3D Observation of Pelvic Organs with Dynamic MRI Segmentation: A Bridge Toward Patient-Specific Models.

INTRODUCTION AND HYPOTHESIS: Female pelvic organ prolapses are common, but their treatment is challenging. Notably, diagnosis and understanding of these troubles remain incomplete. Tridimensional observations of displacement and deformation of the pelvic organs during a strain could support a better understanding and help to develop comprehensive tools for preoperative planning. METHODS: The present feasibility study evaluates tridimensional dynamic MRI in 12 healthy volunteers. Tridimensional acquisitions were approximated using five intersecting slices, each recorded twice per second. MRI was performed during rest and strain, with intrarectal and intravaginal contrast gel. Subject-specific dynamic 3D models were built for each volunteer through segmentation. RESULTS: For each volunteer, pelvic organs could be segmented in three dimensions with a rate of acquisition of two cycles per second on five slices, allowing for a fluid observation of displacements and deformations during strain. Manual segmentation of a full strain required 2 h and 33 min on average. The upper limit of the rectum and the pelvic floor were the most difficult structures to identify. This technique is limited by its time-consuming manual segmentation, which impedes its implantation for routine clinical use. This method must be tried in patients with pelvic organ prolapse. CONCLUSIONS: This multi-planar acquisition technique applied during a dynamic MRI allows for observation of displacement and deformations of pelvic organs during a strain.

One-Year Longitudinal Assessment of Patients With CMT1A Using Quantitative MRI.

BACKGROUND AND OBJECTIVES: Intramuscular fat fraction (FF) assessed using quantitative MRI (qMRI) has emerged as one of the few responsive outcome measures in CMT1A suitable for future clinical trials. This study aimed to identify the relevance of multiple qMRI biomarkers for tracking longitudinal changes in CMT1A and to assess correlations between MRI metrics and clinical parameters. METHODS: qMRI was performed in CMT1A patients at 2 time points, a year apart, and various metrics were extracted from 3-dimensional volumes of interest at thigh and leg levels. A semiautomated segmentation technique was used, enabling the analysis of central slices and a larger 3D muscle volume. Metrics included proton density (PD), magnetization transfer ratio (MTR), and intramuscular FF. The sciatic and tibial nerves were also assessed. Disease severity was gauged using Charcot Marie Tooth Neurologic Score (CMTNSv2), Charcot Marie Tooth Examination Score, Overall Neuropathy Limitation Scale scores, and Medical Research Council (MRC) muscle strength. RESULTS: Twenty-four patients were included. FF significantly rose in the 3D volume at both thigh (+1.04% ± 2.19%, p = 0.041) and leg (+1.36% ± 1.87%, p = 0.045) levels. The 3D analyses unveiled a length-dependent gradient in FF, ranging from 22.61% ± 10.17% to 26.17% ± 10.79% at the leg level. There was noticeable variance in longitudinal changes between muscles: +3.17% ± 6.86% (p = 0.028) in the tibialis anterior compared with 0.37% ± 4.97% (p = 0.893) in the gastrocnemius medialis. MTR across the entire thigh volume showed a significant decline between the 2 time points -2.75 ± 6.58 (p = 0.049), whereas no significant differences were noted for the 3D muscle volume and PD. No longitudinal changes were observed in any nerve metric. Potent correlations were identified between FF and primary clinical measures: CMTNSv2 (ρ = 0.656; p = 0.001) and MRC in the lower limbs (ρ = -0.877; p < 0.001). DISCUSSION: Our results further support that qMRI is a promising tool for following up longitudinal changes in CMT1A patients, FF being the paramount MRI metric for both thigh and leg regions. It is crucial to scrutinize the postimaging data extraction methods considering that annual changes are minimal (around +1.5%). Given the varied FF distribution, the existence of a length-dependent gradient, and the differential fatty involution across muscles, 3D volume analysis appeared more suitable than single slice analysis.

Four-dimensional reconstruction and characterization of bladder deformations.

