Acceleration of skeletal age MR examination using compressed sensing

Terada, Yasuhiko; Tamada, Daiki; Kose, Katsumi; Nozaki, Taiki; Kaneko, Yasuhito; Miyagi, Ryo; Yoshioka, Hiroshi

doi:10.1002/jmri.25140

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…The potential acceleration is markedly higher than acceleration factors reported in a recent study by Terada et al . reducing the acquisition time by a factor of 4 from 2:44 minutes to 41 seconds 42 . However, our results cannot easily be compared to the work of Terada et al ., since they used a low-field MR scanner at 0.3 Tesla.…”

Section: Discussionmentioning

confidence: 99%

Reducing acquisition time for MRI-based forensic age estimation

Neumayer

Schlöegl

Payer

et al. 2018

Sci Rep

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Radiology-based estimation of a living person’s unknown age has recently attracted increasing attention due to large numbers of undocumented immigrants entering Europe. To avoid the application of X-ray-based imaging techniques, magnetic resonance imaging (MRI) has been suggested as an alternative imaging modality. Unfortunately, MRI requires prolonged acquisition times, which potentially represents an additional stressor for young refugees. To eliminate this shortcoming, we investigated the degree of reduction in acquisition time that still led to reliable age estimates. Two radiologists randomly assessed original images and two sets of retrospectively undersampled data of 15 volunteers (N = 45 data sets) applying an established radiological age estimation method to images of the hand and wrist. Additionally, a neural network-based age estimation method analyzed four sets of further undersampled images from the 15 volunteers (N = 105 data sets). Furthermore, we compared retrospectively undersampled and acquired undersampled data for three volunteers. To assess reliability with increasing degree of undersampling, intra-rater and inter-rater agreement were analyzed computing signed differences and intra-class correlation. While our findings have to be confirmed by a larger prospective study, the results from both radiological and automatic age estimation showed that reliable age estimation was still possible for acquisition times of 15 seconds.

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Section: Discussionmentioning

confidence: 99%

Reducing acquisition time for MRI-based forensic age estimation

Neumayer

Schlöegl

Payer

et al. 2018

Sci Rep

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“…A final limitation of the current study is MRI as image modality due to the high cost in comparison to X-ray due to spacious facilities, equipment, medical technical assistants, and examination duration. In [69][70][71][72][73][74][75][76][77][78], a solution is proposed to accelerate acquisition times by reconstructing MRIs using undersampled data in k-space. Besides reducing the costs it can lower the stress to patients as well, which is particularly useful for young refugees with traumatic backgrounds.…”

Section: Study Limitations and Practical Implementationmentioning

confidence: 99%

“…Besides reducing the costs it can lower the stress to patients as well, which is particularly useful for young refugees with traumatic backgrounds. First promising applications of the acceleration technique in the area of age estimation have been analyzed in [71,74,75]. Other non-invasive techniques such as ultrasound (US) have yet to be evaluated for their suitability for age estimation, especially when used in combination with machine learning.…”

Section: Study Limitations and Practical Implementationmentioning

confidence: 99%

Automated age estimation of young individuals based on 3D knee MRI using deep learning

et al. 2020

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Age estimation is a crucial element of forensic medicine to assess the chronological age of living individuals without or lacking valid legal documentation. Methods used in practice are labor-intensive, subjective, and frequently comprise radiation exposure. Recently, also non-invasive methods using magnetic resonance imaging (MRI) have evaluated and confirmed a correlation between growth plate ossification in long bones and the chronological age of young subjects. However, automated and user-independent approaches are required to perform reliable assessments on large datasets. The aim of this study was to develop a fully automated and computer-based method for age estimation based on 3D knee MRIs using machine learning. The proposed solution is based on three parts: image-preprocessing, bone segmentation, and age estimation. A total of 185 coronal and 404 sagittal MR volumes from Caucasian male subjects in the age range of 13 and 21 years were available. The best result of the fivefold cross-validation was a mean absolute error of 0.67 ± 0.49 years in age regression and an accuracy of 90.9%, a sensitivity of 88.6%, and a specificity of 94.2% in classification (18-year age limit) using a combination of convolutional neural networks and tree-based machine learning algorithms. The potential of deep learning for age estimation is reflected in the results and can be further improved if it is trained on even larger and more diverse datasets.

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“…Using compressed sensing with an open 3-T MR scanner, Terada et al demonstrated that suitable hand/wrist images were still obtained with a 55-s acquisition time. 69 Using a 3-T MR scanner, Neumayer et al presented a 4-minute approach to study the AGFAD triad of anatomical sites. 70 These results look promising but need to be validated before they can be generally applied.…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

Dental and Skeletal Imaging in Forensic Age Estimation: Disparities in Current Approaches and the Continuing Search for Optimization

Tobel

Ottow

Widek

et al. 2020

Semin Musculoskelet Radiol

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Medical imaging for forensic age estimation in living adolescents and young adults continues to be controversial and a subject of discussion. Because age estimation based on medical imaging is well studied, it is the current gold standard. However, large disparities exist between the centers conducting age estimation, both between and within countries. This review provides an overview of the most common approaches applied in Europe, with case examples illustrating the differences in imaging modalities, in staging of development, and in statistical processing of the age data. Additionally, the review looks toward the future because several European research groups have intensified studies on age estimation, exploring four strategies for optimization: (1) increasing sample sizes of the reference populations, (2) combining single-site information into multifactorial information, (3) avoiding ionizing radiation, and (4) conducting a fully automated analysis.

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Acceleration of skeletal age MR examination using compressed sensing

Abstract: These results demonstrate the validity and reliability of skeletal age examination accelerated by CS-MRI. We conclude that the acceleration factor of 3 was optimal. J. Magn. Reson. Imaging 2016;44:204-211.

Cited by 9 publications

References 22 publications

Reducing acquisition time for MRI-based forensic age estimation

Reducing acquisition time for MRI-based forensic age estimation

Automated age estimation of young individuals based on 3D knee MRI using deep learning

Dental and Skeletal Imaging in Forensic Age Estimation: Disparities in Current Approaches and the Continuing Search for Optimization

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