C Jaimes, V Rofeberg, C Stopp, CM Ortinau, A Gholipour, KG Friedman, W Tworetzky, J Estroff, JW Newburger, D Wypij, SK Warfield, E Yang, and CK Rollins. 2020. “Association of Isolated Congenital Heart Disease with Fetal Brain Maturation.” AJNR Am J Neuroradiol.Abstract
BACKGROUND AND PURPOSE: Brain MRI of newborns with congenital heart disease show signs of immaturity relative to healthy controls. Our aim was to determine whether the semiquantitative fetal total maturation score can detect abnormalities in brain maturation in fetuses with congenital heart disease in the second and third trimesters. MATERIALS AND METHODS: We analyzed data from a prospective study of fetuses with and without congenital heart disease who underwent fetal MR imaging at 25-35 weeks' gestation. Two independent neuroradiologists blinded to the clinical data reviewed and scored all images using the fetal total maturation score. Interrater reliability was evaluated by the intraclass correlation coefficient using the individual reader scores, which were also used to calculate an average score for each subject. Comparisons of the average and individual reader scores between affected and control fetuses and relationships with clinical variables were evaluated using multivariable linear regression. RESULTS: Data from 69 subjects (48 cardiac, 21 controls) were included. High concordance was observed between readers with an intraclass correlation coefficient of 0.98 (95% CI, 0.97-0.99). The affected group had significantly lower fetal total maturation scores than the control group (-estimate, -0.9 [95% CI, -1.5 to -0.4], = .002), adjusting for gestational age and sex. Averaged fetal total maturation, germinal matrix, myelination, and superior temporal sulcus scores were significantly delayed in fetuses with congenital heart disease versus controls ( < .05 for each). The fetal total maturation score was not significantly associated with any cardiac, anatomic, or physiologic variables. CONCLUSIONS: The fetal total maturation score is sensitive to differences in brain maturation between fetuses with isolated congenital heart disease and healthy controls.
Davood Karimi, Haoran Dou, Simon K Warfield, and Ali Gholipour. 2020. “Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis.” Med Image Anal, 65, Pp. 101759.Abstract
Supervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical image analysis applications. However, the impact of label noise has not received sufficient attention. Recent studies have shown that label noise can significantly impact the performance of deep learning models in many machine learning and computer vision applications. This is especially concerning for medical applications, where datasets are typically small, labeling requires domain expertise and suffers from high inter- and intra-observer variability, and erroneous predictions may influence decisions that directly impact human health. In this paper, we first review the state-of-the-art in handling label noise in deep learning. Then, we review studies that have dealt with label noise in deep learning for medical image analysis. Our review shows that recent progress on handling label noise in deep learning has gone largely unnoticed by the medical image analysis community. To help achieve a better understanding of the extent of the problem and its potential remedies, we conducted experiments with three medical imaging datasets with different types of label noise, where we investigated several existing strategies and developed new methods to combat the negative effect of label noise. Based on the results of these experiments and our review of the literature, we have made recommendations on methods that can be used to alleviate the effects of different types of label noise on deep models trained for medical image analysis. We hope that this article helps the medical image analysis researchers and developers in choosing and devising new techniques that effectively handle label noise in deep learning.
Ayush Singh, Seyed Sadegh Mohseni Salehi, and Ali Gholipour. 2020. “Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging.” IEEE Trans Med Imaging, PP.Abstract
Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, large, and irregular fetal movements. It is, therefore, performed through visual monitoring of fetal motion and repeated acquisitions to ensure diagnostic-quality images are acquired. Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient. The current process is highly operator-dependent, increases scanner usage and cost, and significantly increases the length of fetal MRI scans which makes them hard to tolerate for pregnant women. To help build automatic MRI motion tracking and navigation systems to overcome the limitations of the current process and improve fetal imaging, we have developed a new realtime image-based motion tracking method based on deep learning that learns to predict fetal motion directly from acquired images. Our method is based on a recurrent neural network, composed of spatial and temporal encoder-decoders, that infers motion parameters from anatomical features extracted from sequences of acquired slices. We compared our trained network on heldout test sets (including data with different characteristics, e.g. different fetuses scanned at different ages, and motion trajectories recorded from volunteer subjects) with networks designed for estimation as well as methods adopted to make predictions. The results show that our method outperformed alternative techniques, and achieved real-time performance with average errors of 3.5 and 8 degrees for the estimation and prediction tasks, respectively. Our real-time deep predictive motion tracking technique can be used to assess fetal movements, to guide slice acquisitions, and to build navigation systems for fetal MRI.
