Interaction between clinicians and artificial intelligence to detect fetal atrioventricular septal defects on ultrasound: how can we optimize collaborative performance?

OBJECTIVES: Artificial intelligence (AI) has shown promise in improving the performance of fetal ultrasound screening in detecting congenital heart disease (CHD). The effect of giving AI advice to human operators has not been studied in this context. Giving additional information about AI model workings, such as confidence scores for AI predictions, may be a way of further improving performance. Our aims were to investigate whether AI advice improved overall diagnostic accuracy (using a single CHD lesion as an exemplar), and to determine what, if any, additional information given to clinicians optimized the overall performance of the clinician-AI team. METHODS: An AI model was trained to classify a single fetal CHD lesion (atrioventricular septal defect (AVSD)), using a retrospective cohort of 121 130 cardiac four-chamber images extracted from 173 ultrasound scan videos (98 with normal hearts, 75 with AVSD); a ResNet50 model architecture was used. Temperature scaling of model prediction probability was performed on a validation set, and gradient-weighted class activation maps (grad-CAMs) produced. Ten clinicians (two consultant fetal cardiologists, three trainees in pediatric cardiology and five fetal cardiac sonographers) were recruited from a center of fetal cardiology to participate. Each participant was shown 2000 fetal four-chamber images in a random order (1000 normal and 1000 AVSD). The dataset comprised 500 images, each shown in four conditions: (1) image alone without AI output; (2) image with binary AI classification; (3) image with AI model confidence; and (4) image with grad-CAM image overlays. The clinicians were asked to classify each image as normal or AVSD. RESULTS: A total of 20 000 image classifications were recorded from 10 clinicians. The AI model alone achieved an accuracy of 0.798 (95% CI, 0.760-0.832), a sensitivity of 0.868 (95% CI, 0.834-0.902) and a specificity of 0.728 (95% CI, 0.702-0.754), and the clinicians without AI achieved an accuracy of 0.844 (95% CI, 0.834-0.854), a sensitivity of 0.827 (95% CI, 0.795-0.858) and a specificity of 0.861 (95% CI, 0.828-0.895). Showing a binary (normal or AVSD) AI model output resulted in significant improvement in accuracy to 0.865 (P < 0.001). This effect was seen in both experienced and less-experienced participants. Giving incorrect AI advice resulted in a significant deterioration in overall accuracy, from 0.761 to 0.693 (P < 0.001), which was driven by an increase in both Type-I and Type-II errors by the clinicians. This effect was worsened by showing model confidence (accuracy, 0.649; P < 0.001) or grad-CAM (accuracy, 0.644; P < 0.001). CONCLUSIONS: AI has the potential to improve performance when used in collaboration with clinicians, even if the model performance does not reach expert level. Giving additional information about model workings such as model confidence and class activation map image overlays did not improve overall performance, and actually worsened performance for images for which the AI model was incorrect. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Respiration resolved imaging with continuous stable state 2D acquisition using linear frequency SWEEP.

PURPOSE: To investigate the potential of continuous radiofrequency (RF) shifting (SWEEP) as a technique for creating densely sampled data while maintaining a stable signal state for dynamic imaging. METHODS: We present a method where a continuous stable state of magnetization is swept smoothly across the anatomy of interest, creating an efficient approach to dense multiple 2D slice imaging. This is achieved by introducing a linear frequency offset to successive RF pulses shifting the excited slice by a fraction of the slice thickness with each successive repeat times (TR). Simulations and in vivo imaging were performed to assess how this affects the measured signal. Free breathing, respiration resolved 4D volumes in fetal/placental imaging is explored as potential application of this method. RESULTS: The SWEEP method maintained a stable signal state over a full acquisition reducing artifacts from unstable magnetization. Simulations demonstrated that the effects of SWEEP on slice profiles was of the same order as that produced by physiological motion observed with conventional methods. Respiration resolved 4D data acquired with this method shows reduced respiration artifacts and resilience to non-rigid and non-cyclic motion. CONCLUSIONS: The SWEEP method is presented as a technique for improved acquisition efficiency of densely sampled short-TR 2D sequences. Using conventional slice excitation the number of RF pulses required to enter a true steady state is excessively high when using short-TR 2D acquisitions, SWEEP circumvents this limitation by creating a stable signal state that is preserved between slices.

