they work all day long in the vicinity of a very strong magnet (generally 1.5 or 3 T), which cannot be turned off. Setting up a magnetic field monitoring device on these workers would therefore be a good way to ensure their security. European directive threshold adequacy could then be verified. But this verification does not ensure a complete analysis of the worker exposure. Such an analysis based on quality control charts and exposure time's metrics has been described in this paper. The proposed magnetic field exposure analysis has additionally been tested on a long-term exposure follow-up of 18 MRI workers during 2 months.

Design and Validation of a Novel MR-Compatible Sensor for Respiratory Motion Modeling and Correction.

GOAL: A novel magnetic resonance (MR) compatible accelerometer for respiratory motion sensing (MARMOT) is developed as a surrogate of the vendors' pneumatic belts. We aim to model and correct respiratory motion for free-breathing thoracic-abdominal MR imaging and to simplify patient installation. METHODS: MR compatibility of MARMOT sensors was assessed in phantoms and its motion modeling/correction efficacy was demonstrated on 21 subjects at 3 T. Respiration was modeled and predicted from MARMOT sensors and pneumatic belts, based on real-time images and a regression method. The sensor accuracy was validated by comparing motion errors in the liver/kidney. Sensor data were also exploited as inputs for motion-compensated reconstruction of free-breathing cardiac cine MR images. Multiple and single sensor placement strategies were compared. RESULTS: The new sensor is compatible with the MR environment. The average motion modeling and prediction errors with MARMOT sensors and with pneumatic belts were comparable (liver and kidney) and were below 2 mm with all tested configurations (belts, multiple/single MARMOT sensor). Motion corrected cardiac cine images were of improved image quality, as assessed by an entropy metric (p  <  10-6), with all tested configurations. Expert readings revealed multiple MARMOT sensors were the best (p  <  0.03) and the single MARMOT sensor was similar to the belts (nonsignificant in two of the three readers). CONCLUSION: The proposed sensor can model and predict respiratory motion with sufficient accuracy to allow free-breathing MR imaging strategy. SIGNIFICANCE: It provides an alternative sensor solution for the respiratory motion problem during MR imaging and may improve the convenience of patient setup.

Observer performance for adaptive, image-based denoising and filtered back projection compared to scanner-based iterative reconstruction for lower dose CT enterography.

PURPOSE: The purpose of this study was to compare observer performance for detection of intestinal inflammation for low-dose CT enterography (LD-CTE) using scanner-based iterative reconstruction (IR) vs. vendor-independent, adaptive image-based noise reduction (ANLM) or filtered back projection (FBP). METHODS: Sixty-two LD-CTE exams were performed. LD-CTE images were reconstructed using IR, ANLM, and FBP. Three readers, blinded to image type, marked intestinal inflammation directly on patient images using a specialized workstation over three sessions, interpreting one image type/patient/session. Reference standard was created by a gastroenterologist and radiologist, who reviewed all available data including dismissal Gastroenterology records, and who marked all inflamed bowel segments on the same workstation. Reader and reference localizations were then compared. Non-inferiority was tested using Jackknife free-response ROC (JAFROC) figures of merit (FOM) for ANLM and FBP compared to IR. Patient-level analyses for the presence or absence of inflammation were also conducted. RESULTS: There were 46 inflamed bowel segments in 24/62 patients (CTDIvol interquartile range 6.9-10.1 mGy). JAFROC FOM for ANLM and FBP were 0.84 (95% CI 0.75-0.92) and 0.84 (95% CI 0.75-0.92), and were statistically non-inferior to IR (FOM 0.84; 95% CI 0.76-0.93). Patient-level pooled confidence intervals for sensitivity widely overlapped, as did specificities. Image quality was rated as better with IR and AMLM compared to FBP (p < 0.0001), with no difference in reading times (p = 0.89). CONCLUSIONS: Vendor-independent adaptive image-based noise reduction and FBP provided observer performance that was non-inferior to scanner-based IR methods. Adaptive image-based noise reduction maintained or improved upon image quality ratings compared to FBP when performing CTE at lower dose levels.