Apparent Diffusion Coefficient of Normal Abdominal Organs and Bone Marrow From Whole-Body DWI at 1.5 T: The Effect of Sex and Age

Lavdas, Ioannis; Rockall, Andrea; Castelli, Federica; Sandhu, Ranbir S.; Papadaki, Annie; Honeyfield, Lesley; Waldman, Adam D.; Aboagye, Eric O.

doi:10.2214/ajr.14.13964

Cited by 59 publications

(54 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study was approved by the local ethics committee, and written consent was obtained from the participants before imaging. Fifty‐one healthy volunteers (24 male—mean age = 37, range = 23–67 yr and 27 female—mean age = 39, range = 23–68 yr) were scanned with whole body MRI from February 2012 to May 2014 …”

Section: Methodsmentioning

confidence: 99%

“…Fifty-one healthy volunteers (24 male-mean age = 37, range = 23-67 yr and 27 female-mean age = 39, range = 23-68 yr) were scanned with whole body MRI from February 2012 to May 2014. 17 Whole body MRI was performed in a moving-table 1.5 T system (Siemens Avanto with Syngo MR B17, Erlangen, Germany), using the body coil for transmission and the neck/ body phased array coils as receive coils. Four different imaging stations were used to achieve full body coverage, from the top of the neck to mid-thighs.…”

Section: A Healthy Volunteers and Imaging Protocolmentioning

confidence: 99%

See 1 more Smart Citation

Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi‐atlas (MA) approach

Lavdas¹,

Glocker

Kamnitsas

et al. 2017

Medical Physics

Self Cite

View full text Add to dashboard Cite

Purpose: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. Methods: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three surface distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average surface distance (ASD), root-mean-square surface distance (RMSSD), and Hausdorff distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a 'per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a 'per-organ' basis, when using different imaging combinations as input for training. Results: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 AE 0.18, RE = 0.73 AE 0.18, PR = 0.71 AE 0.14, CNNs: DSC = 0.81 AE 0.13, RE = 0.83 AE 0.14, PR = 0.82 AE 0.10, MA: DSC = 0.71 AE 0.22, RE = 0.70 AE 0.34, PR = 0.77 AE 0.15. Mean surface distance metrics for all the segmented structures were: CFs: ASD = 13.5 AE 11.3 mm, RMSSD = 34.6 AE 37.6 mm and HD = 185.7 AE 194.0 mm, CNNs; ASD = 5.48 AE 4.84 mm, RMSSD = 17.0 AE 13.3 mm and HD = 199.0 AE 101.2 mm, MA: ASD = 4.22 AE 2.42 mm, RMSSD = 6.13 AE 2.55 mm, and HD = 38.9 AE 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder. Conclusions: Three state-of-the-art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: A Healthy Volunteers and Imaging Protocolmentioning

confidence: 99%

Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi‐atlas (MA) approach

Lavdas¹,

Glocker

Kamnitsas

et al. 2017

Medical Physics

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, DWIBS images suffer from increased geometric distortions, combined with chemical shift artifacts and larger voxel size. Hence, the majority of DWIBS protocols are currently performed at 1.5T for whole‐body imaging, necessitating longer scan times to compensate for the reduced SNR . Compared to DWIBS, our DETECT imaging technique provided images with superior SNR and higher spatial resolution in shorter scan times at 3T.…”

Section: Discussionmentioning

confidence: 99%

Whole‐body MRI for metastatic cancer detection using T₂‐weighted imaging with fat and fluid suppression

Wang

Pirasteh

Brugarolas

et al. 2018

Magnetic Resonance in Med

View full text Add to dashboard Cite

show abstract

“…Different ADC values have been reported in different anatomical sites, including bone marrow, iliac marrow, femoral neck, skull, and calcaneus, with decreasing values from vertebral bodies throughout the upper to bottom spine towards femur and calcaneus. Negative correlation has been observed between ADC values and incrementing age and fat fraction in yellow bone marrow, and positive correlation between ADC and red bone marrow (with higher cellularity and less fat than yellow marrow) . Furthermore, significantly higher ADC values have been reported in female healthy subjects in comparison with healthy male subjects …”

Section: Diffusion Mri In the Study Of Bone Aging And Osteoporosismentioning

confidence: 91%

“…Negative correlation has been observed between ADC values and incrementing age and fat fraction in yellow bone marrow, 57 and positive correlation between ADC and red bone marrow (with higher cellularity and less fat than yellow marrow). 62 Furthermore, significantly higher ADC values have been reported in female healthy subjects in comparison with healthy male subjects. 63 During osteoporosis, two counteracting phenomena take place: marrow fat content increases, resulting in reduced ADC, while deterioration of the solid matrix of trabecular bone causes pore enlargement and connection of the neighboring pores, which results in increased ADC.…”

Section: Diffusion Mri In the Study Of Bone Aging And Osteoporosismentioning

confidence: 99%

Diffusion MRI for Assessment of Bone Quality; A Review of Findings in Healthy Aging and Osteoporosis

Kazerooni

Pozo

McCloskey

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Diffusion MRI (dMRI) is a growing imaging technique with the potential to provide biomarkers of tissue variation, such as cellular density, tissue anisotropy, and microvascular perfusion. However, the role of dMRI in characterizing different aspects of bone quality, especially in aging and osteoporosis, has not yet been fully established, particularly in clinical applications. The reason lies in the complications accompanied with implementation of dMRI in assessment of human bone structure, in terms of acquisition and quantification. Bone is a composite tissue comprising different elements, each contributing to the overall quality and functional competence of bone. As diffusion is a critical biophysical process in biological tissues, early changes of tissue microstructure and function can affect diffusive properties of the tissue. While there are multiple MRI methods to detect variations of individual properties of bone quality due to aging and osteoporosis, dMRI has potential to serve as a superior method for characterizing different aspects of bone quality within the same framework but with higher sensitivity to early alterations. This is mainly because several properties of the tissue including directionality and anisotropy of trabecular bone and cell density can be collected using only dMRI. In this review article, we first describe components of human bone that can be potentially detected by their diffusivity properties and contribute to variations in bone quality during aging and osteoporosis. Then we discuss considerations and challenges of dMRI in bone imaging, current status, and suggestions for development of dMRI in research studies and clinics to segregate different contributing components of bone quality in an integrated acquisition. Level of Evidence: 5 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:975–992.

show abstract

Apparent Diffusion Coefficient of Normal Abdominal Organs and Bone Marrow From Whole-Body DWI at 1.5 T: The Effect of Sex and Age

Cited by 59 publications

References 23 publications

Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi‐atlas (MA) approach

Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi‐atlas (MA) approach

Whole‐body MRI for metastatic cancer detection using T₂‐weighted imaging with fat and fluid suppression

Diffusion MRI for Assessment of Bone Quality; A Review of Findings in Healthy Aging and Osteoporosis

Contact Info

Product

Resources

About

Apparent Diffusion Coefficient of Normal Abdominal Organs and Bone Marrow From Whole-Body DWI at 1.5 T: The Effect of Sex and Age

Cited by 59 publications

References 23 publications

Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi‐atlas (MA) approach

Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi‐atlas (MA) approach

Whole‐body MRI for metastatic cancer detection using T2‐weighted imaging with fat and fluid suppression

Diffusion MRI for Assessment of Bone Quality; A Review of Findings in Healthy Aging and Osteoporosis

Contact Info

Product

Resources

About

Whole‐body MRI for metastatic cancer detection using T₂‐weighted imaging with fat and fluid suppression