Machine-learning to characterise neonatal functional connectivity in the preterm brain.

Brain development is adversely affected by preterm birth. Magnetic resonance image analysis has revealed a complex fusion of structural alterations across all tissue compartments that are apparent by term-equivalent age, persistent into adolescence and adulthood, and associated with wide-ranging neurodevelopment disorders. Although functional MRI has revealed the relatively advanced organisational state of the neonatal brain, the full extent and nature of functional disruptions following preterm birth remain unclear. In this study, we apply machine-learning methods to compare whole-brain functional connectivity in preterm infants at term-equivalent age and healthy term-born neonates in order to test the hypothesis that preterm birth results in specific alterations to functional connectivity by term-equivalent age. Functional connectivity networks were estimated in 105 preterm infants and 26 term controls using group-independent component analysis and a graphical lasso model. A random forest-based feature selection method was used to identify discriminative edges within each network and a nonlinear support vector machine was used to classify subjects based on functional connectivity alone. We achieved 80% cross-validated classification accuracy informed by a small set of discriminative edges. These edges connected a number of functional nodes in subcortical and cortical grey matter, and most were stronger in term neonates compared to those born preterm. Half of the discriminative edges connected one or more nodes within the basal ganglia. These results demonstrate that functional connectivity in the preterm brain is significantly altered by term-equivalent age, confirming previous reports of altered connectivity between subcortical structures and higher-level association cortex following preterm birth.

Construction of a fetal spatio-temporal cortical surface atlas from in utero MRI: Application of spectral surface matching.

In this study, we construct a spatio-temporal surface atlas of the developing cerebral cortex, which is an important tool for analysing and understanding normal and abnormal cortical development. In utero Magnetic Resonance Imaging (MRI) of 80 healthy fetuses was performed, with a gestational age range of 21.7 to 38.9 weeks. Topologically correct cortical surface models were extracted from reconstructed 3D MRI volumes. Accurate correspondences were obtained by applying a joint spectral analysis to cortices for sets of subjects close to a specific age. Sulcal alignment was found to be accurate in comparison to spherical demons, a state of the art registration technique for aligning 2D cortical representations (average Fréchet distance≈0.4 mm at 30 weeks). We construct consistent, unbiased average cortical surface templates, for each week of gestation, from age-matched groups of surfaces by applying kernel regression in the spectral domain. These were found to accurately capture the average cortical shape of individuals within the cohort, suggesting a good alignment of cortical geometry. Each spectral embedding and its corresponding cortical surface template provide a dual reference space where cortical geometry is aligned and a vertex-wise morphometric analysis can be undertaken.

Whole-brain mapping of structural connectivity in infants reveals altered connection strength associated with growth and preterm birth.

Cerebral white-matter injury is common in preterm-born infants and is associated with neurocognitive impairments. Identifying the pattern of connectivity changes in the brain following premature birth may provide a more comprehensive understanding of the neurobiology underlying these impairments. Here, we characterize whole-brain, macrostructural connectivity following preterm delivery and explore the influence of age and prematurity using a data-driven, nonsubjective analysis of diffusion magnetic resonance imaging data. T1- and T2-weighted and -diffusion MRI were obtained between 11 and 31 months postconceptional age in 49 infants, born between 25 and 35 weeks postconception. An optimized processing pipeline, combining anatomical, and tissue segmentations with probabilistic diffusion tractography, was used to map mean tract anisotropy. White-matter tracts where connection strength was related to age of delivery or imaging were identified using sparse-penalized regression and stability selection. Older children had stronger connections in tracts predominantly involving frontal lobe structures. Increasing prematurity at birth was related to widespread reductions in connection strength in tracts involving all cortical lobes and several subcortical structures. This nonsubjective approach to mapping whole-brain connectivity detected hypothesized changes in the strength of intracerebral connections during development and widespread reductions in connectivity strength associated with premature birth.

