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US abdominal circumference (AC) plane. 

US abdominal circumference (AC) plane. 

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Objective: To compare the intra and interobserver variability of ultrasound and magnetic resonance imaging in the assessment of common fetal biometry and estimated fetal weight in the second trimester. Methods: Retrospective measurements on preselected image planes were performed independently by two pairs of observers for contemporaneous ultras...

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... Another explanation for the differences found between 1.5 T and 3.0 T measurements can be found in intra-and interobserver variability which was not assessed in the current study. In previous studies, it was found that measurement variabilities can reach up to a magnitude of 5% in some biometric parameters [15,16]. ...
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Purpose Abnormal fetal brain measurements might affect clinical management and parental counseling. The effect of between-field-strength differences was not evaluated in quantitative fetal brain imaging until now. Our study aimed to compare fetal brain biometry measurements in 3.0 T with 1.5 T scanners. Methods A retrospective cohort of 1150 low-risk fetuses scanned between 2012 and 2021, with apparently normal brain anatomy, were retrospectively evaluated for biometric measurements. The cohort included 1.5 T (442 fetuses) and 3.0 T scans (708 fetuses) of populations with comparable characteristics in the same tertiary medical center. Manually measured biometry included bi-parietal, fronto-occipital and trans-cerebellar diameters, length of the corpus-callosum, vermis height, and width. Measurements were then converted to centiles based on previously reported biometric reference charts. The 1.5 T centiles were compared with the 3.0 T centiles. Results No significant differences between centiles of bi-parietal diameter, trans-cerebellar diameter, or length of the corpus callosum between 1.5 T and 3.0 T scanners were found. Small absolute differences were found in the vermis height, with higher centiles in the 3.0 T, compared to the 1.5 T scanner (54.6th-centile, vs. 39.0th-centile, p < 0.001); less significant differences were found in vermis width centiles (46.9th-centile vs. 37.5th-centile, p = 0.03). Fronto-occipital diameter was higher in 1.5 T than in the 3.0 T scanner (66.0th-centile vs. 61.8th-centile, p = 0.02). Conclusions The increasing use of 3.0 T MRI for fetal imaging poses a potential bias when using 1.5 T-based charts. We elucidate those biometric measurements are comparable, with relatively small between-field-strength differences, when using manual biometric measurements. Small inter-magnet differences can be related to higher spatial resolution with 3 T scanners and may be substantial when evaluating small brain structures, such as the vermis.
... Several medical images exist, depending on the modality used in their obtainment. The list includes X-ray [1]- [3], computed tomography (CT) scan [4], [5], magnetic resonance imaging (MRI) [5], ultrasound [6] and positron emission tomography (PET) [7]. Nowadays, medical images are playing important roles in disease identification, critical surgery operations, pregnancy complication monitoring, early diagnosis, and severe disease screening [2]. ...
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Owing to methods of acquisition, medical images often require enhancement for them to serve the intended purpose of computer-aided diagnosis. Most medical image enhancement techniques are application specific, leading to the introduction of different enhancement methods for different medical images. In addition, the execution time of most of the previous enhancement methods is longer than necessary. Hence, there is a need for a method that produces fast and satisfactory results when deployed for the enhancement of several medical images. This paper proposes a tri-modal technique, involving a hybrid combination of unsharp masking, logarithmic transformation, and histogram equalization approaches, for medical image enhancement. Three classes of medical images: X-ray, magnetic resonance, and computer tomographic images are used for the evaluations of the proposed tri-modal method, where absolute mean brightness error, peak signal-to-noise ratio, and entropy are utilized as performance metrics. Both qualitative and quantitative evaluations reveal that the proposed tri-modal method performed better than the four previous methods in the literature for the three classes of medical images used in the evaluation. Also, the execution time of the tri-modal technique compares well with those of mono-mode methods. Thus, the tri-modal technique produces better enhanced medical images from different medical image inputs.
