Real‐time automatic image‐based slice tracking of gadolinium‐filled balloon wedge catheter during <scp>MR</scp>‐guided cardiac catheterization: A proof‐of‐concept study

Vidya Shankar, Rohini; Huang, Li; Neji, Radhouene; Kowalik, Grzegorz; Neofytou, Alexander Paul; Mooiweer, Ronald; Moon, Tracy; Mellor, Nina; Razavi, Reza; Pushparajah, Kuberan; Roujol, Sébastien

doi:10.1002/mrm.29822

Cited by 4 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Automatic tracking of the catheter and imaging plane during navigation may potentially facilitate and shorten the procedure. In a recent proof-of-concept study, automatic slice tracking based on real-time image processing and estimation of the catheter tip location was proposed to enable continuous visualization of the catheter tip during navigation ( 21 ). This approach relies on the dynamic acquisition of three contiguous slices, typically using a bSSFP readout for improved signal-to-noise ratio and reduced flow artefacts.…”

Section: Introductionmentioning

confidence: 99%

Automatic image-based tracking of gadolinium-filled balloon wedge catheters for MRI-guided cardiac catheterization using deep learning

Neofytou,

Kowalik,

Vidya Shankar

et al. 2023

Front. Cardiovasc. Med.

Self Cite

View full text Add to dashboard Cite

IntroductionMagnetic Resonance Imaging (MRI) is a promising alternative to standard x-ray fluoroscopy for the guidance of cardiac catheterization procedures as it enables soft tissue visualization, avoids ionizing radiation and provides improved hemodynamic data. MRI-guided cardiac catheterization procedures currently require frequent manual tracking of the imaging plane during navigation to follow the tip of a gadolinium-filled balloon wedge catheter, which unnecessarily prolongs and complicates the procedures. Therefore, real-time automatic image-based detection of the catheter balloon has the potential to improve catheter visualization and navigation through automatic slice tracking.MethodsIn this study, an automatic, parameter-free, deep-learning-based post-processing pipeline was developed for real-time detection of the catheter balloon. A U-Net architecture with a ResNet-34 encoder was trained on semi-artificial images for the segmentation of the catheter balloon. Post-processing steps were implemented to guarantee a unique estimate of the catheter tip coordinates. This approach was evaluated retrospectively in 7 patients (6M and 1F, age = 7 ± 5 year) who underwent an MRI-guided right heart catheterization procedure with all images acquired in an orientation unseen during training.ResultsThe overall accuracy, specificity and sensitivity of the proposed catheter tracking strategy over all 7 patients were 98.4 ± 2.0%, 99.9 ± 0.2% and 95.4 ± 5.5%, respectively. The computation time of the deep-learning-based segmentation step was ∼10 ms/image, indicating its compatibility with real-time constraints.ConclusionDeep-learning-based catheter balloon tracking is feasible, accurate, parameter-free, and compatible with real-time conditions. Online integration of the technique and its evaluation in a larger patient cohort are now warranted to determine its benefit during MRI-guided cardiac catheterization.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic image-based tracking of gadolinium-filled balloon wedge catheters for MRI-guided cardiac catheterization using deep learning

Neofytou,

Kowalik,

Vidya Shankar

et al. 2023

Front. Cardiovasc. Med.

Self Cite

View full text Add to dashboard Cite

show abstract

Inline automatic quality control of 2D phase‐contrast flow MRI for subject‐specific scan time adaptation

Daudé,

Ramasawmy,

Javed

et al. 2024

Magnetic Resonance in Med

View full text Add to dashboard Cite

PurposeTo develop an inline automatic quality control to achieve consistent diagnostic image quality with subject‐specific scan time, and to demonstrate this method for 2D phase‐contrast flow MRI to reach a predetermined SNR.MethodsWe designed a closed‐loop feedback framework between image reconstruction and data acquisition to intermittently check SNR (every 20 s) and automatically stop the acquisition when a target SNR is achieved. A free‐breathing 2D pseudo‐golden‐angle spiral phase‐contrast sequence was modified to listen for image‐quality messages from the reconstructions. Ten healthy volunteers and 1 patient were imaged at 0.55 T. Target SNR was selected based on retrospective analysis of cardiac output error, and performance of the automatic SNR‐driven “stop” was assessed inline.ResultsSNR calculation and automated segmentation was feasible within 20 s with inline deployment. The SNR‐driven acquisition time was 2 min 39 s ± 67 s (aorta) and 3 min ± 80 s (main pulmonary artery) with a min/max acquisition time of 1 min 43 s/4 min 52 s (aorta) and 1 min 43 s/5 min 50 s (main pulmonary artery) across 6 healthy volunteers, while ensuring a diagnostic measurement with relative absolute error in quantitative flow measurement lower than 2.1% (aorta) and 6.3% (main pulmonary artery).ConclusionThe inline quality control enables subject‐specific optimized scan times while ensuring consistent diagnostic image quality. The distribution of automated stopping times across the population revealed the value of a subject‐specific scan time.

show abstract

Fast, automated, real‐time 3D passive balloon catheter tracking during MRI‐guided cardiac catheterization using orthogonal projection imaging and real‐time image‐based catheter detection

Kowalik,

Kerfoot,

Kunze

et al. 2024

Magnetic Resonance in Med

View full text Add to dashboard Cite

PurposeMRI‐guidance of cardiac catheterization is currently performed using one or multiple 2D imaging planes, which may be suboptimal for catheter navigation, especially in patients with complex anatomies. The purpose of the work was to develop a robust real‐time 3D catheter tracking method and 3D visualization strategy for improved MRI‐guidance of cardiac catheterization procedures.MethodsA fast 3D tracking technique was developed using continuous acquisition of two orthogonal 2D‐projection images. Each projection corresponds to a gradient echo stack of slices with only the central k‐space lines being collected for each slice. To enhance catheter contrast, a saturation pulse is added ahead of the projection pair. An offline image processing algorithm was developed to identify the 2D coordinates of the balloon in each projection image and to estimate its corresponding 3D coordinates. Post‐processing includes background signal suppression using an atlas of background 2D‐projection images. 3D visualization of the catheter and anatomy is proposed using three live sagittal, coronal, and axial (MPR) views and 3D rendering. The technique was tested in a subset of a catheterization step in three patients undergoing MRI‐guided cardiac catheterization using a passive balloon catheter.ResultsThe extraction of the catheter balloon 3D coordinates was successful in all patients and for the majority of time‐points (accuracy >96%). This tracking method enabled a novel 3D visualization strategy for passive balloon catheter, providing enhanced anatomical context during catheter navigation.ConclusionThe proposed tracking strategy shows promise for robust tracking of passive balloon catheter and may enable enhanced visualization during MRI‐guided cardiac catheterization.

show abstract

Real‐time automatic image‐based slice tracking of gadolinium‐filled balloon wedge catheter during MR‐guided cardiac catheterization: A proof‐of‐concept study

Cited by 4 publications

References 39 publications

Automatic image-based tracking of gadolinium-filled balloon wedge catheters for MRI-guided cardiac catheterization using deep learning

Automatic image-based tracking of gadolinium-filled balloon wedge catheters for MRI-guided cardiac catheterization using deep learning

Inline automatic quality control of 2D phase‐contrast flow MRI for subject‐specific scan time adaptation

Fast, automated, real‐time 3D passive balloon catheter tracking during MRI‐guided cardiac catheterization using orthogonal projection imaging and real‐time image‐based catheter detection

Contact Info

Product

Resources

About