pyM2aia: Python interface for mass spectrometry imaging with focus on deep learning.

SUMMARY: Python is the most commonly used language for deep learning (DL). Existing Python packages for mass spectrometry imaging (MSI) data are not optimized for DL tasks. We, therefore, introduce pyM2aia, a Python package for MSI data analysis with a focus on memory-efficient handling, processing and convenient data-access for DL applications. pyM2aia provides interfaces to its parent application M2aia, which offers interactive capabilities for exploring and annotating MSI data in imzML format. pyM2aia utilizes the image input and output routines, data formats, and processing functions of M2aia, ensures data interchangeability, and enables the writing of readable and easy-to-maintain DL pipelines by providing batch generators for typical MSI data access strategies. We showcase the package in several examples, including imzML metadata parsing, signal processing, ion-image generation, and, in particular, DL model training and inference for spectrum-wise approaches, ion-image-based approaches, and approaches that use spectral and spatial information simultaneously. AVAILABILITY AND IMPLEMENTATION: Python package, code and examples are available at (https://m2aia.github.io/m2aia).

Computational Analysis of Alzheimer Amyloid Plaque Composition in 2D- and Elastically Reconstructed 3D-MALDI MS Images.

MALDI mass spectrometry imaging (MSI) enables label-free, spatially resolved analysis of a wide range of analytes in tissue sections. Quantitative analysis of MSI datasets is typically performed on single pixels or manually assigned regions of interest (ROIs). However, many sparse, small objects such as Alzheimer's disease (AD) brain deposits of amyloid peptides called plaques are neither single pixels nor ROIs. Here, we propose a new approach to facilitate the comparative computational evaluation of amyloid plaque-like objects by MSI: a fast PLAQUE PICKER tool that enables a statistical evaluation of heterogeneous amyloid peptide composition. Comparing two AD mouse models, APP NL-G-F and APP PS1, we identified distinct heterogeneous plaque populations in the NL-G-F model but only one class of plaques in the PS1 model. We propose quantitative metrics for the comparison of technical and biological MSI replicates. Furthermore, we reconstructed a high-accuracy 3D-model of amyloid plaques in a fully automated fashion, employing rigid and elastic MSI image registration using structured and plaque-unrelated reference ion images. Statistical single-plaque analysis in reconstructed 3D-MSI objects revealed the Aß1-42Arc peptide to be located either in the core of larger plaques or in small plaques without colocalization of other Aß isoforms. In 3D, a substantially larger number of small plaques were observed than that indicated by the 2D-MSI data, suggesting that quantitative analysis of molecularly diverse sparsely-distributed features may benefit from 3D-reconstruction. Data are available via ProteomeXchange with identifier PXD020824.

Surgical phase and instrument recognition: how to identify appropriate dataset splits.

PURPOSE: Machine learning approaches can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes. Surgical workflow and instrument recognition are two tasks that are complicated in this manner, because of heavy data imbalances resulting from different length of phases and their potential erratic occurrences. Furthermore, sub-properties like instrument (co-)occurrence are usually not particularly considered when defining the split. METHODS: We present a publicly available data visualization tool that enables interactive exploration of dataset partitions for surgical phase and instrument recognition. The application focuses on the visualization of the occurrence of phases, phase transitions, instruments, and instrument combinations across sets. Particularly, it facilitates assessment of dataset splits, especially regarding identification of sub-optimal dataset splits. RESULTS: We performed analysis of the datasets Cholec80, CATARACTS, CaDIS, M2CAI-workflow, and M2CAI-tool using the proposed application. We were able to uncover phase transitions, individual instruments, and combinations of surgical instruments that were not represented in one of the sets. Addressing these issues, we identify possible improvements in the splits using our tool. A user study with ten participants demonstrated that the participants were able to successfully solve a selection of data exploration tasks. CONCLUSION: In highly unbalanced class distributions, special care should be taken with respect to the selection of an appropriate dataset split because it can greatly influence the assessments of machine learning approaches. Our interactive tool allows for determination of better splits to improve current practices in the field. The live application is available at https://cardio-ai.github.io/endovis-ml/ .

Comparative evaluation of three commercially available markerless depth sensors for close-range use in surgical simulation.

