Tess E Wallace1,2, Onur Afacan1,2, Camilo Jaimes1,2, Joanne Rispoli1,2, Kristina Pelkola1, Monet Dugan1, Tobias Kober3,4,5, Simon K Warfield1,2. 1. Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA. 2. Department of Radiology, Harvard Medical School, Boston, MA, USA. 3. Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland. 4. Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland. 5. LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Abstract
PURPOSE: To investigate the ability of free induction decay navigator (FIDnav)-based motion monitoring to predict diagnostic utility and reduce the time and cost associated with acquiring diagnostically useful images in a pediatric patient cohort. METHODS: A study was carried out in 102 pediatric patients (aged 0-18 years) at 3T using a 32-channel head coil array. Subjects were scanned with an FID-navigated MPRAGE sequence and images were graded by two radiologists using a five-point scale to evaluate the impact of motion artifacts on diagnostic image quality. The correlation between image quality and four integrated FIDnav motion metrics was investigated, as well as the sensitivity and specificity of each FIDnav-based metric to detect different levels of motion corruption in the images. Potential time and cost savings were also assessed by retrospectively applying an optimal detection threshold to FIDnav motion scores. RESULTS: A total of 12% of images were rated as non-diagnostic, while a further 12% had compromised diagnostic value due to motion artifacts. FID-navigated metrics exhibited a moderately strong correlation with image grade (Spearman's rho ≥ 0.56). Integrating the cross-correlation between FIDnav signal vectors achieved the highest sensitivity and specificity for detecting non-diagnostic images, yielding total time savings of 7% across all scans. This corresponded to a financial benefit of $2080 in this study. CONCLUSIONS: Our results indicate that integrated motion metrics from FIDnavs embedded in structural MRI are a useful predictor of diagnostic image quality, which translates to substantial time and cost savings when applied to pediatric MRI examinations.
PURPOSE: To investigate the ability of free induction decay navigator (FIDnav)-based motion monitoring to predict diagnostic utility and reduce the time and cost associated with acquiring diagnostically useful images in a pediatric patient cohort. METHODS: A study was carried out in 102 pediatric patients (aged 0-18 years) at 3T using a 32-channel head coil array. Subjects were scanned with an FID-navigated MPRAGE sequence and images were graded by two radiologists using a five-point scale to evaluate the impact of motion artifacts on diagnostic image quality. The correlation between image quality and four integrated FIDnav motion metrics was investigated, as well as the sensitivity and specificity of each FIDnav-based metric to detect different levels of motion corruption in the images. Potential time and cost savings were also assessed by retrospectively applying an optimal detection threshold to FIDnav motion scores. RESULTS: A total of 12% of images were rated as non-diagnostic, while a further 12% had compromised diagnostic value due to motion artifacts. FID-navigated metrics exhibited a moderately strong correlation with image grade (Spearman's rho ≥ 0.56). Integrating the cross-correlation between FIDnav signal vectors achieved the highest sensitivity and specificity for detecting non-diagnostic images, yielding total time savings of 7% across all scans. This corresponded to a financial benefit of $2080 in this study. CONCLUSIONS: Our results indicate that integrated motion metrics from FIDnavs embedded in structural MRI are a useful predictor of diagnostic image quality, which translates to substantial time and cost savings when applied to pediatric MRI examinations.
Authors: Sonya A Vanderby; Paul S Babyn; Michael W Carter; Susan M Jewell; Patricia D McKeever Journal: Radiology Date: 2010-05-26 Impact factor: 11.105
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