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Positron emission tomography (PET) imaging is an effective tool used in determining disease stage and lesion malignancy; however, radiation exposure to patients and technicians during PET scans continues to draw concern. One way to minimize radiation exposure is to reduce the dose of radioactive tracer administered in order to obtain the scan. Yet, low-dose images are inherently noisy and have poor image quality making them difficult to read. This paper proposes the use of a deep learning model that takes specific image features into account in the loss function to denoise low-dose PET image slices and estimate their full-dose image quality equivalent. Testing on low-dose image slices indicates a significant improvement in image quality that is comparable to the ground truth full–dose image slices. Additionally, this approach can lower the cost of conducting a PET scan since less radioactive material is required per scan, which may promote the usage of PET scans for medical diagnosis.

The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., “scrubbing”) and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8–24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population.

T1- and T2-weighted (T1w and T2w) images are essential for tissue classification and anatomical localization in Magnetic Resonance Imaging (MRI) analyses. However, these anatomical data can be challenging to acquire in non-sedated neonatal cohorts, which are prone to high amplitude movement and display lower tissue contrast than adults. As a result, one of these modalities may be missing or of such poor quality that they cannot be used for accurate image processing, resulting in subject loss. While recent literature attempts to overcome these issues in adult populations using synthetic imaging approaches, evaluation of the efficacy of these methods in pediatric populations and the impact of these techniques in conventional MR analyses has not been performed. In this work, we present two novel methods to generate pseudo-T2w images: the first is based in deep learning and expands upon previous models to 3D imaging without the requirement of paired data, the second is based in nonlinear multi-atlas registration providing a computationally lightweight alternative. We demonstrate the anatomical accuracy of pseudo-T2w images and their efficacy in existing MR processing pipelines in two independent neonatal cohorts. Critically, we show that implementing these pseudo-T2w methods in resting-state functional MRI analyses produces virtually identical functional connectivity results when compared to those resulting from T2w images, confirming their utility in infant MRI studies for salvaging otherwise lost subject data.

This book focuses on the relationship between the personal lives of the writers John Middleton Murry, Katherine Mansfield, and D.H. Lawrence and the works they produced. It develops a portrait of a circle of writers who significantly influenced the development of modernism in Britain.

Objective To investigate resting‐state functional connectivity (FC) in pediatric patients with tuberous sclerosis complex and intractable epilepsy requiring surgery. Methods Resting‐state functional MRI was utilized to investigate functional connectivity in 13 pediatric patients with tuberous sclerosis complex (TSC) and intractable epilepsy requiring surgery. Results The majority of patients demonstrated a resting‐state network architecture similar to those reported in healthy individuals. However, preoperative differences were evident between patients with high versus low tuber burden, as well as those with good versus poor neurodevelopmental outcomes, most notably in the cingulo‐opercular and visual resting‐state networks. One patient with high tuber burden and poor preoperative development and seizure control had nearly normal development and seizure resolution after surgery. This was accompanied by significant improvement in resting‐state network architecture just one day postoperatively. Significance Although many patients with tuberous sclerosis complex and medically refractory epilepsy demonstrate functional connectivity patterns similar to healthy children, relationships within and between RSNs demonstrate clear differences in patients with higher tuber burden and worse outcomes. Improvements in resting‐state network organization postoperatively may be related to epilepsy surgery outcomes, providing candidate biomarkers for clinical management in this high‐risk population.

Centred on the relationship between the personal lives of the writers John Middleton Murry, Katherine Mansfield, and D. H. Lawrence and the works they produced this intriguing study develops a portrait of a circle of writers who significantly influenced the development of modernism in Britain.