PURPOSE: Minimally invasive surgeries have restricted surgical ports, demanding a high skill level from the surgeon. Surgical simulation potentially reduces this steep learning curve and additionally provides quantitative feedback. Markerless depth sensors show great promise for quantification, but most such sensors are not designed for accurate reconstruction of complex anatomical forms in close-range. METHODS: This work compares three commercially available depth sensors, namely the Intel D405, D415, and the Stereolabs Zed-Mini in the range of 12-20 cm, for use in surgical simulation. Three environments are designed that closely mimic surgical simulation, comprising planar surfaces, rigid objects, and mitral valve models of silicone and realistic porcine tissue. The cameras are evaluated on Z-accuracy, temporal noise, fill rate, checker distance, point cloud comparisons, and visual inspection of surgical scenes, across several camera settings. RESULTS: The Intel cameras show sub-mm accuracy in most static environments. The D415 fails in reconstructing valve models, while the Zed-Mini provides lesser temporal noise and higher fill rate. The D405 could reconstruct anatomical structures like the mitral valve leaflet and a ring prosthesis, but performs poorly for reflective surfaces like surgical tools and thin structures like sutures. CONCLUSION: If a high temporal resolution is needed and lower spatial resolution is acceptable, the Zed-Mini is the best choice, whereas the Intel D405 is the most suited for close-range applications. The D405 shows potential for applications like deformable registration of surfaces, but is not yet suitable for applications like real-time tool tracking or surgical skill assessment.

Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging.

Mass spectrometry imaging vows to enable simultaneous spatially resolved investigation of hundreds of metabolites in tissues, but it primarily relies on traditional ion images for non-data-driven metabolite visualization and analysis. The rendering and interpretation of ion images neither considers nonlinearities in the resolving power of mass spectrometers nor does it yet evaluate the statistical significance of differential spatial metabolite abundance. Here, we outline the computational framework moleculaR ( https://github.com/CeMOS-Mannheim/moleculaR ) that is expected to improve signal reliability by data-dependent Gaussian-weighting of ion intensities and that introduces probabilistic molecular mapping of statistically significant nonrandom patterns of relative spatial abundance of metabolites-of-interest in tissue. moleculaR also enables cross-tissue statistical comparisons and collective molecular projections of entire biomolecular ensembles followed by their spatial statistical significance evaluation on a single tissue plane. It thereby fosters the spatially resolved investigation of ion milieus, lipid remodeling pathways, or complex scores like the adenylate energy charge within the same image.

Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer.

Prostate cancer (PCa) diagnosis on multi-parametric magnetic resonance images (MRI) requires radiologists with a high level of expertise. Misalignments between the MRI sequences can be caused by patient movement, elastic soft-tissue deformations, and imaging artifacts. They further increase the complexity of the task prompting radiologists to interpret the images. Recently, computer-aided diagnosis (CAD) tools have demonstrated potential for PCa diagnosis typically relying on complex co-registration of the input modalities. However, there is no consensus among research groups on whether CAD systems profit from using registration. Furthermore, alternative strategies to handle multi-modal misalignments have not been explored so far. Our study introduces and compares different strategies to cope with image misalignments and evaluates them regarding to their direct effect on diagnostic accuracy of PCa. In addition to established registration algorithms, we propose 'misalignment augmentation' as a concept to increase CAD robustness. As the results demonstrate, misalignment augmentations can not only compensate for a complete lack of registration, but if used in conjunction with registration, also improve the overall performance on an independent test set.

Quantification of glomerular number and size distribution in normal rat kidneys using magnetic resonance imaging.

BACKGROUND: Glomerular number and size are important risk factors for chronic kidney disease (CKD) and cardiovascular disease and have traditionally been estimated using invasive techniques. Here, we report a novel technique to count and size every glomerulus in the rat kidney using magnetic resonance imaging (MRI). METHODS: The ferromagnetic nature of cationized ferritin allowed visualization of single glomeruli in high-resolution susceptibility-weighted MRI. A segmentation algorithm was used to identify and count all glomeruli within the whole kidney. To prove our concept, we estimated total glomerular number and mean glomerular volume of each kidney using design-based stereology. RESULTS: The glomerular counts obtained with MRI agreed well with estimates obtained using traditional methods [MRI, 32 785 (3117); stereology, 35 132 (3123)]. For the first time, the glomerular volume distribution for the entire kidney is shown. Additionally, the method is substantially faster than the current methods. CONCLUSIONS: MRI provides a new method for measuring these important microanatomical markers of disease risk and leads the way to in vivo analysis of these parameters, including longitudinal studies of animal models of CKD.

