Dorigatti Soldatelli Matheus,  Calixto Camilo,  Jaimes Camilo,  Warfield Simon,  Gholipour Ali,  Karimi Davood

Final Pr. ID: Poster #: SCI-015

Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero. dMRI enables streamlined tractography, a computation with unique applications such as white matter tract-specific analysis and structural connectivity assessment. However, due to low fetal dMRI data quality and the challenging nature of tractography, existing methods often produce highly inaccurate results. This study addresses these challenges by proposing an anatomically constrained tractography method that accurately segments fetal brain tissue directly within dMRI. Read More

Authors:  Dorigatti Soldatelli Matheus , Calixto Camilo , Jaimes Camilo , Warfield Simon , Gholipour Ali , Karimi Davood

Keywords:  Diffusion MRI, Fetal MRI, Neural Network

Castiglione James,  Gilligan Leah,  Somasundaram Elanchezhian,  Trout Andrew,  Brady Samuel

Final Pr. ID: Paper #: 059

In this study, we trained two convolutional neural networks to automatically identify the third vertebral level and segment the abdominal muscle in contrast enhanced abdominal CT images. In the future, these models will be used to determine reference ranges for skeletal muscle mass in children by age for the purpose of identifying patient characteristics associated with differences in skeletal muscle mass. Read More

Authors:  Castiglione James , Gilligan Leah , Somasundaram Elanchezhian , Trout Andrew , Brady Samuel

Keywords:  Neural Network, Segmentation, Deep Learning

Cole Elizabeth,  Pauly John,  Vasanawala Shreyas,  Cheng Joseph

Final Pr. ID: Poster #: SCI-059

Convolutional neural networks (CNNs) have proven to be valuable in the fields of image processing and computer vision. Our work applies complex-valued CNNs to magnetic resonance imaging (MRI) to reduce scan times. The reduction of scan times has widespread pediatric benefits. A typical scan requires that patients remain still for up to an hour to produce a clear image, which is difficult for children without inducing anesthesia, which carries risks. A need exists for greatly improved MRI scan times without the loss of diagnostic accuracy. This scan time can be reduced by subsampling in k-space. We use CNNs to reconstruct images from these undersampled acquisitions. Our work investigates complex-valued CNNs for image reconstruction in lieu of two-channel real-valued CNNs. Read More

Authors:  Cole Elizabeth , Pauly John , Vasanawala Shreyas , Cheng Joseph

Keywords:  Magnetic Resonance Imaging, deep learning, Neural network

Annapragada Akshaya,  Donaruma-kwoh Marcella,  Annapragada Ananth,  Starosolski Zbigniew

Final Pr. ID: Paper #: 028

Child abuse is the principal cause of traumatic injury and death in children 0-36 months old. Radiologic findings are a critically important piece of evidence necessary for assessment by a Child Protection Team to assess possible abuse. Current algorithmic strategies for the detection of abuse are sensitive, but compromise specificity. The purpose of this study was to determine the performance of deep learning to diagnose child abuse using Natural Language Processing of the unstructured free-text from Electronic Health Records (EHR), including essential information from Radiology Reports. Read More

Authors:  Annapragada Akshaya , Donaruma-kwoh Marcella , Annapragada Ananth , Starosolski Zbigniew

Keywords:  Child Abuse, decision support, Convolutional Neural Network