Technical Note: Deep learning based <scp>MRAC</scp> using rapid ultrashort echo time imaging

Jang, Hyungseok; Liu, Fang; Zhao, Gengyan; Bradshaw, Tyler; McMillan, A.

doi:10.1002/mp.12964

Cited by 55 publications

(66 citation statements)

References 52 publications

(75 reference statements)

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“…Although the applied U‐Net is widely accepted for mapping image contrast and texture as a results of efficient convolutional encoding and decoding design, successfully translation among different anatomy might still require further design of the generator architecture tailored for handling both contrast translation and geometric transformation. In addition, translating image contrasts among different imaging modalities would be useful in applications such as PET/MR attenuation correction, where there is the need to generate synthetic CT images from MR images for photon attenuation calculation …”

Section: Discussionmentioning

confidence: 99%

“…In addition, translating image contrasts among different imaging modalities would be useful in applications such as PET/MR attenuation correction, where there is the need to generate synthetic CT images from MR images for photon attenuation calculation. 63,64 In the current study, the use of adversarial training and cycle consistency regularization as key techniques in the CycleGAN framework was proven to be effective to learn mutually correlated image features with unpaired data in the medical image domain. The adversarial learning ensures the translated images falling into the same data distribution of the target images; the cycle consistency prevents the degeneracy of the adversarial process from generating hallucinated image features.…”

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confidence: 89%

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SUSAN: segment unannotated image structure using adversarial network

Liu

2018

Magnetic Resonance in Med

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Purpose To describe and evaluate a segmentation method using joint adversarial and segmentation convolutional neural network to achieve accurate segmentation using unannotated MR image datasets. Theory and Methods A segmentation pipeline was built using joint adversarial and segmentation network. A convolutional neural network technique called cycle‐consistent generative adversarial network (CycleGAN) was applied as the core of the method to perform unpaired image‐to‐image translation between different MR image datasets. A joint segmentation network was incorporated into the adversarial network to obtain additional functionality for semantic segmentation. The fully automated segmentation method termed as SUSAN was tested for segmenting bone and cartilage on 2 clinical knee MR image datasets using images and annotated segmentation masks from an online publicly available knee MR image dataset. The segmentation results were compared using quantitative segmentation metrics with the results from a supervised U‐Net segmentation method and 2 registration methods. The Wilcoxon signed‐rank test was used to evaluate the value difference of quantitative metrics between different methods. Results The proposed method SUSAN provided high segmentation accuracy with results comparable to the supervised U‐Net segmentation method (most quantitative metrics having P > 0.05) and significantly better than a multiatlas registration method (all quantitative metrics having P < 0.001) and a direct registration method (all quantitative metrics having P< 0.0001) for the clinical knee image datasets. SUSAN also demonstrated the applicability for segmenting knee MR images with different tissue contrasts. Conclusion SUSAN performed rapid and accurate tissue segmentation for multiple MR image datasets without the need for sequence specific segmentation annotation. The joint adversarial and segmentation network and training strategy have promising potential applications in medical image segmentation.

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Section: Discussionmentioning

confidence: 99%

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confidence: 89%

SUSAN: segment unannotated image structure using adversarial network

Liu

2018

Magnetic Resonance in Med

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“…Inspired by the network design in Ref., we utilized the deep convolutional encoder‐decoder network structure shown in Fig. , which is capable of mapping pixel‐wise image intensity from MRI to CT in multiple image scales.…”

Section: Methodsmentioning

confidence: 99%

“…As a result, three‐discrete labels were assigned to soft tissue, air and bone in the generated pseudo CTs that delivered accurate PET/MR attenuation correction with significantly reduced error in reconstructed PET images compared with existing segmentation‐based and atlas‐based methods . In one recent study from the same group, the deep learning approach was applied in combination with an advanced UTE sequence, which achieved reliable and accurate tissue identification for bone in PET/MR attenuation correction in brain imaging . Another recent study demonstrated excellent performance utilizing deep learning generated pseudo CTs for PET/MR attenuation correction in pelvis…”