M2aia-Interactive, fast, and memory-efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data.

BACKGROUND: Mass spectrometry imaging (MSI) is a label-free analysis method for resolving bio-molecules or pharmaceuticals in the spatial domain. It offers unique perspectives for the examination of entire organs or other tissue specimens. Owing to increasing capabilities of modern MSI devices, the use of 3D and multi-modal MSI becomes feasible in routine applications-resulting in hundreds of gigabytes of data. To fully leverage such MSI acquisitions, interactive tools for 3D image reconstruction, visualization, and analysis are required, which preferably should be open-source to allow scientists to develop custom extensions. FINDINGS: We introduce M2aia (MSI applications for interactive analysis in MITK), a software tool providing interactive and memory-efficient data access and signal processing of multiple large MSI datasets stored in imzML format. M2aia extends MITK, a popular open-source tool in medical image processing. Besides the steps of a typical signal processing workflow, M2aia offers fast visual interaction, image segmentation, deformable 3D image reconstruction, and multi-modal registration. A unique feature is that fused data with individual mass axes can be visualized in a shared coordinate system. We demonstrate features of M2aia by reanalyzing an N-glycan mouse kidney dataset and 3D reconstruction and multi-modal image registration of a lipid and peptide dataset of a mouse brain, which we make publicly available. CONCLUSIONS: To our knowledge, M2aia is the first extensible open-source application that enables a fast, user-friendly, and interactive exploration of large datasets. M2aia is applicable to a wide range of MSI analysis tasks.

Unsupervised Domain Adaptation From Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks.

Anisotropic multi-slice Cardiac Magnetic Resonance (CMR) Images are conventionally acquired in patient-specific short-axis (SAX) orientation. In specific cardiovascular diseases that affect right ventricular (RV) morphology, acquisitions in standard axial (AX) orientation are preferred by some investigators, due to potential superiority in RV volume measurement for treatment planning. Unfortunately, due to the rare occurrence of these diseases, data in this domain is scarce. Recent research in deep learning-based methods mainly focused on SAX CMR images and they had proven to be very successful. In this work, we show that there is a considerable domain shift between AX and SAX images, and therefore, direct application of existing models yield sub-optimal results on AX samples. We propose a novel unsupervised domain adaptation approach, which uses task-related probabilities in an attention mechanism. Beyond that, cycle consistency is imposed on the learned patient-individual 3D rigid transformation to improve stability when automatically re-sampling the AX images to SAX orientations. The network was trained on 122 registered 3D AX-SAX CMR volume pairs from a multi-centric patient cohort. A mean 3D Dice of 0.86 ± 0.06 for the left ventricle, 0.65 ± 0.08 for the myocardium, and 0.77 ± 0.10 for the right ventricle could be achieved. This is an improvement of 25% in Dice for RV in comparison to direct application on axial slices. To conclude, our pre-trained task module has neither seen CMR images nor labels from the target domain, but is able to segment them after the domain gap is reduced. Code: https://github.com/Cardio-AI/3d-mri-domain-adaptation.

Is a circular polypropylene mesh appropriate for application at the esophageal hiatus? Results from an experimental study in a porcine model.