Automatic quantification of normal cortical folding patterns from fetal brain MRI.

We automatically quantify patterns of normal cortical folding in the developing fetus from in utero MR images (N=80) over a wide gestational age (GA) range (21.7 to 38.9weeks). This work on data from healthy subjects represents a first step towards characterising abnormal folding that may be related to pathology, facilitating earlier diagnosis and intervention. The cortical boundary was delineated by automatically segmenting the brain MR image into a number of key structures. This utilised a spatio-temporal atlas as tissue priors in an expectation-maximization approach with second order Markov random field (MRF) regularization to improve the accuracy of the cortical boundary estimate. An implicit high resolution surface was then used to compute cortical folding measures. We validated the automated segmentations with manual delineations and the average surface discrepancy was of the order of 1mm. Eight curvature-based folding measures were computed for each fetal cortex and used to give summary shape descriptors. These were strongly correlated with GA (R(2)=0.99) confirming the close link between neurological development and cortical convolution. This allowed an age-dependent non-linear model to be accurately fitted to the folding measures. The model supports visual observations that, after a slow initial start, cortical folding increases rapidly between 25 and 30weeks and subsequently slows near birth. The model allows the accurate prediction of fetal age from an observed folding measure with a smaller error where growth is fastest. We also analysed regional patterns in folding by parcellating each fetal cortex using a nine-region anatomical atlas and found that Gompertz models fitted the change in lobar regions. Regional differences in growth rate were detected, with the parietal and posterior temporal lobes exhibiting the fastest growth, while the cingulate, frontal and medial temporal lobes developed more slowly.

Automated fetal brain segmentation from 2D MRI slices for motion correction.

Motion correction is a key element for imaging the fetal brain in-utero using Magnetic Resonance Imaging (MRI). Maternal breathing can introduce motion, but a larger effect is frequently due to fetal movement within the womb. Consequently, imaging is frequently performed slice-by-slice using single shot techniques, which are then combined into volumetric images using slice-to-volume reconstruction methods (SVR). For successful SVR, a key preprocessing step is to isolate fetal brain tissues from maternal anatomy before correcting for the motion of the fetal head. This has hitherto been a manual or semi-automatic procedure. We propose an automatic method to localize and segment the brain of the fetus when the image data is acquired as stacks of 2D slices with anatomy misaligned due to fetal motion. We combine this segmentation process with a robust motion correction method, enabling the segmentation to be refined as the reconstruction proceeds. The fetal brain localization process uses Maximally Stable Extremal Regions (MSER), which are classified using a Bag-of-Words model with Scale-Invariant Feature Transform (SIFT) features. The segmentation process is a patch-based propagation of the MSER regions selected during detection, combined with a Conditional Random Field (CRF). The gestational age (GA) is used to incorporate prior knowledge about the size and volume of the fetal brain into the detection and segmentation process. The method was tested in a ten-fold cross-validation experiment on 66 datasets of healthy fetuses whose GA ranged from 22 to 39 weeks. In 85% of the tested cases, our proposed method produced a motion corrected volume of a relevant quality for clinical diagnosis, thus removing the need for manually delineating the contours of the brain before motion correction. Our method automatically generated as a side-product a segmentation of the reconstructed fetal brain with a mean Dice score of 93%, which can be used for further processing.

Molecular signatures of cortical expansion in the human fetal brain.

The third trimester of human gestation is characterised by rapid increases in brain volume and cortical surface area. A growing catalogue of cells in the prenatal brain has revealed remarkable molecular diversity across cortical areas.1,2 Despite this, little is known about how this translates into the patterns of differential cortical expansion observed in humans during the latter stages of gestation. Here we present a new resource, µBrain, to facilitate knowledge translation between molecular and anatomical descriptions of the prenatal developing brain. Built using generative artificial intelligence, µBrain is a three-dimensional cellular-resolution digital atlas combining publicly-available serial sections of the postmortem human brain at 21 weeks gestation3 with bulk tissue microarray data, sampled across 29 cortical regions and 5 transient tissue zones.4 Using µBrain, we evaluate the molecular signatures of preferentially-expanded cortical regions during human gestation, quantified in utero using magnetic resonance imaging (MRI). We find that differences in the rates of expansion across cortical areas during gestation respect anatomical and evolutionary boundaries between cortical types5 and are founded upon extended periods of upper-layer cortical neuron migration that continue beyond mid-gestation. We identify a set of genes that are upregulated from mid-gestation and highly expressed in rapidly expanding neocortex, which are implicated in genetic disorders with cognitive sequelae. Our findings demonstrate a spatial coupling between areal differences in the timing of neurogenesis and rates of expansion across the neocortical sheet during the prenatal epoch. The µBrain atlas is available from: https://garedaba.github.io/micro-brain/ and provides a new tool to comprehensively map early brain development across domains, model systems and resolution scales.

