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Craniofacial deformity associated with holoprosencephaly. Mid-sagittal b-FFE in a fetus at 34 WG shows a profoundly small head results in frontal sloping and abnormal face profile in this fetus with holoprosencephaly (semilobar). Note the abnormal anterior fusion of cerebral hemispheres, partial anterior agenesis of the corpus callosum (arrowhead), and inferior vermian hypoplasia (arrow).
Source publication
MRI has been increasingly used for detailed visualization of the fetus in utero as well as pregnancy structures. Yet, the familiarity of radiologists and clinicians with fetal MRI is still limited. This article provides a practical approach to fetal MR imaging. Fetal MRI is an interactive scanning of the moving fetus owed to the use of fast sequenc...
Similar publications
MRI has been increasingly used for detailed visualization of the fetus in utero as well as pregnancy structures. Yet, the familiarity of radiologists and clinicians with fetal MRI is still limited. This article provides a practical approach to fetal MR imaging. Fetal MRI is an interactive scanning of the moving fetus owed to the use of fast sequenc...
Citations
... Annenin MRG öncesi 4 saatlik açlığının postprandial fetal hareketi engelleyerek hareketten kaynaklanabilecek artefaktları azaltacağı bildirilmiştir. 10 Sedasyon gerekli değildir. 7 American College of Radiology (ACR)'nin yayınladığı rapora göre MRG gebelik boyunca herhangi bir dönemde güvenli bir şekilde uygulanabilmektedir. 6 Ancak MRG'nin, anne ve fetüsü ilgilendiren önemli bilgilere diğer yöntemlerle yeterince ulaşılamadığı veya bulguların açıklanamadığı düşünülüyorsa, yapılması gerektiği de ayrıca belirtilmektedir. ...
... Uterus ve incelenecek fetal alanın boyutuna göre torso veya kardiyak faz dizilimli yüzeyel koil kullanılmalıdır. 10,11 Hasta mıknatısa yüzeyel koil ile sığmıyorsa vücut koili kullanılması önerilmektedir. 11 Hasta mümkünse supin pozisyonda çekime alınmalıdır. ...
... Ancak ileri gebelik yaşında vena kava inferiora basıyı engellemek için sol lateral dekübit pozisyonda da çekim yapılabilir. 6,10,11 Klostrofobi için ayaklar ilk girecek şekilde çekim yapılabilir. ...
Fetal manyetik rezonans görüntüleme (MRG) pozisyondan etkilenmeden fetüsün ve plasentanın değerlendirilmesini sağlayan ve prenatal tanıda ultrasonografinin tamamlayıcısı olarak kullanılan bir görüntüleme yöntemidir. Ayrıca maternal pelvik ve abdominal organlar hakkında da bilgi verebilmektedir. Hızlı görüntüleme sağlayan sekansların uygulamaya girmesiyle fetal hareketten kaynaklanan artefaktlar en aza indirgenmekte ve fetal santral sinir sistemi, toraks, addomen ve kas iskelet sistemi hakkında ek bilgiye ulaşılmaktadır. Ayrıca difuzyon ağırlıklı MRG, spektroskopi veya kalın kesit T2 ağırlıklı sekanslar gibi ileri fetal MRG yöntemlerinin uygulamaya girmesiyle fetüs hakkında daha geniş bilgiler de elde edilmesine olanak sağlayan bir radyolojik yöntem olarak MRG, prenatal tanıda yerini almıştır.
Biological tissue, pharmaceutical tablets, wood, porous rocks, catalytic reactors, concrete, and foams are examples of heterogeneous systems that may contain one or several fluid phases. Fluids in such systems carry chemical species that may participate in chemical reactions in the bulk of a fluid, as homogeneous reactions, or at the fluid/fluid or fluid/solid interfaces, as heterogeneous reactions. Magnetic resonance relaxation measures the return of ¹ H nuclear magnetization in chemical species of these fluids to an equilibrium state in a static magnetic field. Despite the perceived difference between reaction–diffusion and relaxation–diffusion in heterogeneous systems, similarities between the two are remarkable. This work draws a close parallel between magnetic resonance relaxation–diffusion and chemical reaction–diffusion for elementary unitary reaction $${\text{A}}\to {\text{B}}$$ A → B in a dilute solution—both in heterogeneous systems. A striking similarity between the dimensionless numbers that characterize their relevant behavior is observed: the Damköhler number of the second kind $${{\text{Da}}}^{{\text{II}}}$$ Da II for reaction and the Brownstein–Tarr number $${{\text{BT}}}_{i}$$ BT i for relaxation. The new vision of analogy between reaction- and magnetic resonance relaxation–diffusion in heterogeneous systems encourages the exploitation of similarities between reaction and relaxation processes to noninvasively investigate the dynamics of chemical species and reactions. One such example of importance in chemical engineering is provided for solid–fluid reaction in packed beds.