BACKGROUND: Mesh reinforcement in hiatal hernia surgery is debated. Randomized controlled trials have shown that recurrences may be reduced, but there is also the fear of mesh-related complications. Experimental studies on the characteristics of specific mesh types with regard to the risk of such complications are rare. The current study aimed to investigate the properties of a circular heavy-weight polypropylene mesh in terms of stenosis, migration, erosions, and adhesions in a porcine model. METHODS: A 55 x 55-mm heavy-weight polypropylene mesh with a 16.5-mm eccentric hole for the esophagus corresponding to a calculated mesh area of 2811 mm(2) and a hole area of 214 mm(2) were implanted in nine German Landrace pigs. Six weeks later, the meshes were explanted and investigated for size, shrinkage, migration and adhesions. RESULTS: The total mesh area shrank to a mean of 2,040 +/- 178 mm(2) (p < 0.001), and the hole for the esophagus showed a trend toward an increase to 239 +/- 38 mm(2) (p = 0.108). In not a single location did the mesh overhang the hiatal margin. The mean distance of retraction from the hiatal margin was 4.3 +/- 2.8 mm. Therefore, no stenoses, migrations, or erosions occurred. CONCLUSIONS: A circular heavy-weight polypropylene mesh seems to be appropriate for the application at the esophageal hiatus in terms of safety and stability. This means that it is characterized by a position-stable centered fixation around the esophagus without a tendency toward stenosis, migration, or erosion.

Flexible and comprehensive patient-specific mitral valve silicone models with chordae tendineae made from 3D-printable molds.

PURPOSE: Given the multitude of challenges surgeons face during mitral valve repair surgery, they should have a high confidence in handling of instruments and in the application of surgical techniques before they enter the operating room. Unfortunately, opportunities for surgical training of minimally invasive repair are very limited, leading to a situation where most surgeons undergo a steep learning curve while operating the first patients. METHODS: In order to provide a realistic tool for surgical training, a commercial simulator was augmented by flexible patient-specific mitral valve replica. In an elaborated production pipeline, finalized after many optimization cycles, models were segmented from 3D ultrasound and then 3D-printable molds were computed automatically and printed in rigid material, the lower part being water-soluble. After silicone injection, the silicone model was dissolved from the mold and anchored in the simulator. RESULTS: To our knowledge, our models are the first to comprise the full mitral valve apparatus, i.e., the annulus, leaflets, chordae tendineae and papillary muscles. Nine different valve molds were automatically created according to the proposed workflow (seven prolapsed valves and two valves with functional mitral insufficiency). From these mold geometries, 16 replica were manufactured. A material test revealed that EcoflexTM 00-30 is the most suitable material for leaflet-mimicking tissue out of seven mixtures. Production time was around 36 h per valve. Twelve surgeons performed various surgical techniques, e.g., annuloplasty, neo-chordae implantation, triangular leaflet resection, and assessed the realism of the valves very positively. CONCLUSION: The standardized production process guarantees a high anatomical recapitulation of the silicone valves to the segmented models and the ultrasound data. Models are of unprecedented quality and maintain a high realism during haptic interaction with instruments and suture material.

Replicated mitral valve models from real patients offer training opportunities for minimally invasive mitral valve repair.

OBJECTIVES: Minimally invasive mitral valve repair is considered a challenging procedure. Mastering the necessary skills takes years of training and clinical experience. To date, reconstructive surgery is performed mainly by a few surgeons with a strong track record, whereas trainees have only limited opportunities to practise. METHODS: A high-fidelity training simulator was equipped with novel silicone replicas of patient-specific mitral valves containing all of the anatomical components of the valve. The goal of this system was to aid members of the surgical community to overcome the steep learning curve. RESULTS: Twelve surgeons (5 experts and 7 surgical resident trainees) performed a minimally invasive mitral valve repair procedure on these models and assessed the usefulness for different applications. The trainees found the main application to be general surgical training and education for mitral valve repair, whereas the experts found the main benefit to be rehearsal for a specific patient. The skills of the trainees were improved in only a single session. The valve models placed in a water solution showed a high echogenicity. CONCLUSIONS: Preoperative patient-specific simulation could improve the safety and effectiveness of mitral valve repair in the hands of a larger number of surgeons. Because the system is based on a quantitative segmentation of the anatomy of the mitral valve, it offers young surgeons training in general dexterity and also provides an exact numerical quantitative assessment of valvular geometry. This system can be used to educate surgeons to strive for and achieve well-defined and measurable surgical changes to the anatomy of the valve and to achieve the desired functional results.

Volumetric cardiac quantification by using 3D dual-phase whole-heart MR imaging.