Camilo Jaimes, Fedel Machado-Rivas, Onur Afacan, Shadab Khan, Bahram Marami, Cynthia M Ortinau, Caitlin K Rollins, Clemente Velasco-Annis, Simon K Warfield, and Ali Gholipour. 2020. “In vivo characterization of emerging white matter microstructure in the fetal brain in the third trimester.” Hum Brain Mapp, 41, 12, Pp. 3177-3185.Abstract
The third trimester of pregnancy is a period of rapid development of fiber bundles in the fetal white matter. Using a recently developed motion-tracked slice-to-volume registration (MT-SVR) method, we aimed to quantify tract-specific developmental changes in apparent diffusion coefficient (ADC), fractional anisotropy (FA), and volume in third trimester healthy fetuses. To this end, we reconstructed diffusion tensor images from motion corrected fetal diffusion magnetic resonance imaging data. With an approved protocol, fetal MRI exams were performed on healthy pregnant women at 3 Tesla and included multiple (2-8) diffusion scans of the fetal head (1-2 b = 0 s/mm images and 12 diffusion-sensitized images at b = 500 s/mm ). Diffusion data from 32 fetuses (13 females) with median gestational age (GA) of 33 weeks 4 days were processed with MT-SVR and deterministic tractography seeded by regions of interest corresponding to 12 major fiber tracts. Multivariable regression analysis was used to evaluate the association of GA with volume, FA, and ADC for each tract. For all tracts, the volume and FA increased, and the ADC decreased with GA. Associations reached statistical significance for: FA and ADC of the forceps major; volume and ADC for the forceps minor; FA, ADC, and volume for the cingulum; ADC, FA, and volume for the uncinate fasciculi; ADC of the inferior fronto-occipital fasciculi, ADC of the inferior longitudinal fasciculi; and FA and ADC for the corticospinal tracts. These quantitative results demonstrate the complex pattern and rates of tract-specific, GA-related microstructural changes of the developing white matter in human fetal brain.
Lana Vasung, Caitlin K Rollins, Hyuk Jin Yun, Clemente Velasco-Annis, Jennings Zhang, Konrad Wagstyl, Alan Evans, Simon K Warfield, Henry A Feldman, Ellen P Grant, and Ali Gholipour. 2020. “Quantitative In vivo MRI Assessment of Structural Asymmetries and Sexual Dimorphism of Transient Fetal Compartments in the Human Brain.” Cereb Cortex, 30, 3, Pp. 1752-1767.Abstract
Structural asymmetries and sexual dimorphism of the human cerebral cortex have been identified in newborns, infants, children, adolescents, and adults. Some of these findings were linked with cognitive and neuropsychiatric disorders, which have roots in altered prenatal brain development. However, little is known about structural asymmetries or sexual dimorphism of transient fetal compartments that arise in utero. Thus, we aimed to identify structural asymmetries and sexual dimorphism in the volume of transient fetal compartments (cortical plate [CP] and subplate [SP]) across 22 regions. For this purpose, we used in vivo structural T2-weighted MRIs of 42 healthy fetuses (16.43-36.86 gestational weeks old, 15 females). We found significant leftward asymmetry in the volume of the CP and SP in the inferior frontal gyrus. The orbitofrontal cortex showed significant rightward asymmetry in the volume of CP merged with SP. Males had significantly larger volumes in regions belonging to limbic, occipital, and frontal lobes, which were driven by a significantly larger SP. Lastly, we did not observe sexual dimorphism in the growth trajectories of the CP or SP. In conclusion, these results support the hypothesis that structural asymmetries and sexual dimorphism in relative volumes of cortical regions are present during prenatal brain development.