Background
Congenital mesoblastic nephroma is the most common solid renal tumor in neonates. Therefore, patients <3 months of age are advised to undergo upfront nephrectomy, whereas invasive procedures at diagnosis in patients ≥3 months of age are discouraged by the International Society of Pediatric Oncology-Renal Tumor Study Group (SIOP-RTSG). Nevertheless, discriminating congenital mesoblastic nephroma, especially from the more common Wilms tumor, solely based on imaging remains difficult. Recently, magnetic resonance imaging (MRI) has become the preferred modality. Studies focusing on MRI characteristics of congenital mesoblastic nephroma are limited.
Objective
This study aims to identify diagnostic MRI characteristics of congenital mesoblastic nephroma in the largest series of patients to date.
Materials and methods
In this retrospective multicenter study, five SIOP-RTSG national review radiologists identified 52 diagnostic MRIs of histologically proven congenital mesoblastic nephromas. MRI was performed following SIOP-RTSG protocols, while radiologists assessed their national cases using a validated case report form.
Results
Patients (24/52 classic, 11/52 cellular, and 15/52 mixed type congenital mesoblastic nephroma, 2/52 unknown) had a median age of 1 month (range 1 day–3 months). Classic type congenital mesoblastic nephroma appeared homogeneous with a lack of hemorrhage, necrosis and/or cysts, showing a concentric ring sign in 14 (58.3%) patients. Cellular and mixed type congenital mesoblastic nephroma appeared more heterogeneous and were larger (311.6 and 174.2 cm³, respectively, versus 41.0 cm³ for the classic type (P<0.001)). All cases were predominantly T2-weighted isointense and T1-weighted hypointense, and mean overall apparent diffusion coefficient values ranged from 1.05–1.10×10⁻³ mm²/s.
Conclusion
This retrospective international collaborative study showed classic type congenital mesoblastic nephroma predominantly presented as a homogeneous T2-weighted isointense mass with a typical concentric ring sign, whereas the cellular type appeared more heterogeneous. Future studies may use identified MRI characteristic of congenital mesoblastic nephroma for validation and for exploring the discriminative non-invasive value of MRI, especially from Wilms tumor.
Graphical Abstract
Intrapartum foetal hypoxia is related to long-term morbidity and mortality of the foetus and the mother. Foetal surveillance is extremely important to minimize the adverse outcomes arising from foetal hypoxia during labour. Several methods have been used in current clinical practice to monitor foetal well-being. For instance, biophysical technologies including cardiotocography, ST-analysis adjunct to cardiotocography, and Doppler ultrasound are used for intrapartum foetal monitoring. However, these technologies result in a high false-positive rate and increased obstetric interventions during labour. Alternatively, biochemical-based technologies including foetal scalp blood sampling and foetal pulse oximetry are used to identify metabolic acidosis and oxygen deprivation resulting from foetal hypoxia. These technologies neither improve clinical outcomes nor reduce unnecessary interventions during labour. Also, there is a need to link the physiological changes during foetal hypoxia to foetal monitoring technologies. The objective of this article is to assess the clinical background of foetal hypoxia and to review existing monitoring technologies for the detection and monitoring of foetal hypoxia. A comprehensive review has been made to predict foetal hypoxia using computational and machine-learning algorithms. The detection of more specific biomarkers or new sensing technologies is also reviewed which may help in the enhancement of the reliability of continuous foetal monitoring and may result in the accurate detection of intrapartum foetal hypoxia.
Neuroimaging in the pregnant patient is tailored to evaluating the neuraxis of either the patients themselves or their underlying gestation(s). Imaging indications can range from outpatient follow-up and pre-scheduled gestational anatomic surveys to emergent indications affecting either the expectant patient or fetus.
Background
Fetal brain magnetic resonance imaging (MRI)‐based gestational age prediction has been widely used to characterize normal fetal brain development and diagnose congenital brain malformations.
Purpose
The uncertainty of fetal position and external interference leads to variable localization and direction of the fetal brain. In addition, pregnant women typically concentrate on receiving MRI scans during the fetal anomaly scanning week, leading to an imbalanced distribution of fetal brain MRI data. The above‐mentioned problems pose great challenges for deep learning‐based fetal brain MRI gestational age prediction.
