Publications by authors named "S W Needleman"
Article Synopsis
- - This study addresses the challenges of accurately segmenting airway trees in the context of diagnosing and characterizing chronic respiratory diseases, emphasizing the limitations of existing traditional methods requiring manual adjustments due to inconsistent segmentation results.
- - It introduces a novel deep learning approach called Interpolation-Split, which enhances segmentation performance by improving data quality through interpolation and image splitting, while also being efficient in terms of computational resource usage.
- - The results show that this new method significantly outperforms previous models in segmentation accuracy, achieving high dice similarity coefficients while requiring less GPU memory, making it more accessible for various computational environments.
Introduction: Following the COVID-19 pandemic and ongoing pressures within the National Health Service, there has been an increasing concern about the well-being of junior doctors in the UK. Newly qualified doctors are particularly vulnerable due to the significant shift in responsibility they experience.
Objectives: To implement peer-led reflective session for foundation year 1 (FY1) (first-year postgraduation) doctors and to create a dedicated space in which doctors could share difficult or challenging experiences.
Purpose: To demonstrate proof-of-concept of a T *-sensitized oxygen-enhanced MRI (OE-MRI) method at 3T by assessing signal characteristics, repeatability, and reproducibility of dynamic lung OE-MRI metrics in healthy volunteers.
Methods: We performed sequence-specific simulations for protocol optimisation and acquired free-breathing OE-MRI data from 16 healthy subjects using a dual-echo RF-spoiled gradient echo approach at 3T across two institutions. Non-linear registration and tissue density correction were applied.
Purpose: Dynamic lung oxygen-enhanced MRI (OE-MRI) is challenging due to the presence of confounding signals and poor signal-to-noise ratio, particularly at 3 T. We have created a robust pipeline utilizing independent component analysis (ICA) to automatically extract the oxygen-induced signal change from confounding factors to improve the accuracy and sensitivity of lung OE-MRI.
Methods: Dynamic OE-MRI was performed on healthy participants using a dual-echo multi-slice spoiled gradient echo sequence at 3 T and cyclical gas delivery.