This study was approved by the local institutional ethics committee, and informed consent was obtained from all volunteers and patients. The purpose of the study was to assess ventricular volumes by using three-dimensional (3D) whole-heart data sets acquired during end-systolic and end-diastolic phases during one free-breathing magnetic resonance imaging examination. In five healthy volunteers and 10 patients, 3D dual cardiac phase data sets, short-axis multisection breath-hold images, and through-plane flow images of the great vessels were acquired. Within these data sets, statistic analyses were performed to compare stroke, end-systolic, and end-diastolic volumes for the left ventricle (LV) and the right ventricle (RV). Results showed that the breath-hold multisection approach, the flow measurement approach, and the new dual-phase 3D approach delivered comparable results for quantification of cardiac volumes and function. High correlation values greater than 0.95 were found when these methods were compared, and no significant differences were recognized for stroke, end-systolic, or end-diastolic volumes in either the LV or the RV.

AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques.

In biological assays, automated cell/colony segmentation and counting is imperative owing to huge image sets. Problems occurring due to drifting image acquisition conditions, background noise and high variation in colony features in experiments demand a user-friendly, adaptive and robust image processing/analysis method. We present AutoCellSeg (based on MATLAB) that implements a supervised automatic and robust image segmentation method. AutoCellSeg utilizes multi-thresholding aided by a feedback-based watershed algorithm taking segmentation plausibility criteria into account. It is usable in different operation modes and intuitively enables the user to select object features interactively for supervised image segmentation method. It allows the user to correct results with a graphical interface. This publicly available tool outperforms tools like OpenCFU and CellProfiler in terms of accuracy and provides many additional useful features for end-users.

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

Implementation and evaluation of a new workflow for registration and segmentation of pulmonary MRI data for regional lung perfusion assessment.

Recently it has been shown that regional lung perfusion can be assessed using time-resolved contrast-enhanced magnetic resonance (MR) imaging. Quantification of the perfusion images has been attempted, based on definition of small regions of interest (ROIs). Use of complete lung segmentations instead of ROIs could possibly increase quantification accuracy. Due to the low signal-to-noise ratio, automatic segmentation algorithms cannot be applied. On the other hand, manual segmentation of the lung tissue is very time consuming and can become inaccurate, as the borders of the lung to adjacent tissues are not always clearly visible. We propose a new workflow for semi-automatic segmentation of the lung from additionally acquired morphological HASTE MR images. First the lung is delineated semi-automatically in the HASTE image. Next the HASTE image is automatically registered with the perfusion images. Finally, the transformation resulting from the registration is used to align the lung segmentation from the morphological dataset with the perfusion images. We evaluated rigid, affine and locally elastic transformations, suitable optimizers and different implementations of mutual information (MI) metrics to determine the best possible registration algorithm. We located the shortcomings of the registration procedure and under which conditions automatic registration will succeed or fail. Segmentation results were evaluated using overlap and distance measures. Integration of the new workflow reduces the time needed for post-processing of the data, simplifies the perfusion quantification and reduces interobserver variability in the segmentation process. In addition, the matched morphological data set can be used to identify morphologic changes as the source for the perfusion abnormalities.

Application of a new segmentation tool based on interactive simplex meshes to cardiac images and pulmonary MRI data.

RATIONALE AND OBJECTIVES: Medical image segmentation is still very time consuming and is therefore seldom integrated into clinical routine. Various three-dimensional (3D) segmentation approaches could facilitate the work, but they are rarely used in clinical setups because of complex initialization and parametrization of such models. MATERIALS AND METHODS: We developed a new semiautomatic 3D-segmentation tool based on deformable simplex meshes. The user can define attracting points in the original image data. The new deformation algorithm guarantees that the surface model will pass through these interactively set points. The user can directly influence the evolution of the deformable model and gets direct feedback during the segmentation process. RESULTS: The segmentation tool was evaluated for cardiac image data and magnetic resonance imaging lung images. Comparison with manual segmentation showed high accuracy. Time needed for delineation of the various structures could be reduced in some cases. The model was not sensitive to noise in the input data and model initialization. CONCLUSIONS: The tool is suitable for fast interactive segmentation of any kind of 3D or 3D time-resolved medical image data. It enables the clinician to influence a complex 3D-segmentation algorithm and makes this algorithm controllable. The better the quality of the data, the less interaction is required. The tool still works when the processed images have low quality.

Comparison of four freely available frameworks for image processing and visualization that use ITK.