A Constant-Force End-Effector With Online Force Adjustment for Robotic Ultrasonography.

In this letter, we propose a novel constant-force end-effector (CFEE) to address current limitations in robotic ultrasonography. The CFEE uses a parallel, motor-spring-based solution to precisely generate constant operating forces over a wide range and enable the ultrasound (US) probe to adapt to the abdominal contours autonomously. A displacement measurement unit was developed to realize the acquisition of probe position and precise control of the operating force. Moreover, the operating force can be adjusted online to maintain safety and continuity of operation. Simulations and experiments were carried out to evaluate the performance. Results show that the proposed CFEE can provide constant forces of 4-12 N with displacements of 0-8 mm. The maximum relative error of force generation is 8.28%, and the accuracy and precision for displacement measurement are 0.29 mm and ±0.16 mm, respectively. Various operating forces can be adjusted online during the same operation. Ultrasound images acquired by the proposed CFEE are of equally good quality compared to a manual sonographer scan. The proposed CFEE would have potential further medical applications.

Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: A clinical evaluation and quality assessment comparison.

INTRODUCTION: Clinical evaluation of deep learning (DL) tools is essential to compliment technical accuracy metrics. This study assessed the image quality of standard fetal head planes automatically-extracted from three-dimensional (3D) ultrasound fetal head volumes using a customised DL-algorithm. METHODS: Two observers retrospectively reviewed standard fetal head planes against pre-defined image quality criteria. Forty-eight images (29 transventricular, 19 transcerebellar) were selected from 91 transabdominal fetal scans (mean gestational age = 26 completed weeks, range = 20+5-32+3 weeks). Each had two-dimensional (2D) manually-acquired (2D-MA), 3D operator-selected (3D-OS) and 3D-DL automatically-acquired (3D-DL) images. The proportion of adequate images from each plane and modality, and the number of inadequate images per plane was compared for each method. Inter and intra-observer agreement of overall image quality was calculated. RESULTS: Sixty-seven percent of 3D-OS and 3D-DL transventricular planes were adequate quality. Forty-five percent of 3D-OS and 55% of 3D-DL transcerebellar planes were adequate. Seventy-one percent of 3D-OS and 86% of 3D-DL transventricular planes failed with poor visualisation of intra-cranial structures. Eighty-six percent of 3D-OS and 80% of 3D-DL transcerebellar planes failed due to inadequate visualisation of cerebellar hemispheres. Image quality was significantly different between 2D and 3D, however, no significant difference between 3D-modalities was demonstrated (p < 0.005). Inter-observer agreement of transventricular plane adequacy was moderate for both 3D-modalities, and weak for transcerebellar planes. CONCLUSION: The 3D-DL algorithm can automatically extract standard fetal head planes from 3D-head volumes of comparable quality to operator-selected planes. Image quality in 3D is inferior to corresponding 2D planes, likely due to limitations with 3D-technology and acquisition technique. IMPLICATIONS FOR PRACTICE: Automated image extraction of standard planes from US-volumes could facilitate use of 3DUS in clinical practice, however image quality is dependent on the volume acquisition technique.

Evaluation of DISORDER: Retrospective Image Motion Correction for Volumetric Brain MRI in a Pediatric Setting.

