Deep Convolution Neural Network (DCNN) Multiplane Approach to Synthetic CT Generation From MR images—Application in Brain Proton Therapy

Spadea, Maria Francesca; Pileggi, Giampaolo; Zaffino, Paolo; Salome, Patrick; Catana, Ciprian; Izquierdo‐Garcia, David; Amato, Francesco; Seco, Joao

doi:10.1016/j.ijrobp.2019.06.2535

Cited by 89 publications

(88 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More recent publications from the group at Emory University used a three‐dimensional (3D) cycleGAN to generate sCT images, and obtained an MAE of 51.32 ± 16.91 HU for pelvic sCT 24 and 72.87 ± 18.16 HU for liver sCT 25 . A parallel work from Spadea et al 26 achieved an MAE value of 54 ± 7 HU for an sCT generation method for brain patients using a deep convolutional neural network (DCNN) model. Note that the comparison with published results from different groups can be affected by differences between patient datasets (tumor location, patient characteristics, etc.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, the method proposed in this work enables a fast (1 s for sCT generation) and entirely automatic MRI‐only treatment planning process that removes all manual components from the workflow and achieves excellent dosimetric accuracy. A more recent study from Spadea et al 26 investigated the use of deep convolutional neural networks for sCT generation and also analyzed their dosimetric accuracy for single‐field uniform dose (SFUD) plans for brain tumor patients. In contrast, the present work investigated the dosimetric accuracy of the generated sCT for fully IMPT treatment planning, which is much more challenging than the case of SFUD due to the extra sensitivity of this technique to HU uncertainties.…”

Section: Discussionmentioning

confidence: 99%

“…Using the newly developed deep learning methods mentioned above could help to achieve higher accuracy while removing any manual operations. In the last year, several groups have started to investigate the application of deep learning for sCT generation, achieving very promising results 24‐27 . In addition, they analyzed the dosimetric accuracy of the generated sCT for single field uniform dose (SFUD) and conventional PTV optimization.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dosimetric evaluation of synthetic CT generated with GANs for MRI‐only proton therapy treatment planning of brain tumors

Kazemifar

Montero

Souris

et al. 2020

J Applied Clin Med Phys

View full text Add to dashboard Cite

Purpose The purpose of this study was to address the dosimetric accuracy of synthetic computed tomography (sCT) images of patients with brain tumor generated using a modified generative adversarial network (GAN) method, for their use in magnetic resonance imaging (MRI)‐only treatment planning for proton therapy. Methods Dose volume histogram (DVH) analysis was performed on CT and sCT images of patients with brain tumor for plans generated for intensity‐modulated proton therapy (IMPT). All plans were robustly optimized using a commercially available treatment planning system (RayStation, from RaySearch Laboratories) and standard robust parameters reported in the literature. The IMPT plan was then used to compute the dose on CT and sCT images for dosimetric comparison, using RayStation analytical (pencil beam) dose algorithm. We used a second, independent Monte Carlo dose calculation engine to recompute the dose on both CT and sCT images to ensure a proper analysis of the dosimetric accuracy of the sCT images. Results The results extracted from RayStation showed excellent agreement for most DVH metrics computed on the CT and sCT for the nominal case, with a mean absolute difference below 0.5% (0.3 Gy) of the prescription dose for the clinical target volume (CTV) and below 2% (1.2 Gy) for the organs at risk (OARs) considered. This demonstrates a high dosimetric accuracy for the generated sCT images, especially in the target volume. The metrics obtained from the Monte Carlo doses mostly agreed with the values extracted from RayStation for the nominal and worst‐case scenarios (mean difference below 3%). Conclusions This work demonstrated the feasibility of using sCT generated with a GAN‐based deep learning method for MRI‐only treatment planning of patients with brain tumor in intensity‐modulated proton therapy.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dosimetric evaluation of synthetic CT generated with GANs for MRI‐only proton therapy treatment planning of brain tumors

Kazemifar

Montero

Souris

et al. 2020

J Applied Clin Med Phys

View full text Add to dashboard Cite

show abstract

“…Therefore, in measurement-based evaluation, the supplementary decision of the clinician is important.Artificial intelligence (AI), especially deep learning using convolutional neural networks (CNNs), has become the most sought-after field [11][12][13][14]. With the development of computing hardware and graphics processing units, the computational processing speed has increased, complex calculations can be performed in a short time, and deep learning using deep convolutional neural networks (DCNNs) has been activated [15,16]. Particularly, the development of deep learning in the categorization of images is surprising, and in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) of 2015, it went beyond human perception [17].Various deep learning neural network models are being developed and applied to many fields.…”

mentioning

confidence: 99%

Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications

et al. 2020

View full text Add to dashboard Cite

The aim of this study was to evaluate the deep convolutional neural networks (DCNNs) based on analysis of cephalometric radiographs for the differential diagnosis of the indications of orthognathic surgery. Among the DCNNs, Modified-Alexnet, MobileNet, and Resnet50 were used, and the accuracy of the models was evaluated by performing 4-fold cross validation. Additionally, gradient-weighted class activation mapping (Grad-CAM) was used to perform visualized interpretation to determine which region affected the DCNNs’ class classification. The prediction accuracy of the models was 96.4% for Modified-Alexnet, 95.4% for MobileNet, and 95.6% for Resnet50. According to the Grad-CAM analysis, the most influential regions for the DCNNs’ class classification were the maxillary and mandibular teeth, mandible, and mandibular symphysis. This study suggests that DCNNs-based analysis of cephalometric radiograph images can be successfully applied for differential diagnosis of the indications of orthognathic surgery.

show abstract

“…An end-to-end test helps to determine if there are other requirements on the sCT (for instance, some systems expect square pixels of the (synthetic) CT). After training, one should establish strict inclusion and exclusion criteria for its use, which implies for example patients with dental implants causing signifi- [95,[104][105][106][107][108][109]. This minimum number also depends on how the sCT is situated in the workflow.…”

Section: Commissioningmentioning

confidence: 99%

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

Vandewinckele

Claessens

Dinkla

et al. 2020

Radiotherapy and Oncology

192

116

View full text Add to dashboard Cite

This work commenced during the 3rd European Society for Therapeutic Radiology and Oncology (ESTRO) physics workshop on 'Implementation/commissioning/QA of artificial intelligence techniques' in Budapest (2019) Radiotherapy and Oncology xxx (xxxx) xxx Contents lists available at ScienceDirect Radiotherapy and Oncology j o u r n a l h o m e p a g e : w w w. t h e g r e e n j o u r n a l. c o m Please cite this article as: L. Vandewinckele, M. Claessens, A. Dinkla et al., Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance, Radiotherapy and Oncology,

show abstract

Deep Convolution Neural Network (DCNN) Multiplane Approach to Synthetic CT Generation From MR images—Application in Brain Proton Therapy

Cited by 89 publications

References 34 publications

Dosimetric evaluation of synthetic CT generated with GANs for MRI‐only proton therapy treatment planning of brain tumors

Dosimetric evaluation of synthetic CT generated with GANs for MRI‐only proton therapy treatment planning of brain tumors

Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

Contact Info

Product

Resources

About