... An image database containing anonymised US DICOM images was compiled using the Osirix image review software for offline measurement (version 7.5, Geneva, Switzerland). US databases were duplicated and randomised using a computer-generated randomiser before being reviewed offline for inter-and intraobserver variability by the 2 fetal imaging experts, blinded to previous imaging results and clinical history (including GA) as previously described [27]. Both US-observers used the first US database to independently measure 2D-US fetal biometry for interobserver measurements, and then USobserver 1 repeated the measures after a 6-week interval to generate intraobserver measurements. ...
... Statistical analysis was performed as per recommended guidelines to avoid study reporting variation [29][30][31][32][33]. For the primary aim, a power calculation determined that a sample size of 20 was required to give a power of 80% for a type 1 error of 5% to detect an effect size of 13.0 g difference (assuming a standard deviation of 104 g based on previous studies) [27,34]. ...
... A recent literature search revealed no studies focusing on volumetric MRI-EFW for mid-second trimester fetuses using either the Baker or the Kacem formula and then comparing the results to US and/or birthweight. However, a few studies have looked at 2D-MRI biometry to estimate fetal weight in the second trimester with limited success [27,37]. Our results contrast with recent findings by Kadji et al. [10] who assessed observer variability in EFW calculation for MRI and US in full-term fetuses. ...
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Objectives: The aim of this study was to compare the standard ultrasound (US) estimated fetal weight (EFW) and MRI volume-derived methods for the midtrimester fetus. Methods: Twenty-five paired US and MRI scans had the EFW calculated (gestational age [GA] range = 20-26 weeks). The intra- and interobserver variability of each method was assessed (2 operators/modality). A small sub-analysis was performed on 5 fetuses who were delivered preterm (mean GA 29 +3 weeks) and compared to the actual birthweight. Results: Two MRI volumetry EFW formulae under-measured compared to US by -10.9% and -14.5% in the midpregnancy fetus (p < 0.001) but had excellent intra- and interobserver agreement (intraclass correlation coefficient = 0.998 and 0.993). In the preterm fetus, the mean relative difference (MRD) between the MRI volume-derived EFW (MRI-EFW) and actual expected birthweight (at the scan GA) was -13.7% (-159.0 g, 95% CI: -341.7 to 23.7 g) and -17.1% (-204.6 g, 95% CI: -380.4 to -28.8 g), for the 2 MRI formulae. The MRD was smaller for US at 5.3% (69.8 g, 95% CI: -34.3 to 173.9). Conclusions: MRI-EFW results should be interpreted with caution in midpregnancy. Despite excellent observer agreement with MRI volumetry, refinement of the EFW formula is needed in the second trimester, for the small and for the GA and preterm fetus to compensate for lower fetal densities.
... 7 In a study comparing US and MRI in the assessment of fetal biometry and weight it was shown that both techniques are operator dependent and are subject to random error. 8 However, inter-method, inter-and intraobserver variability comparing simultaneous measurements from combined MRI and US imaging have not been previously assessed. ...
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Objectives To assess synchronisation of MRI and US in measuring foetus phantom head structures; inter-method, intra- and inter-observer differences on biparietal diameter (BPD), head diameter, anterio-posterior head diameter (HAP) and lateral ventricle structures (VS). Methods Fusion Imaging (FI) has been performed by combining MRI and US simultaneously. Axial scans of 1.5 Tesla MRI on a foetus phantom were acquired and uploaded on a US machine (EPIQ 7G, Philips). A PercuNav US tracker allowed the system to recognise and display the position of the transducer. A fetal phantom tracker was used as a phantom reference. Real-time US of the phantom head was performed by synchronising the uploaded MRI images using different landmarks. Synchronisation has been assessed by taking measurements after rotating the US probe by 90. Measurements were taken by three different observers twice. Differences in measurements between MRI and US, inter-, intra-observer differences in all measurements were assessed. Results BPD, HAP and VS measurements before rotation were 0.13 ± 0.06 cm, 0.46 ± 0.09 cm and 0.4 ± 0.23 cm (width) and mean 0.6 ± 0.25 cm (length) larger at MRI than at US using any number of landmarks. After US probe rotation VS were 0.3 ± 0.24 cm in width and 0.3 ± 0.27 cm in length. Intra- and inter-observer differences in all measurements were small. Conclusions FI showed good synchronisation in measurements. BPD, HAP and VS were larger at MRI than US, likely a result of the way images are generated. Intra-, inter-observer differences between measurements were small. This can be important when reporting geometric measures from FI.