BACKGROUND AND PURPOSE: Head motion causes image degradation in brain MR imaging examinations, negatively impacting image quality, especially in pediatric populations. Here, we used a retrospective motion correction technique in children and assessed image quality improvement for 3D MR imaging acquisitions. MATERIALS AND METHODS: We prospectively acquired brain MR imaging at 3T using 3D sequences, T1-weighted MPRAGE, T2-weighted TSE, and FLAIR in 32 unsedated children, including 7 with epilepsy (age range, 2-18 years). We implemented a novel motion correction technique through a modification of k-space data acquisition: Distributed and Incoherent Sample Orders for Reconstruction Deblurring by using Encoding Redundancy (DISORDER). For each participant and technique, we obtained 3 reconstructions as acquired (Aq), after DISORDER motion correction (Di), and Di with additional outlier rejection (DiOut). We analyzed 288 images quantitatively, measuring 2 objective no-reference image quality metrics: gradient entropy (GE) and MPRAGE white matter (WM) homogeneity. As a qualitative metric, we presented blinded and randomized images to 2 expert neuroradiologists who scored them for clinical readability. RESULTS: Both image quality metrics improved after motion correction for all modalities, and improvement correlated with the amount of intrascan motion. Neuroradiologists also considered the motion corrected images as of higher quality (Wilcoxon z = -3.164 for MPRAGE; z = -2.066 for TSE; z = -2.645 for FLAIR; all P < .05). CONCLUSIONS: Retrospective image motion correction with DISORDER increased image quality both from an objective and qualitative perspective. In 75% of sessions, at least 1 sequence was improved by this approach, indicating the benefit of this technique in unsedated children for both clinical and research environments.

MRI Findings at Term-Corrected Age and Neurodevelopmental Outcomes in a Large Cohort of Very Preterm Infants.

BACKGROUND AND PURPOSE: Brain MR imaging at term-equivalent age is a useful tool to define brain injury in preterm infants. We report pragmatic clinical radiological assessment of images from a large unselected cohort of preterm infants imaged at term and document the spectrum and frequency of acquired brain lesions and their relation to outcomes at 20 months. MATERIALS AND METHODS: Infants born at <33 weeks' gestation were recruited from South and North West London neonatal units and imaged in a single center at 3T at term-equivalent age. At 20 months' corrected age, they were invited for neurodevelopmental assessment. The frequency of acquired brain lesions and the sensitivity, specificity, and negative and positive predictive values for motor, cognitive, and language outcomes were calculated, and corpus callosal thinning and ventricular dilation were qualitatively assessed. RESULTS: Five hundred four infants underwent 3T MR imaging at term-equivalent age; 477 attended for assessment. Seventy-six percent of infants had acquired lesions, which included periventricular leukomalacia, hemorrhagic parenchymal infarction, germinal matrix-intraventricular hemorrhage, punctate white matter lesions, cerebellar hemorrhage, and subependymal cysts. All infants with periventricular leukomalacia, and 60% of those with hemorrhagic parenchymal infarction had abnormal motor outcomes. Routine 3T MR imaging of the brain at term-equivalent age in an unselected preterm population that demonstrates no focal lesion is 45% sensitive and 61% specific for normal neurodevelopment at 20 months and 17% sensitive and 94% specific for a normal motor outcome. CONCLUSIONS: Acquired brain lesions are common in preterm infants routinely imaged at term-equivalent age, but not all predict an adverse neurodevelopmental outcome.

Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy.

Quantitative research in neuroimaging often relies on anatomical segmentation of human brain MR images. Recent multi-atlas based approaches provide highly accurate structural segmentations of the brain by propagating manual delineations from multiple atlases in a database to a query subject and combining them. The atlas databases which can be used for these purposes are growing steadily. We present a framework to address the consequent problems of scale in multi-atlas segmentation. We show that selecting a custom subset of atlases for each query subject provides more accurate subcortical segmentations than those given by non-selective combination of random atlas subsets. Using a database of 275 atlases, we tested an image-based similarity criterion as well as a demographic criterion (age) in a leave-one-out cross-validation study. Using a custom ranking of the database for each subject, we combined a varying number n of atlases from the top of the ranked list. The resulting segmentations were compared with manual reference segmentations using Dice overlap. Image-based selection provided better segmentations than random subsets (mean Dice overlap 0.854 vs. 0.811 for the estimated optimal subset size, n=20). Age-based selection resulted in a similar marked improvement. We conclude that selecting atlases from large databases for atlas-based brain image segmentation improves the accuracy of the segmentations achieved. We show that image similarity is a suitable selection criterion and give results based on selecting atlases by age that demonstrate the value of meta-information for selection.

