Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning

Wada, Akihiko; Saito, Yuya; Fujita, Shohei; Irie, Ryusuke; Akashi, Toshiaki; Sano, Katsuhiro; Kato, Shigeo; Ikenouchi, Yutaka; Hagiwara, Akifumi; Sato, Kanako; Tomizawa, Nobuo; Hayakawa, Yasushi; Kikuta, Jun; Kamagata, Koji; Suzuki, Michitaka; Hori, Masaaki; Nakanishi, Atsushi; Aoki, Shigeki

doi:10.2463/mrms.mp.2021-0068

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…The images were then resized to between 0.6 and 1, and a random crop was used to cut out images of size 448 × 448. The contrast of the original image was randomly changed from 0.8 to 1.2, and the brightness was also randomly changed from 0.8 to 1.2 17–19 . Gaussian noise was added, considering the signal‐to‐noise ratio (SNR) of T2WIs and DWIs.…”

Section: Methodsmentioning

confidence: 99%

“…The contrast of the original image was randomly changed from 0.8 to 1.2, and the brightness was also randomly changed from 0.8 to 1.2. [17][18][19] Gaussian noise was added, considering the signal-to-noise ratio (SNR) of T2WIs and DWIs. The spatial resolution remained unchanged between the input image and the teacher image.…”

Section: Creation Of Diverse Contrast Imagesmentioning

confidence: 99%

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Deep Learning‐Driven Transformation: A Novel Approach for Mitigating Batch Effects in Diffusion MRI Beyond Traditional Harmonization

Wada,

Akashi,

Hagiwara

et al. 2023

Magnetic Resonance Imaging

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Background“Batch effect” in MR images, due to vendor‐specific features, MR machine generations, and imaging parameters, challenges image quality and hinders deep learning (DL) model generalizability.PurposeWe aim to develop a DL model using contrast adjustment and super‐resolution to reduce diffusion‐weighted images (DWIs) diversity across magnetic field strengths and imaging parameters.Study TypeRetrospective.SubjectsThe DL model was built using an open dataset from one individual. The MR machine identification model was trained and validated on a dataset of 1134 adults (54% females, 46% males), with 1050 subjects showing no DWI abnormalities and 84 with conditions like stroke and tumors. The 21,000 images were divided into 80% for training, 20% for validation, and 3500 for testing.Field Strength/SequenceSeven MR scanners from four manufacturers with 1.5 T and 3 T magnetic field strengths. DWIs were acquired using spin‐echo sequences and high‐resolution T2WIs using the T2‐SPACE sequence.AssessmentAn experienced, board‐certified radiologist evaluated the effectiveness of restoring high‐resolution T2WI and harmonizing diverse DWI with metrics such as PSNR and SSIM, and the texture and frequency attributes were further analyzed using gray‐level co‐occurrence matrix and 1‐dimensional power spectral density. The model's impact on machine‐specific characteristics was gauged through the performance metrics of a ResNet‐50 model. Comprehensive statistical tests were employed for statistical robustness, including McNemar's test and the Dice index.ResultsOur DL protocol reduced DWI contrast and resolution variation. ResNet‐50 model's accuracy decreased from 0.9443 to 0.5786, precision from 0.9442 to 0.6494, recall from 0.9443 to 0.5786, and F1 score from 0.9438 to 0.5587. The t‐SNE visualization indicated more consistent image features across multiple MR devices. Autoencoder halved learning iterations; Dice coefficient >0.74 confirmed signal reproducibility in 84 lesions.ConclusionThis study presents a DL strategy to mitigate batch effects in diffusion MR images, improving their quality and generalizability.Evidence Level3Technical EfficacyStage 1

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Section: Methodsmentioning

confidence: 99%

Section: Creation Of Diverse Contrast Imagesmentioning

confidence: 99%

Deep Learning‐Driven Transformation: A Novel Approach for Mitigating Batch Effects in Diffusion MRI Beyond Traditional Harmonization

Wada,

Akashi,

Hagiwara

et al. 2023

Magnetic Resonance Imaging

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“…128,129 In recent years, deep learning, particularly CNN, has gained substantial popularity in the MRI field for image reconstruction, image quality improvement, image transfer, disease detection, tissue segmentation, and classification. [130][131][132][133][134][135][136][137][138][139][140][141][142] Multiparametric MRI has been used for analyses using deep learning. Multiparametric MRI is potentially useful for lesion segmentation to fully capture the extent of the disease and has been used to segment cancers and multiple sclerosis plaques.…”

Section: Deep Learningmentioning

confidence: 99%

“…Convolutional neural network (CNN) is a type of deep learning known to be useful for image tasks, with its architecture resembling that of the human visual cortex 128,129 . In recent years, deep learning, particularly CNN, has gained substantial popularity in the MRI field for image reconstruction, image quality improvement, image transfer, disease detection, tissue segmentation, and classification 130–142 …”

Section: Analyzing Multiparametric Mr Imagesmentioning

confidence: 99%

Multiparametric MRI

et al. 2023

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With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.

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“…To assess the differences among the number of training images, the composite images were randomly selected as a set of 100, 200, 400, and 600 images. Then, each set of composite images was augmented by applying left-right flip, scaling (0.95 and 1.05 magnification), and rotation (+ 15° and − 15°) 19,20 . Consequently, each set of 600, 1200, 2400, and 3600 composite images was provided.…”

Section: Deep Learningmentioning

confidence: 99%

Automatic detection of punctate white matter lesions in infants using deep learning of composite images from two cases

Sun

Niwa

Okazaki

et al. 2023

Sci Rep

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Punctate white matter lesions (PWMLs) in infants may be related to neurodevelopmental outcomes based on the location or number of lesions. This study aimed to assess the automatic detectability of PWMLs in infants on deep learning using composite images created from several cases. To create the initial composite images, magnetic resonance (MR) images of two infants with the most PWMLs were used; their PWMLs were extracted and pasted onto MR images of infants without abnormality, creating many composite PWML images. Deep learning models based on a convolutional neural network, You Only Look Once v3 (YOLOv3), were constructed using the training set of 600, 1200, 2400, and 3600 composite images. As a result, a threshold of detection probability of 20% and 30% for all deep learning model sets yielded a relatively high sensitivity for automatic PWML detection (0.908–0.957). Although relatively high false-positive detections occurred with the lower threshold of detection probability, primarily, in the partial volume of the cerebral cortex (≥ 85.8%), those can be easily distinguished from the white matter lesions. Relatively highly sensitive automatic detection of PWMLs was achieved by creating composite images from two cases using deep learning.

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Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning

Cited by 6 publications

References 30 publications

Deep Learning‐Driven Transformation: A Novel Approach for Mitigating Batch Effects in Diffusion MRI Beyond Traditional Harmonization

Deep Learning‐Driven Transformation: A Novel Approach for Mitigating Batch Effects in Diffusion MRI Beyond Traditional Harmonization

Multiparametric MRI

Automatic detection of punctate white matter lesions in infants using deep learning of composite images from two cases

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