Onur Afacan, Scott W Hoge, Tess E Wallace, Ali Gholipour, Sila Kurugol, and Simon K Warfield. 2020. “Simultaneous Motion and Distortion Correction Using Dual-Echo Diffusion-Weighted MRI.” J Neuroimaging, 30, 3, Pp. 276-285.Abstract
BACKGROUND AND PURPOSE: Geometric distortions resulting from large pose changes reduce the accuracy of motion measurements and interfere with the ability to generate artifact-free information. Our goal is to develop an algorithm and pulse sequence to enable motion-compensated, geometric distortion compensated diffusion-weighted MRI, and to evaluate its efficacy in correcting for the field inhomogeneity and position changes, induced by large and frequent head motions. METHODS: Dual echo planar imaging (EPI) with a blip-reversed phase encoding distortion correction technique was evaluated in five volunteers in two separate experiments and compared with static field map distortion correction. In the first experiment, dual-echo EPI images were acquired in two head positions designed to induce a large field inhomogeneity change. A field map and a distortion-free structural image were acquired at each position to assess the ability of dual-echo EPI to generate reliable field maps and enable geometric distortion correction in both positions. In the second experiment, volunteers were asked to move to multiple random positions during a diffusion scan. Images were reconstructed using the dual-echo correction and a slice-to-volume registration (SVR) registration algorithm. The accuracy of SVR motion estimates was compared to externally measured ground truth motion parameters. RESULTS: Our results show that dual-echo EPI can produce slice-level field maps with comparable quality to field maps generated by the reference gold standard method. We also show that slice-level distortion correction improves the accuracy of SVR algorithms as slices acquired at different orientations have different levels of distortion, which can create errors in the registration process. CONCLUSIONS: Dual-echo acquisitions with blip-reversed phase encoding can be used to generate slice-level distortion-free images, which is critical for motion-robust slice to volume registration. The distortion corrected images not only result in better motion estimates, but they also enable a more accurate final diffusion image reconstruction.
Lana Vasung, Caitlin K Rollins, Clemente Velasco-Annis, Hyuk Jin Yun, Jennings Zhang, Simon K Warfield, Henry A Feldman, Ali Gholipour, and Ellen P Grant. 2020. “Spatiotemporal Differences in the Regional Cortical Plate and Subplate Volume Growth during Fetal Development.” Cereb Cortex, 30, 8, Pp. 4438-4453.Abstract
The regional specification of the cerebral cortex can be described by protomap and protocortex hypotheses. The protomap hypothesis suggests that the regional destiny of cortical neurons and the relative size of the cortical area are genetically determined early during embryonic development. The protocortex hypothesis suggests that the regional growth rate is predominantly shaped by external influences. In order to determine regional volumes of cortical compartments (cortical plate (CP) or subplate (SP)) and estimate their growth rates, we acquired T2-weighted in utero MRIs of 40 healthy fetuses and grouped them into early (<25.5 GW), mid- (25.5-31.6 GW), and late (>31.6 GW) prenatal periods. MRIs were segmented into CP and SP and further parcellated into 22 gyral regions. No significant difference was found between periods in regional volume fractions of the CP or SP. However, during the early and mid-prenatal periods, we found significant differences in relative growth rates (% increase per GW) between regions of cortical compartments. Thus, the relative size of these regions are most likely conserved and determined early during development whereas more subtle growth differences between regions are fine-tuned later, during periods of peak thalamocortical growth. This is in agreement with both the protomap and protocortex hypothesis.
Seyed Raein Hashemi, Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, Sanjay P Prabhu, Simon K Warfield, and Ali Gholipour. 2019. “Asymmetric loss functions and deep densely-connected networks for highly-imbalanced medical image segmentation: Application to multiple sclerosis lesion detection.” IEEE Access, 7, Pp. 1721-1735.
Cynthia M Ortinau, Caitlin K Rollins, Ali Gholipour, Hyuk Jin Yun, Mackenzie Marshall, Borjan Gagoski, Onur Afacan, Kevin Friedman, Wayne Tworetzky, Simon K Warfield, Jane W Newburger, Terrie E Inder, Ellen P Grant, and Kiho Im. 2019. “Early-Emerging Sulcal Patterns Are Atypical in Fetuses with Congenital Heart Disease.” Cereb Cortex, 29, 8, Pp. 3605-3616.Abstract
Fetuses with congenital heart disease (CHD) have third trimester alterations in cortical development on brain magnetic resonance imaging (MRI). However, the intersulcal relationships contributing to global sulcal pattern remain unknown. This study applied a novel method for examining the geometric and topological relationships between sulci to fetal brain MRIs from 21-30 gestational weeks in CHD fetuses (n = 19) and typically developing (TD) fetuses (n = 17). Sulcal pattern similarity index (SI) to template fetal brain MRIs was determined for the position, area, and depth for corresponding sulcal basins and intersulcal relationships for each subject. CHD fetuses demonstrated altered global sulcal patterns in the left hemisphere compared with TD fetuses (TD [SI, mean ± SD]: 0.822 ± 0.023, CHD: 0.795 ± 0.030, P = 0.002). These differences were present in the earliest emerging sulci and were driven by differences in the position of corresponding sulcal basins (TD: 0.897 ± 0.024, CHD: 0.878 ± 0.019, P = 0.006) and intersulcal relationships (TD: 0.876 ± 0.031, CHD: 0.857 ± 0.018, P = 0.033). No differences in cortical gyrification index, mean curvature, or surface area were present. These data suggest our methods may be more sensitive than traditional measures for evaluating cortical developmental alterations early in gestation.