Microstructured magnetic materials for RF flux guides in magnetic resonance imaging.

Magnetic resonance imaging and spectroscopy systems use coils, either singly or as arrays, to intercept radio-frequency (RF) magnetic flux from regions of interest, often deep within the body. Here, we show that a new magnetic material offers novel possibilities for guiding RF flux to the receiver coil, permitting a clear image to be obtained where none might otherwise be detectable. The new material contains microstructure designed according to concepts taken from the field of photonic band gap materials. In the RF range, it has a magnetic permeability that can be produced to specification while exhibiting negligible direct-current magnetism. The latter property is vital to avoid perturbing the static and audio-frequency magnetic fields needed to obtain image and spectral data. The concept offers a new paradigm for the manipulation of RF flux in all nuclear magnetic resonance systems.

Chiral Swiss rolls show a negative refractive index.

Chiral Swiss rolls, consisting of a metal/dielectric laminate tape helically wound on an insulating mandrel, have been developed to form the basis of a highly chiral metamaterial. We have fabricated these elements using a custom-built machine, and have characterized them. We find that the permeability, permittivity, and chirality are all resonant in the region of 80 MHz. The chirality is so strong that it can be directly measured by observing the magnetic response to an applied electric field, and is larger than either the permeability or the permittivity. We have estimated the refractive indices from these data, and find both strong circular dichroism and a wide frequency range where the refractive index is negative.

White matter brain lesions in midlife familial hypercholesterolemic patients at 3-Tesla magnetic resonance imaging.

BACKGROUND: Patients with hypercholesterolemia of 60 years and older have an increased risk for white matter brain lesions and dementia. PURPOSE: To investigate whether patients with familial hypercholesterolemia (FH) develop white matter lesions at 3-Tesla (T) MRI as early as in midlife. MATERIAL AND METHODS: Non-diabetic, nonsmoking, and non-hypertensive heterozygous FH patients on treatment with maximally tolerated dose of a statin for more than 5 years (n = 14) and matched controls (n = 22) aged 25 to 60 years of age were studied. Imaging was performed at 3T with a fluid-attenuated T2-weighted MR pulse sequence and a T1-weighted spin-echo pulse sequence following 10 ml of i.v. gadopentetate dimeglumine. Images were evaluated by two independent readers. Fasting blood samples were taken. Student's t test was employed at P<0.05. RESULTS: Three volunteers and one FH patient had white matter lesions (P<0.53). No other evidence of past ischemic stroke was observed. Mean total serum cholesterol and low-density lipoprotein (LDL) cholesterol were significantly higher in the FH group (6.0+/-1.1 vs. 5.1+/-0.9 mmol/l, P<0.02 and 4.1+/-0.9 vs. 3.1+/-0.8 mmol/l, P<0.004, respectively). CONCLUSION: Heterozygous FH patients on statin treatment in the age range of 25 to 60 years are not at increased risk of white matter lesions at 3T MRI.

Neurodevelopmental Correlates of Fetal Motor Behavior Assessed Using Cine MR Imaging.