Most image processing and visualization applications allow users to configure computation parameters and manipulate the resulting visualizations. SCIRun, VolView, MeVisLab, and the Medical Interaction Toolkit (MITK) are four image processing and visualization frameworks that were built for these purposes. All frameworks are freely available and all allow the use of the ITK C++ library. In this paper, the benefits and limitations of each visualization framework are presented to aid both application developers and users in the decision of which framework may be best to use for their application. The analysis is based on more than 50 evaluation criteria, functionalities, and example applications. We report implementation times for various steps in the creation of a reference application in each of the compared frameworks. The data-flow programming frameworks, SCIRun and MeVisLab, were determined to be best for developing application prototypes, while VolView was advantageous for nonautomatic end-user applications based on existing ITK functionalities, and MITK was preferable for automated end-user applications that might include new ITK classes specifically designed for the application.

Computer-based comparison of different methods for selecting mitral annuloplasty ring size.

BACKGROUND: Ring sizing for mitral valve annuloplasty is conventionally done intraoperatively using specific 'sizer' instruments, which are placed onto the valve tissue. This approach is barely reproducible since different sizing strategies have been established among surgeons. The goal of this study is to virtually apply different sizing methods on the basis of pre-repair echocardiography to find out basic differences between sizing strategies. METHODS: In three-dimensional echocardiographs of 43 patients, the mitral annulus and the contour of the anterior mitral leaflet were segmented using MITK Mitralyzer software. Similarly, three-dimensional virtual models of Carpentier-Edwards Physio II annuloplasty rings and their corresponding sizers were interactively generated from computer tomography images. For each patient, the matching annuloplasty ring was selected repeatedly according to popular sizing strategies, such as the height of anterior mitral leaflet, the intercommissural distance and the surface area of anterior mitral leaflet. The areas of the selected rings were considered as the neo-surface area of the mitral annulus after implantation. RESULTS: The sizing of the mitral valve according to the height of anterior mitral leaflet (mean ring size = 29.9 ± 3.90), intercommissural distance (mean ring size = 37.5 ± 1.92) or surface area of anterior mitral leaflet (mean ring size = 32.7 ± 3.3) led to significantly different measurements (p ≤ 0.01). In contrary to intercommissural distance, height and surface area of the anterior mitral leaflet exhibited significant variations between the patients (p ≤ 0.01). The sizing according to the height of anterior mitral leaflet led to the maximal reduction of the mitral annulus surface area followed by the sizing according to the surface area of anterior mitral leaflet and finally by the intercommissural distance. CONCLUSIONS: This novel comprehensive computer-based analysis reveals that the surveyed sizing methods led to the selection of significantly different annuloplasty rings and therefore underscore the ambiguity of routinely applied annuloplasty sizing strategies.

The geometrical effect of different annuloplasty rings on mitral valve annulus.

BACKGROUND: Different types of mitral annuloplasty rings are commercially available. The aim of this study was to investigate the effect of implantation of six types of annuloplasty rings on the geometry and dynamics of the mitral valve. METHODS: Three-dimensional echocardiography images of 42 patients were acquired to visualize the mitral valve annulus. Virtual representations of six commercially available annuloplasty rings were matched to anatomical mitral annuli of each patient according to anterolateral-posteromedial diameter. The virtual displacement of each annuloplasty ring after the implantation was measured and compared with the other rings. RESULTS: Patients with severe mitral regurgitation had significantly dilated annuli according to anterolateral-posteromedial diameter, anterior-posterior diameter and to annulus circumference. Anterior and posterior heights of the mitral annuli and non-planarity angle showed no significant differences among different patients with different degree of mitral regurgitation. The ratio of anterior-posterior to anterolateral-posteromedial diameter was almost identical in all groups with identical annular shapes. The implantation of the Carpentier-Edwards Classic Annuloplasty Ring™ led to maximal displacement of mitral annulus, followed by the IM-Ring™, without a statistical significance. In contrary, the implantation of a MyxoETlogix Ring™ was associated with minimal displacement of mitral annulus throughout the groups, but without statistical significance. CONCLUSIONS: The implantation of different ring types in patients with different annuli shapes and dimensions did not lead to any significant change in the configuration of mitral annuli after the virtual implantation of the tested annuloplasty rings.