Johan L. Bloem
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3 records found
1
Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: A feasibility study (vol 79, pg 1127, 2018)
Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: A feasibility study (Magn Reson Med. 2018; 79:1127‐1134)
In Magn Reson Med. 2018; 79:1127-1134, the acquisition matrix of the coronal T1-weighted sequence acquired prior to contrast injection was mistyped. The correct acquisition matrix is 388 × 288 instead of: 388 × 88. We apologize for any inconvenience this may have caused to the readers.
Methods: For 485 early arthritis patients (clinically confirmed arthritis of one or more joints, symptoms for less than 2 years), MR scans of the wrist were processed in three automatic stages. First, super-resolution reconstruction was applied to fuse coronal and axial scans into a single high-resolution 3D image. Next, the carpal bones were located and delineated using atlas-based segmentation. Finally, the extent of BME within each bone was quantified by identifying image intensity values characteristic of BME by fuzzy clustering and measuring the fraction of voxels with these characteristic intensities within each bone. Correlation with visual BME scores was assessed through Pearson correlation coefficient.
Results: Pearson correlation between quantitative and visual BME scores across 485 patients was r=0.83, P<0.001. Conclusions: Quantitative measurement of BME on MRI of the wrist has the potential to provide a feasible alternative to visual scoring. Complete automation requires automatic detection and compensation of acquisition artifacts. ...
Purpose: To investigate the feasibility of automatic quantification of bone marrow edema (BME) on MRI of the wrist in patients with early arthritis.
Methods: For 485 early arthritis patients (clinically confirmed arthritis of one or more joints, symptoms for less than 2 years), MR scans of the wrist were processed in three automatic stages. First, super-resolution reconstruction was applied to fuse coronal and axial scans into a single high-resolution 3D image. Next, the carpal bones were located and delineated using atlas-based segmentation. Finally, the extent of BME within each bone was quantified by identifying image intensity values characteristic of BME by fuzzy clustering and measuring the fraction of voxels with these characteristic intensities within each bone. Correlation with visual BME scores was assessed through Pearson correlation coefficient.
Results: Pearson correlation between quantitative and visual BME scores across 485 patients was r=0.83, P<0.001. Conclusions: Quantitative measurement of BME on MRI of the wrist has the potential to provide a feasible alternative to visual scoring. Complete automation requires automatic detection and compensation of acquisition artifacts.