BACKGROUND AND PURPOSE: Fetal motor behavior is widely used as a clinical indicator for healthy development; however, our understanding of its potential as a marker for neurologic integrity is underdeveloped. MR imaging allows complete views of the whole fetus, which, combined with brain imaging, may improve the characterization of this relationship. This study aimed to combine an analysis of fetal motor behavior, brain MR imaging, and postnatal outcome, to provide insight into neurodevelopmental correlates of motor behavior. MATERIALS AND METHODS: Cine MR imaging was used to acquire sequences of fetal motor behavior in subjects with normal and abnormal findings on conventional brain MR imaging between 18 weeks' gestation and term. General movement sequences were analyzed using established criteria. Brain MR imaging was reported by an expert fetal neuroradiologist. Subjects were followed for up to 4 years postnatally with standard postnatal assessments. RESULTS: Nineteen of 21 fetuses with normal brain MR imaging findings showed normal general movements, compared with 14 of 22 of the fetuses with abnormal brain MR imaging findings, which, when classified by severity of the malformation, showed a significant relationship with postnatal outcome (P = .021). There was a significant relationship among neurodevelopmental outcome, general movement quality, and MR imaging of the brain (P = .020). CONCLUSIONS: The findings from this study demonstrate that a combined structural and functional imaging approach to the fetus will improve the characterization of early neurologic integrity, with the potential to inform postnatal outcome. This also lays the groundwork for further in vivo research as advanced imaging techniques are developed to study fetal neurologic development.

A comparison of MR cholangiopancreatography at 1.5 and 3.0 Tesla.

Clinical MR systems operating at 3.0 Tesla have the potential to significantly improve spatial resolution due to the boost in intrinsic signal to noise ratio. However, body imaging at these field strengths presents a number of technical challenges. We performed a prospective pilot study in which 10 patients underwent an MR cholangiopancreatography (MRCP) examination consecutively on 1.5 and 3.0 Tesla systems (both Philips Intera). An axial half Fourier segmented turbo spin echo (HASTE) sequence and a coronal thick-slab 2D turbo-spin echo (TSE) sequence were compared on both systems. A reader measured the signal intensity (SI) ratios of common bile duct (CBD): liver, and CBD: fat on HASTE images and CBD: liver on the TSE images. A second reader performed a qualitative analysis of the intrahepatic and extrahepatic biliary anatomy. Quantitative data was compared using the paired t-test and qualitative data with the paired Wilcoxon signed rank test with p < 0.05. The quantitative analysis of the HASTE sequences showed a slightly higher signal intensity ratio (CBD:liver) at 3.0 Tesla compared with 1.5 Tesla (8.1 vs 5.6, p = 0.002). No significant difference was found between the SI ratios of (CBD:fat) on HASTE images or (CBD:liver) on TSE images. The qualitative analysis showed superior image quality of 3.0 Tesla over 1.5 Tesla images on both HASTE (31 vs 25, p = 0.032), and TSE sequences (34 vs 28, p = 0.043). This pilot study shows that MRCP is feasible at 3.0 Tesla with some improvement in image quality and signal characteristics. Further development may be achieved with sequence optimization and improved coil design.

Visceral adipose tissue and metabolic complications of obesity are reduced in Prader-Willi syndrome female adults: evidence for novel influences on body fat distribution.

Visceral obesity is detrimental to health, but the mechanisms controlling body fat distribution are not fully understood. In premenopausal adult females (30 nonobese, 14 obese [body mass index >30 kg/m(2)]), variance in fasting insulin, glucose, insulin/glucose ratio, C-peptide/insulin ratio, triglycerides, and high-density lipoprotein/low-density lipoprotein-cholesterol ratio, were independently influenced by visceral but not total sc or abdominal sc adipose tissue, as measured by whole-body magnetic resonance imaging. Adult females with Prader-Willi syndrome (n = 13) had significantly reduced visceral adiposity, compared with obese controls (visceral/total sc adipose tissue ratio: 0.067 +/- 0.017 vs. 0.108 +/- 0.021), independent of their total adiposity (P < 0.001), or use of exogenous sex steroids. This is in contrast to that expected by their physical inactivity, hypogonadism, adult GH deficiency, and psychiatric problems. Females with Prader-Willi syndrome not receiving sex steroids (n = 8) had significantly reduced fasting insulin, insulin/glucose ratio, and triglycerides and increased C-peptide/insulin ratio, compared with obese controls, adjusting for total (P < 0.05) but not visceral adiposity (P = 0.3-0.6), supporting their association. The cause of the reduced visceral adiposity in Prader-Willi syndrome may reflect novel hormonal, hypothalamic, and/or genetic influences on body fat distribution.