Shadab Khan, Lana Vasung, Bahram Marami, Caitlin K Rollins, Onur Afacan, Cynthia M Ortinau, Edward Yang, Simon K Warfield, and Ali Gholipour. 2019. “Fetal brain growth portrayed by a spatiotemporal diffusion tensor MRI atlas computed from in utero images.” Neuroimage, 185, Pp. 593-608.Abstract
Altered structural fetal brain development has been linked to neuro-developmental disorders. These structural alterations can be potentially detected in utero using diffusion tensor imaging (DTI). However, acquisition and reconstruction of in utero fetal brain DTI remains challenging. Until now, motion-robust DTI methods have been employed for reconstruction of in utero fetal DTIs. However, due to the unconstrained fetal motion and permissible in utero acquisition times, these methods yielded limited success and have typically resulted in noisy DTIs. Consequently, atlases and methods that could enable groupwise studies, multi-modality imaging, and computer-aided diagnosis from in utero DTIs have not yet been developed. This paper presents the first DTI atlas of the fetal brain computed from in utero diffusion-weighted images. For this purpose an algorithm for computing an unbiased spatiotemporal DTI atlas, which integrates kernel-regression in age with a diffeomorphic tensor-to-tensor registration of motion-corrected and reconstructed individual fetal brain DTIs, was developed. Our new algorithm was applied to a set of 67 fetal DTI scans acquired from healthy fetuses each scanned at a gestational age between 21 and 39 weeks. The neurodevelopmental trends in the fetal brain, characterized by the atlas, were qualitatively and quantitatively compared with the observations reported in prior ex vivo and in utero studies, and with results from imaging gestational-age equivalent preterm infants. Our major findings revealed early presence of limbic fiber bundles, followed by the appearance and maturation of projection pathways (characterized by an age related increase in FA) during late 2nd and early 3rd trimesters. During the 3rd trimester association fiber bundles become evident. In parallel with the appearance and maturation of fiber bundles, from 21 to 39 gestational weeks gradual disappearance of the radial coherence of the telencephalic wall was qualitatively identified. These results and analyses show that our DTI atlas of the fetal brain is useful for reliable detection of major neuronal fiber bundle pathways and for characterization of the fetal brain reorganization that occurs in utero. The atlas can also serve as a useful resource for detection of normal and abnormal fetal brain development in utero.
Onur Afacan, Judy A Estroff, Edward Yang, Carol E Barnewolt, Susan A Connolly, Richard B Parad, Robert V Mulkern, Simon K Warfield, and Ali Gholipour. 2019. “Fetal Echoplanar Imaging: Promises and Challenges.” Top Magn Reson Imaging, 28, 5, Pp. 245-254.Abstract
Fetal magnetic resonance imaging (MRI) has been gaining increasing interest in both clinical radiology and research. Echoplanar imaging (EPI) offers a unique potential, as it can be used to acquire images very fast. It can be used to freeze motion, or to get multiple images with various contrast mechanisms that allow studying the microstructure and function of the fetal brain and body organs. In this article, we discuss the current clinical and research applications of fetal EPI. This includes T2*-weighted imaging to better identify blood products and vessels, using diffusion-weighted MRI to investigate connections of the developing brain and using functional MRI (fMRI) to identify the functional networks of the developing brain. EPI can also be used as an alternative structural sequence when banding or standing wave artifacts adversely affect the mainstream sequences used routinely in structural fetal MRI. We also discuss the challenges with EPI acquisitions, and potential solutions. As EPI acquisitions are inherently sensitive to susceptibility artifacts, geometric distortions limit the use of high-resolution EPI acquisitions. Also, interslice motion and transmit and receive field inhomogeneities may create significant artifacts in fetal EPI. We conclude by discussing promising research directions to overcome these challenges to improve the use of EPI in clinical and research applications.