BACKGROUND AND OBJECTIVE: Pelvic floor disorders are prevalent diseases and patient care remains difficult as the dynamics of the pelvic floor remains poorly understood. So far, only 2D dynamic observations of straining exercises at excretion are available in the clinics and 3D mechanical defects of pelvic organs are not well studied. In this context, we propose a complete methodology for the 3D representation of non-reversible bladder deformations during exercises, combined with a 3D representation of the location of the highest strain areas on the organ surface. METHODS: Novel image segmentation and registration approaches have been combined with three geometrical configurations of up-to-date rapid dynamic multi-slice MRI acquisitions for the reconstruction of real-time dynamic bladder volumes. RESULTS: For the first time, we proposed real-time 3D deformation fields of the bladder under strain from in-bore forced breathing exercises. The potential of our method was assessed on eight control subjects undergoing forced breathing exercises. We obtained average volume deviations of the reconstructed dynamic volume of bladders around 2.5% and high registration accuracy with mean distance values of 0.4 ± 0.3 mm and Hausdorff distance values of 2.2 ± 1.1 mm. CONCLUSIONS: The proposed framework provides proper 3D+t spatial tracking of non-reversible bladder deformations. This has immediate applicability in clinical settings for a better understanding of pelvic organ prolapse pathophysiology. This work can be extended to patients with cavity filling or excretion problems to better characterize the severity of pelvic floor pathologies or to be used for preoperative surgical planning.

The Impact of Fatty Infiltration on MRI Segmentation of Lower Limb Muscles in Neuromuscular Diseases: A Comparative Study of Deep Learning Approaches.

BACKGROUND: Deep learning methods have been shown to be useful for segmentation of lower limb muscle MRIs of healthy subjects but, have not been sufficiently evaluated on neuromuscular disease (NDM) patients. PURPOSE: Evaluate the influence of fat infiltration on convolutional neural network (CNN) segmentation of MRIs from NMD patients. STUDY TYPE: Retrospective study. SUBJECTS: Data were collected from a hospital database of 67 patients with NMDs and 14 controls (age: 53 ± 17 years, sex: 48 M, 33 F). Ten individual muscles were segmented from the thigh and six from the calf (20 slices, 200 cm section). FIELD STRENGTH/SEQUENCE: A 1.5 T. Sequences: 2D T1 -weighted fast spin echo. Fat fraction (FF): three-point Dixon 3D GRE, magnetization transfer ratio (MTR): 3D MT-prepared GRE, T2: 2D multispin-echo sequence. ASSESSMENT: U-Net 2D, U-Net 3D, TransUNet, and HRNet were trained to segment thigh and leg muscles (101/11 and 95/11 training/validation images, 10-fold cross-validation). Automatic and manual segmentations were compared based on geometric criteria (Dice coefficient [DSC], outlier rate, absence rate) and reliability of measured MRI quantities (FF, MTR, T2, volume). STATISTICAL TESTS: Bland-Altman plots were chosen to describe agreement between manual vs. automatic estimated FF, MTR, T2 and volume. Comparisons were made between muscle populations with an FF greater than 20% (G20+) and lower than 20% (G20-). RESULTS: The CNNs achieved equivalent results, yet only HRNet recognized every muscle in the database, with a DSC of 0.91 ± 0.08, and measurement biases reaching -0.32% ± 0.92% for FF, 0.19 ± 0.77 for MTR, -0.55 ± 1.95 msec for T2, and - 0.38 ± 3.67 cm3 for volume. The performances of HRNet, between G20- and G20+ decreased significantly. DATA CONCLUSION: HRNet was the most appropriate network, as it did not omit any muscle. The accuracy obtained shows that CNNs could provide fully automated methods for studying NMDs. However, the accuracy of the methods may be degraded on the most infiltrated muscles (>20%). EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

Characterization of surface motion patterns in highly deformable soft tissue organs from dynamic MRI: An application to assess 4D bladder motion.