Methods
In this study, a pyramid squeeze attention (PSA)‐guided dynamic feature fusion CNN (PDFF‐CNN) is proposed to robustly predict gestational ages from fetal brain MRI images on an imbalanced dataset. PDFF‐CNN contains four components: transformation module, feature extraction module, dynamic feature fusion module, and balanced mean square error (MSE) loss. The transformation and feature extraction modules are employed by using the PSA to learn multiscale and multi‐orientation feature representations in a parallel weight‐sharing Siamese network. The dynamic feature fusion module automatically learns the weights of feature vectors generated in the feature extraction module to dynamically fuse multiscale and multi‐orientation brain sulci and gyri features. Considering the fact of the imbalanced dataset, the balanced MSE loss is used to mitigate the negative impact of imbalanced data distribution on gestational age prediction performance.
Results
Evaluated on an imbalanced fetal brain MRI dataset of 1327 routine clinical T2‐weighted MRI images from 157 subjects, PDFF‐CNN achieved promising gestational age prediction performance with an overall mean absolute error of 0.848 weeks and an R2$R^2$ of 0.904. Furthermore, the attention activation maps of PDFF‐CNN were derived, which revealed regional features that contributed to gestational age prediction at each gestational stage.
Conclusions
These results suggest that the proposed PDFF‐CNN might have broad clinical applicability in guiding treatment interventions and delivery planning, which has the potential to be helpful with prenatal diagnosis.
Fetal Magnetic Resonance Imaging (MRI) is challenged by fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion frequently occurs in the acquisition of spatially adjacent slices. Motion correction for each slice is thus critical for the reconstruction of 3D fetal brain MRI. In this paper, we propose a novel multi-task learning framework that adopts a coarse-to-fine strategy to jointly learn the pose estimation parameters for motion correction and tissue segmentation map of each slice in fetal MRI. Particularly, we design a regression-based segmentation loss as a deep supervision to learn anatomically more meaningful features for pose estimation and segmentation. In the coarse stage, a U-Net-like network learns the features shared for both tasks. In the refinement stage, to fully utilize the anatomical information, signed distance maps constructed from the coarse segmentation are introduced to guide the feature learning for both tasks. Finally, iterative incorporation of the signed distance maps further improves the performance of both regression and segmentation progressively. Experimental results of cross-validation across two different fetal datasets acquired with different scanners and imaging protocols demonstrate the effectiveness of the proposed method in reducing the pose estimation error and obtaining superior tissue segmentation results simultaneously, compared with state-of-the-art methods.
Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where large and unpredictable fetal motion can lead to substantial artifacts in the acquired images. Existing methods for fetal brain quality assessment operate at the slice level, and fail to get a comprehensive picture of the quality of an image, that can only be achieved by looking at the entire brain volume. In this work, we propose FetMRQC, a machine learning framework for automated image quality assessment tailored to fetal brain MRI, which extracts an ensemble of quality metrics that are then used to predict experts’ ratings. Based on the manual ratings of more than 1000 low-resolution stacks acquired across two different institutions, we show that, compared with existing quality metrics, FetMRQC is able to generalize out-of-domain, while being interpretable and data efficient. We also release a novel manual quality rating tool designed to facilitate and optimize quality rating of fetal brain images.
Our tool, along with all the code to generate, train and evaluate the model is available at https://github.com/Medical-Image-Analysis-Laboratory/fetal_brain_qc/.
Tuning the regularization hyperparameter \(\alpha \) in inverse problems has been a longstanding problem. This is particularly true in the case of fetal brain magnetic resonance imaging, where an isotropic high-resolution volume is reconstructed from motion-corrupted low-resolution series of two-dimensional thick slices. Indeed, the lack of ground truth images makes challenging the adaptation of \(\alpha \) to a given setting of interest in a quantitative manner. In this work, we propose a simulation-based approach to tune \(\alpha \) for a given acquisition setting. We focus on the influence of the magnetic field strength and availability of input low-resolution images on the ill-posedness of the problem. Our results show that the optimal \(\alpha \), chosen as the one maximizing the similarity with the simulated reference image, significantly improves the super-resolution reconstruction accuracy compared to the generally adopted default regularization values, independently of the selected reconstruction pipeline. Qualitative validation on clinical data confirms the importance of tuning this parameter to the targeted clinical image setting. The simulated data and their reconstructions are available at https://zenodo.org/record/8123677.
Magnetic resonance imaging is being increasingly used to diagnose and follow up a variety of medical conditions in pregnancy, both for maternal and fetal indications. However, limited data regarding its safe use in pregnancy may be a source of anxiety and avoidance for both patients and their healthcare providers. In this review, we critically discuss the main safety concerns of Magnetic Resonance Imaging (MRI) in pregnancy including energy deposition, acoustic noise, and use of contrast agents, supported by data from animal and human studies. Use of maternal sedatives and concerns related to occupational exposure in pregnant personnel are also addressed. Exposure to gadolinium-based contrast agents and sedation for MRI during pregnancy should be avoided whenever feasible.
