Posterior vaginal prolapse shape and position changes at maximal Valsalva seen in 3-D MRI-based models

Luo, Jiajia; Larson, Kindra; Fenner, Dee E.; Ashton‐Miller, James A.; DeLancey, John O.L.

doi:10.1007/s00192-012-1760-9

Cited by 27 publications

(21 citation statements)

References 21 publications

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“…Also, we found that the apical portion of the vagina support system has greater compliance than the distal portion. Previous MR imaging and modeling studies demonstrated the ability to study vaginal wall deformation at maximum Valsalva loading in living women with cystocele and rectocele [10–12]. The present study extends their work on the qualitative changes in vaginal wall location by providing the first quantitative measurements of the movements during Valsalva.…”

Section: Discussionsupporting

confidence: 53%

Relationship between intra-abdominal pressure and vaginal wall movements during Valsalva in women with and without pelvic organ prolapse: technique development and early observations

et al. 2014

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Objectives To develop and test a method for measuring the relationship between the rise in intra-abdominal pressure and sagittal plane movements of the anterior and posterior vaginal walls during Valsalva in a pilot sample of women with and without prolapse. Methods Mid-sagittal MRI images were obtained during Valsalva while changes in intra-abdominal pressure were measured via a bladder catheter in 5 women with cystocele, 5 women with rectocele, and 5 controls. The regional compliance of the anterior and posterior vagina wall support systems were estimated from the ratio of displacement (mm) of equidistant points along the anterior and posterior vaginal walls to intra-abdominal pressure rise (mmHg). Results The compliance of both anterior and posterior vaginal wall support systems varied along different regions of vaginal wall for all three groups, with the highest compliance found near the vaginal apex and the lowest near the introitus. Women with cystocele had more compliant anterior and posterior vaginal wall support systems than women with rectocele. The movement direction differs between cystocele and rectocele. In cystocele, the anterior vaginal wall moves mostly toward the vaginal orifice in the upper vagina, but in a ventral direction in the lower vagina. In rectocele, the direction of the posterior vaginal wall movement is generally toward the vaginal orifice. Conclusions Movement of the vaginal wall and compliance of its support is quantifiable and was found to vary along the length of the vagina. Compliance was greatest in the upper vagina of all groups. Women with cystocele demonstrated the most compliant vaginal wall support.

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Section: Discussionsupporting

confidence: 53%

Relationship between intra-abdominal pressure and vaginal wall movements during Valsalva in women with and without pelvic organ prolapse: technique development and early observations

et al. 2014

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“…The characteristic downward displacement, forward protrusion, and perineal descent as well as the “kneeling” profile of the rectocele matching those seen in 3-D stress MRI (Luo et al, 2012b) can also be seen in the model results (Figure 5D, E and F). One can also see that the CL and USL lengthen under the application of pressure.…”

Section: Resultssupporting

confidence: 61%

“…By allowing the consequences of specific alterations in individual components of the support system to be studied the model facilitates analyses that can never ethically be conducted in human subjects. The simulation results are similar to cystocele and rectocele seen during clinical exams and they also exhibit similarities to the geometry of 3D models based on sequential images taken during “Stress” MR examinations of women with anterior and posterior compartment prolapse made during a Valsalva maneuver (Larson et al, 2010a; Luo et al, 2012b). The model should prove useful for planning surgeries for different types of impairment.…”

Section: Discussionsupporting

confidence: 63%

“…As described in our previous work (Luo et al, 2012b), the subject underwent supine multi-planar, two-dimensional, fast spin, proton density MR imaging both at rest and during maximal Valsalva using a 3T superconducting magnet (Philips Medical Systems Inc., Bothell, WA, USA) with version 2.5.1.0 software. At rest, 30 images were serially obtained in the axial, sagittal, and coronal planes using a 20×20 cm field of view, 4 mm slice thickness, and a 1 mm gap between slices.…”

Section: Methodsmentioning

confidence: 99%

“…The model was further qualitatively validated by comparing the deformations of the model anterior and posterior vaginal walls against published 3-D measurements of cystocele and rectocele deformation in 3-D Stress MR studies of women with these conditions (Larson et al, 2010a; Luo et al, 2012b) to check whether predicted prolapse deformation characteristics were reasonable.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A multi-compartment 3-D finite element model of rectocele and its interaction with cystocele