BACKGROUND AND OBJECTIVES: Dynamic Magnetic Resonance Imaging (MRI) may capture temporal anatomical changes in soft tissue organs with high-contrast but the obtained sequences usually suffer from limited volume coverage which makes the high-resolution reconstruction of organ shape trajectories a major challenge in temporal studies. Because of the variability of abdominal organ shapes across time and subjects, the objective of the present study is to go towards 3D dense velocity measurements to fully cover the entire surface and to extract meaningful features characterizing the observed organ deformations and enabling clinical action or decision. METHODS: We present a pipeline for characterization of bladder surface dynamics during deep respiratory movements. For a compact shape representation, the reconstructed temporal volumes were first used to establish subject-specific dynamical 4D mesh sequences using the large deformation diffeomorphic metric mapping (LDDMM) framework. Then, we performed a statistical characterization of organ dynamics from mechanical parameters such as mesh elongations and distortions. Since we refer to organs as non-flat surfaces, we have also used the mean curvature change as metric to quantify surface evolution. However, the numerical computation of curvature is strongly dependant on the surface parameterization (i.e. the mesh resolution). To cope with this dependency, we employed a non-parametric method for surface deformation analysis. Independent of parameterization and minimizing the length of the geodesic curves, it stretches smoothly the surface curves towards a sphere by minimizing a Dirichlet energy. An Eulerian PDE approach is used to derive a shape descriptor from the curve-shortening flow. Intercorrelations between individuals' motion patterns are computed using the Laplace-Beltrami Operator (LBO) eigenfunctions for spherical mapping. RESULTS: Application to extracting characterization correlation curves for locally-controlled simulated shape trajectories demonstrates the stability of the proposed shape descriptor. Its usability was shown on MRI acquired for seven healthy participants for which the bladder was highly deformed by maximum of inspiration. As expected, the study showed that deformations occured essentially on the top lateral regions. CONCLUSION: Promising results were obtained, showing the organ in its 3D complexity during deformation due to strain conditions. Smooth genus-0 manifold reconstruction from sparse dynamic MRI data is employed to perform a statistical shape analysis for the determination of bladder deformation.

Overview of MR Image Segmentation Strategies in Neuromuscular Disorders.

Neuromuscular disorders are rare diseases for which few therapeutic strategies currently exist. Assessment of therapeutic strategies efficiency is limited by the lack of biomarkers sensitive to the slow progression of neuromuscular diseases (NMD). Magnetic resonance imaging (MRI) has emerged as a tool of choice for the development of qualitative scores for the study of NMD. The recent emergence of quantitative MRI has enabled to provide quantitative biomarkers more sensitive to the evaluation of pathological changes in muscle tissue. However, in order to extract these biomarkers from specific regions of interest, muscle segmentation is mandatory. The time-consuming aspect of manual segmentation has limited the evaluation of these biomarkers on large cohorts. In recent years, several methods have been proposed to make the segmentation step automatic or semi-automatic. The purpose of this study was to review these methods and discuss their reliability, reproducibility, and limitations in the context of NMD. A particular attention has been paid to recent deep learning methods, as they have emerged as an effective method of image segmentation in many other clinical contexts.

A novel segmentation framework dedicated to the follow-up of fat infiltration in individual muscles of patients with neuromuscular disorders.

PURPOSE: To propose a novel segmentation framework that is dedicated to the follow-up of fat infiltration in individual muscles of patients with neuromuscular disorders. METHODS: We designed a semi-automatic segmentation pipeline of individual leg muscles in MR images based on automatic propagation through nonlinear registrations of initial delineation in a minimal number of MR slices. This approach has been validated for the segmentation of individual muscles from MRI data sets, acquired over a 10-month period, from thighs and legs in 10 patients with muscular dystrophy. The robustness of the framework was evaluated using conventional metrics related to muscle volume and clinical metrics related to fat infiltration. RESULTS: High accuracy of the semi-automatic segmentation (mean Dice similarity coefficient higher than 0.89) was reported. The provided method has excellent reliability regarding the reproducibility of the fat fraction estimation, with an average intraclass correlation coefficient score of 0.99. Furthermore, the present segmentation framework was determined to be more reliable than the intra-expert performance, which had an average intraclass correlation coefficient of 0.93. CONCLUSION: The proposed framework of segmentation can successfully provide an effective and reliable tool for accurate follow-up of any MRI biomarkers in neuromuscular disorders. This method could assist the quantitative assessment of muscular changes occurring in such diseases.

Multi-object segmentation framework using deformable models for medical imaging analysis.