Jamshid Sourati, Ali Gholipour, Jennifer G Dy, Xavier Tomas-Fernandez, Sila Kurugol, and Simon K Warfield. 2019. “Intelligent Labeling Based on Fisher Information for Medical Image Segmentation Using Deep Learning.” IEEE Trans Med Imaging, 38, 11, Pp. 2642-2653.Abstract
Deep convolutional neural networks (CNN) have recently achieved superior performance at the task of medical image segmentation compared to classic models. However, training a generalizable CNN requires a large amount of training data, which is difficult, expensive, and time-consuming to obtain in medical settings. Active Learning (AL) algorithms can facilitate training CNN models by proposing a small number of the most informative data samples to be annotated to achieve a rapid increase in performance. We proposed a new active learning method based on Fisher information (FI) for CNNs for the first time. Using efficient backpropagation methods for computing gradients together with a novel low-dimensional approximation of FI enabled us to compute FI for CNNs with a large number of parameters. We evaluated the proposed method for brain extraction with a patch-wise segmentation CNN model in two different learning scenarios: universal active learning and active semi-automatic segmentation. In both scenarios, an initial model was obtained using labeled training subjects of a source data set and the goal was to annotate a small subset of new samples to build a model that performs well on the target subject(s). The target data sets included images that differed from the source data by either age group (e.g. newborns with different image contrast) or underlying pathology that was not available in the source data. In comparison to several recently proposed AL methods and brain extraction baselines, the results showed that FI-based AL outperformed the competing methods in improving the performance of the model after labeling a very small portion of target data set (<0.25%).
Ali Gholipour and Simon K Warfield. 2019. “Motion-corrected foetal cardiac MRI.” Nat Biomed Eng, 3, 11, Pp. 852-854.
Bahram Marami, Benoit Scherrer, Shadab Khan, Onur Afacan, Sanjay P Prabhu, Mustafa Sahin, Simon K Warfield, and Ali Gholipour. 2019. “Motion-robust diffusion compartment imaging using simultaneous multi-slice acquisition.” Magn Reson Med, 81, 5, Pp. 3314-3329.Abstract
PURPOSE: To achieve motion-robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi-slice, diffusion-weighted brain MRI. METHODS: Simultaneous multi-slice (SMS) acquisition enables fast and dense sampling of k- and q-space. We propose to achieve motion-robust DCI via slice-level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice-to-volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction. RESULTS: We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT-SMR) to multi b-value SMS diffusion-weighted brain MRI of healthy volunteers and motion-corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing-fiber regions show robust reconstruction in the presence of motion. CONCLUSION: The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non-cooperative patients.
Seyed Sadegh Mohseni Salehi, Shadab Khan, Deniz Erdogmus, and Ali Gholipour. 2019. “Real-Time Deep Pose Estimation With Geodesic Loss for Image-to-Template Rigid Registration.” IEEE Trans Med Imaging, 38, 2, Pp. 470-481.Abstract
With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3-D rigid registration, we propose deep learning-based methods that are trained to find the 3-D position of arbitrarily-oriented subjects or anatomy in a canonical space based on slices or volumes of medical images. For this, we propose regression convolutional neural networks (CNNs) that learn to predict the angle-axis representation of 3-D rotations and translations using image features. We use and compare mean square error and geodesic loss to train regression CNNs for 3-D pose estimation used in two different scenarios: slice-to-volume registration and volume-to-volume registration. As an exemplary application, we applied the proposed methods to register arbitrarily oriented reconstructed images of fetuses scanned in-utero at a wide gestational age range to a standard atlas space. Our results show that in such registration applications that are amendable to learning, the proposed deep learning methods with geodesic loss minimization achieved 3-D pose estimation with a wide capture range in real-time (<100ms). We also tested the generalization capability of the trained CNNs on an expanded age range and on images of newborn subjects with similar and different MR image contrasts. We trained our models on T2-weighted fetal brain MRI scans and used them to predict the 3-D pose of newborn brains based on T1-weighted MRI scans. We showed that the trained models generalized well for the new domain when we performed image contrast transfer through a conditional generative adversarial network. This indicates that the domain of application of the trained deep regression CNNs can be further expanded to image modalities and contrasts other than those used in training. A combination of our proposed methods with accelerated optimization-based registration algorithms can dramatically enhance the performance of automatic imaging devices and image processing methods of the future.
Joseph Enguehard, Peter O’Halloran, and Ali Gholipour. 2019. “Semi Supervised Learning with Deep Embedded Clustering for Image Classification and Segmentation.” IEEE Access.
Jinglong Du, Lulu Wang, Ali Gholipour, Zhongshi He, and Yuanyuan Jia. 2018. “Accelerated Super-resolution MR Image Reconstruction via a 3D Densely Connected Deep Convolutional Neural Network.” 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE.