Luo

Chen

Fenner

et al. 2015

Journal of Biomechanics

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We developed a subject-specific 3-D finite element model to understand the mechanics underlying formation of female pelvic organ prolapse, specifically a rectocele and its interaction with a cystocele. The model was created from MRI 3-D geometry of a healthy 45 year-old multiparous woman. It included anterior and posterior vaginal walls, levator ani muscle, cardinal and uterosacral ligaments, anterior and posterior arcus tendineus fascia pelvis, arcus tendineus levator ani, perineal body, perineal membrane and anal sphincter. Material properties were mostly from the literature. Tissue impairment was modeled as decreased tissue stiffness based on previous clinical studies. Model equations were solved using Abaqus v 6.11. The sensitivity of anterior and posterior vaginal wall geometry was calculated for different combinations tissue impairments under increasing intraabdominal pressure. Prolapse size was reported as POP-Q point at point Bp for rectocele and point Ba for cystocele. Results show that a rectocele resulted from impairments of the levator ani and posterior compartment support. For 20% levator and 85% posterior support impairments, simulated rectocele size (at POP-Q point: Bp) increased 0.29 mm/cm H2O without apical impairment and 0.36 mm/cm H2O with 60% apical impairment, as intraabdominal pressures increased from 0 to 150 cm H2O. Apical support impairment could result in the development of either a cystocele or rectocele. Simulated repair of posterior compartment support decreased rectocele but increased a preexisting cystocele. We conclude that development of rectocele and cystocele depend on the presence of anterior, posterior, levator and/or or apical support impairments, as well as the interaction of the prolapse with the opposing compartment.

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Convolutional neural network‐based pelvic floor structure segmentation using magnetic resonance imaging in pelvic organ prolapse

et al. 2020

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Purpose: Automated segmentation could improve the efficiency of modeling-based pelvic organ prolapse (POP) evaluations. However, segmentation performance is limited by the blurry soft tissue boundaries. In this study, we aimed to present a hybrid solution for uterus, rectum, bladder, and levator ani muscle segmentation by combining a convolutional neural network (CNN) and a level set method. Methods: We used 24 sagittal pelvic floor magnetic resonance (MR) series from six anterior vaginal prolapse and six posterior vaginal prolapse subjects (a total 528 MR images). The stress MR images were performed both at rest and at maximal Valsalva. We assigned 264 images for training, 132 images for validation, and 132 images for testing. A CNN was designed by introducing a multi-resolution features pyramid module (MRFP) into an encoder-decoder model. Depth separable convolution and pretraining were used to improve model convergence. Multiclass cross entropy loss and multiclass Dice loss were used for model training. The dice similarity coefficient (DSC) and average surface distance (ASD) were used for evaluating the segmentation results. To prove the effectiveness of our model, we compared it with advanced segmentation methods including Deeplabv3+, U-Net, and FCN-8s. The ablation study was designed to quantify the contributions of MRFP, the encoder network, and pretraining. Besides, we investigated the working mechanism of MRFP in the segmentation network by comparing our model with three of its variants. Finally, the level set method was used to improve the CNN model further. Results: Dice loss showed better segmentation performance than multiclass cross entropy loss. MRFP was efficacious for different encoder networks. With MRFP, U-Net and U-Net-X (X represents Xception encoder network) have improved the DSC, on average by 6.8 and 5.3 points. Compared with different CNN models, our model achieved the highest average DSC of 65.6 points and the lowest average ASD of 2.9 mm. With the level set method, the DSC of our model improved to 69.4 points. Conclusions: MRFP proved to be effective in addressing the blurry soft tissue boundary problem on pelvic floor MR images. A hybrid solution based on CNN and level set method was presented for pelvic organ segmentation both at rest and at maximal Valsalva; with this method, we achieved state-of-the-art results.

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Posterior vaginal prolapse shape and position changes at maximal Valsalva seen in 3-D MRI-based models

Cited by 27 publications

References 21 publications

Relationship between intra-abdominal pressure and vaginal wall movements during Valsalva in women with and without pelvic organ prolapse: technique development and early observations

Relationship between intra-abdominal pressure and vaginal wall movements during Valsalva in women with and without pelvic organ prolapse: technique development and early observations

A multi-compartment 3-D finite element model of rectocele and its interaction with cystocele

Convolutional neural network‐based pelvic floor structure segmentation using magnetic resonance imaging in pelvic organ prolapse

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