Segmenting structures of interest in medical images is an important step in different tasks such as visualization, quantitative analysis, simulation, and image-guided surgery, among several other clinical applications. Numerous segmentation methods have been developed in the past three decades for extraction of anatomical or functional structures on medical imaging. Deformable models, which include the active contour models or snakes, are among the most popular methods for image segmentation combining several desirable features such as inherent connectivity and smoothness. Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms, there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques. This paper presents a novel open-source framework called deformable model array (DMA) for the segmentation of multiple and complex structures of interest in different imaging modalities. While most active contour algorithms can extract one region at a time, DMA allows integrating several deformable models to deal with multiple segmentation scenarios. Moreover, it is possible to consider any existing explicit deformable model formulation and even to incorporate new active contour methods, allowing to select a suitable combination in different conditions. The framework also introduces a control module that coordinates the cooperative evolution of the snakes and is able to solve interaction issues toward the segmentation goal. Thus, DMA can implement complex object and multi-object segmentations in both 2D and 3D using the contextual information derived from the model interaction. These are important features for several medical image analysis tasks in which different but related objects need to be simultaneously extracted. Experimental results on both computed tomography and magnetic resonance imaging show that the proposed framework has a wide range of applications especially in the presence of adjacent structures of interest or under intra-structure inhomogeneities giving excellent quantitative results.

Female patient-specific finite element modeling of pelvic organ prolapse (POP).

Pelvic organ prolapse (POP) occurs only in women and becomes more common as women age. However, the surgical practices remain poorly evaluated. The realization of a simulator of the dynamic behavior of the pelvic organs is then identified as a need. It allows the surgeon to estimate the functional impact of his actions before his implementation. In this work, the simulation will be based on a patient-specific approach in which each geometrical model will be carried out starting from magnetic resonance image (MRI) acquisition of pelvic organs of one patient. To determine the strain and stress in the soft biological tissues, hyperelastic constitutive laws are used in the context of finite element analysis. The Yeoh model has been implemented into an in-house finite element code FER to model these organ tissues taking into account large deformations with multiple contacts. The 2D and 3D models are considered in this preliminary study and the results show that our method can help to improve the understanding of different forms of POP.

Geometric modeling of pelvic organs.

The pelvic floor can be subjected to different disorders, coming from a physiological change in the spatial configuration of the organs of interest: the bladder, the rectum, the uterus and the vagina. However, resort to surgery to replace them is complicated to achieve. In order to support the decision of the surgeon as to the invasive method to use for the patient, the MoDyPe (Pelvis Dynamics Modeling) project was launched, aiming at building a patient specific pelvic organ behavior. Our approach consists in creating thick surfaces of hollow organs, using periodic B-splines and offsets, then in controlling their discretization and in exporting a hexahedral model to provide input data for the study on the dynamics of the soft bodies of interest. From a segmentation step providing a dataset of 3D points, a function is built to measure the bidirectional distance between the surface and the data. It is minimized with an alternate iterative Hoschek-like method, by updating the parametric map and moving the control points. Several offsets of the base surface are then created to build up the thickness of the organ.

Automatic estimation of pelvic organ anatomical references.

Pelvic floor diseases cover pathologies of which physiopathology is not well understood. 2D sagittal MRI sequences used in the clinical assessment allow to visualize the dynamic behavior of the main organs involved (bladder, uterus-vagina and rectum). Clinicians use anatomical landmarks and measurements related to the pelvic organs in their pathology assessment. Usually, those tasks are performed manually which results in being both tedious and subject to operator dependency. A methodology is proposed to attempt a quantitative and objective characterization of the organ behaviors under abdominal strain condition. This approach automatically assesses the organ movements, through the estimation of characteristic angles (anorectal angle, uterovaginal angle, bladder inclination), and the tracking of anatomically significant points (anorectal angle vertex, uterovaginal angle vertex, bladder neck). From a multi-subject analysis, pathological organs have been distinguished from healthy ones, which shows the relevance of the computed features. In addition, a stability analysis has shown the soundness of the approach.

Bone surface reconstruction using localized freehand ultrasound imaging.

We propose a bone surface reconstruction method using localized ultrasound imagery. A set of bone contours is first extracted from a series of freehand 2D B-mode localized images, using an automatic segmentation method. This set is then used to reconstruct the bone surface with a tensor product B-splines approximation. Results of the partial surface reconstruction are shown for real bones and for a phantom physical model.