Jamshid Sourati, Ali Gholipour, Jennifer G Dy, Sila Kurugol, and Simon K Warfield. 2018. “Active Deep Learning with Fisher Information for Patch-Wise Semantic Segmentation.” In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Pp. 83–91. Springer.
Cynthia M Ortinau, Kathryn Mangin-Heimos, Joseph Moen, Dimitrios Alexopoulos, Terrie E Inder, Ali Gholipour, Joshua S Shimony, Pirooz Eghtesady, Bradley L Schlaggar, and Christopher D Smyser. 2018. “Prenatal to postnatal trajectory of brain growth in complex congenital heart disease.” Neuroimage Clin, 20, Pp. 913-922.Abstract
Altered brain development is a common feature of the neurological sequelae of complex congenital heart disease (CHD). These alterations include abnormalities in brain size and growth that begin prenatally and persist postnatally. However, the longitudinal trajectory of changes in brain volume from the prenatal to postnatal environment have not been investigated. We aimed to evaluate the trajectory of brain growth in a cohort of patients with complex CHD (n = 16) and healthy controls (n = 15) to test the hypothesis that patients with complex CHD would have smaller total brain volume (TBV) prenatally, which would become increasingly prominent by three months of age. Participants underwent fetal magnetic resonance imaging (MRI) at a mean of 32 weeks gestation, a preoperative/neonatal MRI shortly after birth, a postoperative MRI (CHD only), and a 3-month MRI to evaluate the trajectory of brain growth. Three-dimensional volumetric analysis was applied to the MRI data to measure TBV, as well as tissue-specific volumes of the cortical gray matter (CGM), white matter (WM), subcortical (deep nuclear) gray matter (SCGM), cerebellum, and cerebrospinal fluid (CSF). A random coefficients model was used to investigate longitudinal changes in TBV and demonstrated an altered trajectory of brain growth in the CHD population. The estimated slope for TBV from fetal to 3-month MRI was 11.5 cm per week for CHD infants compared to 16.7 cm per week for controls (p = 0.0002). Brain growth followed a similar trajectory for the CGM (p < 0.0001), SCGM (p = 0.002), and cerebellum (p = 0.005). There was no difference in growth of the WM (p = 0.30) or CSF (p = 0.085). Brain injury was associated with reduced TBV at 3-month MRI (p = 0.02). After removing infants with brain injury from the model, an altered trajectory of brain growth persisted in CHD infants (p = 0.006). These findings extend the existing literature by demonstrating longitudinal impairments in brain development in the CHD population and emphasize the global nature of disrupted brain growth from the prenatal environment through early infancy.
Sebastien Benali, Patrick R Johnston, Ali Gholipour, Monet E Dugan, Keith Heberlein, Himanshu Bhat, and Sarah D Bixby. 2018. “Simultaneous multi-slice accelerated turbo spin echo of the knee in pediatric patients.” Skeletal Radiol, 47, 6, Pp. 821-831.Abstract
PURPOSE: To compare knee MRI performed with the integrated parallel acquisition technique (PAT) and simultaneous multislice (SMS) turbo spin echo (TSE) T2-weighted (T2w) sequences with conventional TSE sequences in pediatric patients. MATERIALS AND METHODS: This was a retrospective IRB-approved study. Seventy-four subjects (26 male, 48 female, mean age 15.3 years, range 8-20) underwent 3-T MRI of the knee with a T2w TSE pulse sequence prototype with four-fold PAT and SMS acceleration as well as the standard PAT-only accelerated sequences. Images were anonymized and two study folders were created: one examination with only T2w PAT2 images (conventional examination) and one examination with only T2w SMS2/PAT2 sequences (SMS examination). Two readers rated examinations for 15 specific imaging findings and 5 quality metrics. Interreader agreement was measured. Signal to noise (SNR) and contrast to noise (CNR) were measured for SMS and conventional T2w sequences. RESULTS: Consensus review demonstrated diagnostic quality performance of SMS examinations with respect to all 15 structures. Average area under the curve (AROC) was 0.95 and 0.97 for readers 1 and 2, respectively. The conventional sequence was favored over SMS for four out of five quality metrics (p < 0.001). SNR and CNR were higher for the conventional sequences compared to SMS. CONCLUSION: SMS accelerated T2w TSE sequences offer a faster alternative for knee imaging in pediatric patients without compromise in diagnostic performance despite diminished SNR. The four-fold acceleration of SMS is beneficial to pediatric patients who often have difficulty staying still for long MRI examinations.