Section: Introductionmentioning

confidence: 99%

MR‐based treatment planning in radiation therapy using a deep learning approach

Liu

Yadav

Baschnagel

et al. 2019

J Applied Clin Med Phys

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Purpose To develop and evaluate the feasibility of deep learning approaches for MR‐based treatment planning (deepMTP) in brain tumor radiation therapy. Methods and materials A treatment planning pipeline was constructed using a deep learning approach to generate continuously valued pseudo CT images from MR images. A deep convolutional neural network was designed to identify tissue features in volumetric head MR images training with co‐registered kVCT images. A set of 40 retrospective 3D T1‐weighted head images was utilized to train the model, and evaluated in 10 clinical cases with brain metastases by comparing treatment plans using deep learning generated pseudo CT and using an acquired planning kVCT. Paired‐sample Wilcoxon signed rank sum tests were used for statistical analysis to compare dosimetric parameters of plans made with pseudo CT images generated from deepMTP to those made with kVCT‐based clinical treatment plan (CTTP). Results deepMTP provides an accurate pseudo CT with Dice coefficients for air: 0.95 ± 0.01, soft tissue: 0.94 ± 0.02, and bone: 0.85 ± 0.02 and a mean absolute error of 75 ± 23 HU compared with acquired kVCTs. The absolute percentage differences of dosimetric parameters between deepMTP and CTTP was 0.24% ± 0.46% for planning target volume (PTV) volume, 1.39% ± 1.31% for maximum dose and 0.27% ± 0.79% for the PTV receiving 95% of the prescribed dose (V95). Furthermore, no significant difference was found for PTV volume (P = 0.50), the maximum dose (P = 0.83) and V95 (P = 0.19) between deepMTP and CTTP. Conclusions We have developed an automated approach (deepMTP) that allows generation of a continuously valued pseudo CT from a single high‐resolution 3D MR image and evaluated it in partial brain tumor treatment planning. The deepMTP provided dose distribution with no significant difference relative to a kVCT‐based standard volumetric modulated arc therapy plans.

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“…We previously used a convolutional encoder-decoder (CED) network for PET/MR attenuation correction in the brain, where contrast-enhanced T1-weighted MR images were used as network inputs to achieve reconstructed PET errors of ∼1% in the brain ( 17 ). We also evaluated the same CED network but with a UTE image as input and using transfer learning to initialize the network weights ( 18 ). This method achieved even better results, with PET errors in the brain generally <1%.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Deep Learning–Based PET/MR Attenuation Correction in the Pelvis Using Only Diagnostic MR Images

et al. 2018

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This study evaluated the feasibility of using only diagnostically relevant magnetic resonance (MR) images together with deep learning for positron emission tomography (PET)/MR attenuation correction (deepMRAC) in the pelvis. Such an approach could eliminate dedicated MRAC sequences that have limited diagnostic utility but can substantially lengthen acquisition times for multibed position scans. We used axial T2 and T1 LAVA Flex magnetic resonance imaging images that were acquired for diagnostic purposes as inputs to a 3D deep convolutional neural network. The network was trained to produce a discretized (air, water, fat, and bone) substitute computed tomography (CT) (CTsub). Discretized (CTref-discrete) and continuously valued (CTref) reference CT images were created to serve as ground truth for network training and attenuation correction, respectively. Training was performed with data from 12 subjects. CTsub, CTref, and the system MRAC were used for PET/MR attenuation correction, and quantitative PET values of the resulting images were compared in 6 test subjects. Overall, the network produced CTsub with Dice coefficients of 0.79 ± 0.03 for cortical bone, 0.98 ± 0.01 for soft tissue (fat: 0.94 ± 0.0; water: 0.88 ± 0.02), and 0.49 ± 0.17 for bowel gas when compared with CTref-discrete. The root mean square error of the whole PET image was 4.9% by using deepMRAC and 11.6% by using the system MRAC. In evaluating 16 soft tissue lesions, the distribution of errors for maximum standardized uptake value was significantly narrower using deepMRAC (−1.0% ± 1.3%) than using system MRAC method (0.0% ± 6.4%) according to the Brown–Forsy the test (P < .05). These results indicate that improved PET/MR attenuation correction can be achieved in the pelvis using only diagnostically relevant MR images.

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Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging

Abstract: The proposed MRAC method utilizing deep learning with transfer learning and an efficient dRHE acquisition enables reliable PET quantitation with accurate and rapid pseudo CT generation.

Cited by 55 publications

References 52 publications

SUSAN: segment unannotated image structure using adversarial network

SUSAN: segment unannotated image structure using adversarial network

MR‐based treatment planning in radiation therapy using a deep learning approach

Feasibility of Deep Learning–Based PET/MR Attenuation Correction in the Pelvis Using Only Diagnostic MR Images

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