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Cine MRI during spontaneous cramps in women with menstrual pain.

ABSTRACT: BACKGROUND:The lack of noninvasive methods to study dysmenorrhea has resulted in poor understanding of the mechanisms underlying pain, insufficient diagnostic tests, and limited treatment options. To address this knowledge gap, we have developed a magnetic resonance imaging-based strategy for continuously monitoring the uterus in relationship to participants' spontaneous pain perception. OBJECTIVE:The study objective was to evaluate whether magnetic resonance imaging can detect real-time changes in myometrial activity during cramping episodes in women with dysmenorrhea, with a handheld squeeze bulb for pain reporting. STUDY DESIGN:Sixteen women with dysmenorrhea and 10 healthy control women both on and off their menses were evaluated with magnetic resonance imaging while not taking analgesic medication. Continuous magnetic resonance imaging was acquired using half-Fourier acquisition single-shot turbo spin echo sequence along with simultaneous reporting of pain severity with a squeeze bulb. Pearson's coefficient was used to compare results between reviewers. Proportional differences between women with dysmenorrhea and controls on/off menses were evaluated with a Fisher exact test. The temporal relationships between signal changes were evaluated with Monte Carlo simulations. RESULTS:Spontaneous progressive decreases in myometrial signal intensity were more frequently observed in women on their menses than in the absence of pain in the same women off their menses or participants without dysmenorrhea (P < .01). Women without reductions in myometrial signal intensity on their menses either had a history of endometriosis or were not in pain. Observations of myometrial events were consistently reported between 2 raters blinded to menstrual pain or day status (r = 0.97, P < .001). Episodes of cramping occurred either immediately before or 32-70 seconds after myometrial signal change onset (P < .05). CONCLUSION:Transient decreases in myometrial uterine T2-weighted signal intensity can be reliably measured in women with menstrual pain. The directionality of signal change and temporal relationship to pain onset suggest that cramping pain may be caused by a combination of uterine pressure and hemodynamic dysfunction.

SUBMITTER: Hellman KM

PROVIDER: S-EPMC5916049 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Primary dysmenorrhea is a common condition in women of reproductive age. A previous app-based study undertaken by our group demonstrated that a smartphone app supporting self-acupressure introduced by a health care professional can reduce menstrual pain. OBJECTIVE:This study aims to evaluate whether a specific smartphone app is effective in reducing menstrual pain in 18- to 34-year-old women with primary dysmenorrhea in a self-care setting. One group of women has access to the full-featured study app and will be compared with 2 control groups who have access to fewer app features. Here, we report the trial design, app development, user access, and engagement. METHODS:On the basis of the practical implications of the previous app-based study, we revised and reengineered the study app and included the ResearchKit (Apple Inc) framework. Behavior change techniques (BCTs) were implemented in the app and validated by expert ratings. User access was estimated by assessing recruitment progress over time. User evolution and baseline survey respondent rate were assessed to evaluate user engagement. RESULTS:The development of the study app for a 3-armed randomized controlled trial required a multidisciplinary team. The app is accessible for the target population free of charge via the Apple App Store. In Germany, within 9 months, the app was downloaded 1458 times and 328 study participants were recruited using it without external advertising. A total of 98.27% (5157/5248) of the app-based baseline questions were answered. The correct classification of BCTs used in the app required psychological expertise. CONCLUSIONS:Conducting an innovative app study requires multidisciplinary effort. Easy access and engagement with such an app can be achieved by recruitment via the App Store. Future research is needed to investigate the determinants of user engagement, optimal BCT application, and potential clinical and self-care scenarios for app use. TRIAL REGISTRATION:ClinicalTrials.gov NCT03432611; https://clinicaltrials.gov/ct2/show/NCT03432611 (Archived by WebCite at http://www.webcitation.org/75LLAcnCQ).

Project description:Defining myocardial contours is often the most time-consuming portion of dynamic cardiac MRI image analysis. Displacement encoding with stimulated echoes (DENSE) is a quantitative MRI technique that encodes tissue displacement into the phase of the complex MRI images. Cine DENSE provides a time series of these images, thus facilitating the non-invasive study of myocardial kinematics. Epicardial and endocardial contours need to be defined at each frame on cine DENSE images for the quantification of regional displacement and strain as a function of time. This work presents a reliable and effective two-dimensional semi-automated segmentation technique that uses the encoded motion to project a manually-defined region of interest through time. Contours can then easily be extracted for each cardiac phase. This method boasts several advantages, including, (1) parameters are based on practical physiological limits, (2) contours are calculated for the first few cardiac phases, where it is difficult to visually distinguish blood from myocardium, and (3) the method is independent of the shape of the tissue delineated and can be applied to short- or long-axis views, and on arbitrary regions of interest. Motion-guided contours were compared to manual contours for six conventional and six slice-followed mid-ventricular short-axis cine DENSE datasets. Using an area measure of segmentation error, the accuracy of the segmentation algorithm was shown to be similar to inter-observer variability. In addition, a radial segmentation error metric was introduced for short-axis data. The average radial epicardial segmentation error was 0.36+/-0.08 and 0.40+/-0.10 pixels for slice-followed and conventional cine DENSE, respectively, and the average radial endocardial segmentation error was 0.46+/-0.12 and 0.46+/-0.16 pixels for slice following and conventional cine DENSE, respectively. Motion-guided segmentation employs the displacement-encoded phase shifts intrinsic to DENSE MRI to accurately propagate a single set of pre-defined contours throughout the remaining cardiac phases.

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Cine MRI during spontaneous cramps in women with menstrual pain.

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