... An obvious possibility would be to change the "risk of SGA" definition to EFW us ≤ −12% on the expense of a doubled FPR. Furthermore 3-dimensional ultrasound 23 and magnetic resonance imaging24,25 for better estimates of EFW could be considered. Introduction of routine EFW us ("universal ultrasound screening") has been shown to increase the sensitivity from 29%-33% to 42%-80%, but at the expense of increased FPR from 0.26%-3% to 5%-13% in previous studies.7,14,26,27 ...
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Introduction: It is well established that correct antenatal identification of small-for-gestational-age (SGA) fetuses reduces their risk of adverse perinatal outcome with long-term consequences. Ultrasound estimates of fetal weight (EFWus ) is the ultimate tool for this identification. It can be conducted as a "universal screening", ie all pregnant women at a specific gestational age. However, in Denmark it is conducted as "selective screening", ie only on clinical indication. The aim of this study was to assess the performance of the Danish national SGA screening program and the consequences of false positive and false negative SGA cases. Material and methods: In this retrospective cohort study, we included 2928 women with singleton pregnancies with due date in 2015. We defined "risk of SGA" by an EFWus ≤ -15% of expected for the gestational age and "SGA" as birthweight ≤ -22% of expected for gestational age. Results: At birth, the prevalence of SGA was 3.3%. The overall sensitivity of the Danish screening program was 62% at a false-positive rate of 5.6%. Within the entire cohort, 63% had an EFWus as compared to 79% of the SGA-cases. The sensitivity was 79% for those born before 37 weeks' gestation but only 40% for those born after 40 weeks' gestation. The sensitivity was also associated to birthweight deviation; 73% among extreme SGA cases (birthweight deviation ≤ -33%) and 55% among mild SGA (birthweight deviation between -22% and 27%). False diagnosis of SGA was associated with an increased rate of induction of labor (ORadj = 2.51, 95% CI; 1.70 to 3.71) and cesarean section (ORadj = 1.44, 95% CI; 0.96 to 2.18). Conclusions: The performance of the Danish national screening program for SGA based on selective EFWus on clinical indication have improved considerably over the last 20 years. Limitations of the program are the large proportion of women referred to ultrasound scan and the low performance post-term.
Article
Objectives To evaluate a deep learning model for predicting gestational age from fetal brain MRI acquired after the first trimester in comparison to biparietal diameter (BPD).Materials and methodsOur Institutional Review Board approved this retrospective study, and a total of 184 T2-weighted MRI acquisitions from 184 fetuses (mean gestational age: 29.4 weeks) who underwent MRI between January 2014 and June 2019 were included. The reference standard gestational age was based on the last menstruation and ultrasonography measurements in the first trimester. The deep learning model was trained with T2-weighted images from 126 training cases and 29 validation cases. The remaining 29 cases were used as test data, with fetal age estimated by both the model and BPD measurement. The relationship between the estimated gestational age and the reference standard was evaluated with Lin’s concordance correlation coefficient (ρc) and a Bland-Altman plot. The ρc was assessed with McBride’s definition.ResultsThe ρc of the model prediction was substantial (ρc = 0.964), but the ρc of the BPD prediction was moderate (ρc = 0.920). Both the model and BPD predictions had greater differences from the reference standard at increasing gestational age. However, the upper limit of the model’s prediction (2.45 weeks) was significantly shorter than that of BPD (5.62 weeks).Conclusions Deep learning can accurately predict gestational age from fetal brain MR acquired after the first trimester.Key Points • The prediction of gestational age using ultrasound is accurate in the first trimester but becomes inaccurate as gestational age increases. • Deep learning can accurately predict gestational age from fetal brain MRI acquired in the second and third trimester. • Prediction of gestational age by deep learning may have benefits for prenatal care in pregnancies that are underserved during the first trimester.