Publications by authors named "Malek E Husseini"
Objectives: To quantitatively investigate the age- and sex-related longitudinal changes in trabecular volumetric bone mineral density (vBMD) and vertebral body volume at the thoracolumbar spine in adults.
Methods: We retrospectively included 168 adults (mean age 58.7 ± 9.
(1) Background and Purpose: In magnetic resonance imaging (MRI) of the spine, T2-weighted (T2-w) fat-saturated (fs) images improve the diagnostic assessment of pathologies. However, in the daily clinical setting, additional T2-w fs images are frequently missing due to time constraints or motion artifacts. Generative adversarial networks (GANs) can generate synthetic T2-w fs images in a clinically feasible time.
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- Osteoporosis is a common disease leading to fractures, particularly in the spine, and while areal bone mineral density (BMD) from DXA is the standard measurement, it has limitations that texture analysis from imaging techniques like CT and MRI might address.
- A study of 26 patients, mostly older females, explored whether MRI texture analysis could predict volumetric BMD and differentiate between those with and without osteoporotic fractures, using advanced imaging data and machine learning methods.
- Results showed that patients with fractures had significantly lower BMD values, and a model incorporating MRI data explained 40% of the variance in integral BMD linked to fracture status, revealing potential for improved risk estimation.
Article Synopsis
- Vertebral labelling and segmentation are crucial for improving automated spine image processing, aiding in clinical decision-making and population health analysis.
- The Large Scale Vertebrae Segmentation Challenge (VerSe) was created to tackle the challenges of this field by having participants develop algorithms for labelling and segmenting vertebrae using a curated dataset of CT scans.
- Results showed that an algorithm's performance depends significantly on its ability to identify vertebrae with rare anatomical variations, highlighting the complexities in spine analysis.