Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC)

Shiri, Isaac; Ghafarian, Pardis; Geramifar, Parham; Leung, Kevin; Ghelich-Oghli, Mostafa; Oveisi, Mehrdad; Rahmim, Arman; Ay, Mohammad Reza

doi:10.1007/s00330-019-06229-1

Cited by 83 publications

(61 citation statements)

References 38 publications

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“…In this study, we followed the IBSI protocol to resample all images to a constant voxel size to reduce feature sensitivity to variable image generation parameters (36), also ensuring the robustness and reproducibility of the study (45). Having a dataset of images generated using varying data acquisition (46,47), reconstruction (48),processing (49) and segmentation (50) results produce inconsistencies in feature evaluation.…”

Section: Discussionmentioning

confidence: 99%

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Shiri¹,

Maleki²,

Hajianfar³

et al. 2020

Mol Imaging Biol

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Conflicts of Interest:The authors declare no potential conflicts of interest. Abstract PurposeConsiderable progress has been made in assessment and management of NSCLC patients based on mutation status in the EGFR and KRAS. At the same time, NSCLC management through KRAS and EGFR mutation profiling faces some challenges. In the present work, we aimed to evaluate a comprehensive radiomics framework that enabled prediction of EGFR and KRAS mutation status in NSCLC cancer patients based on low dose CT, diagnostic CT, PET modalities radiomic features and machine learning (ML) algorithms. MethodsOur study involved NSCLC cancer patient with 186 PET, and 175 low dose CT and CTD images. More than twenty thousand radiomic features from different image-feature sets were extracted. Conventional clinically used standard uptake value (SUV) parameters were also obtained for PET images. Feature value was normalized to obtain Z-scores, followed by student t-test students for comparison, high correlated features were eliminated and the False discovery rate (FDR) correction were performed and q-value were reported for univariate analysis. Six feature selection methods and twelve classifiers were used to predict gene status in patient. We performed 10-fold cross-validation for model tuning to improve robustness and all model evaluation was reported on independent validation sets (68 patients). The mean area under the receiver operator characteristic (ROC) curve (AUC) was obtained for performance evaluation. ResultsThe best predictive power of conventional PET parameters was achieved by SUVpeak (AUC: 0.69, P-value = 0.0002) and MTV (AUC: 0.55, P-value = 0.0011) for EGFR and KRAS, respectively. Univariate analysis of extracted radiomics features improved prediction power up to AUC: 75 (q-value: 0.003, Short Run Emphasis feature of GLRLM from LOG preprocessed image of PET with sigma value 1.5) and AUC: 0.71 (q-value 0.00005, The Large Dependence Low Gray Level Emphasis from GLDM in LOG preprocessed image of CTD sigma value 5) for EGFR and KRAS, respectively. Furthermore, the machine learning algorithm improved the perdition power up to AUC: 0.82 for EGFR (LOG preprocessed of PET image set with sigma 3 with VT feature selector and SGD classifier) and AUC: 0.83 for KRAS (CT image set with sigma 3.5 with SM feature selector and SGD classifier). ConclusionOur findings demonstrated that non-invasive and reliable radiomics analysis can be successfully used to predict EGFR and KRAS mutation status in NSCLC patients. We demonstrated that radiomic features extracted from different image-feature sets could be used for EGFR and KRAS mutation status prediction in NSCLC patients, and showed that they have more predictive power than conventional imaging parameters.This study was conducted on 211 NSCLC cancer patients with available imaging and genomic data. Imaging modalities, including diagnostic CT (CTD) and PET/CT (i.e., low-dose CT (CT) used for PET attenuation correction and the PET image) for all patients were obtained (29-32) . All images we...

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Section: Discussionmentioning

confidence: 99%

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Shiri¹,

Maleki²,

Hajianfar³

et al. 2020

Mol Imaging Biol

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“…Radiomics is a new era of science which faces many challenges, including image acquisition (22), reconstruction (23,24), processing (25), and model development (26,27) to provide robust and reproducible models. Previous studies have shown that the radiomics signature is valuable for differentiating high/low grade ccRCC tumors (28,29).…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

et al. 2020

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To identify optimal classification methods for computed tomography (CT) radiomicsbased preoperative prediction of clear cells renal cell carcinoma (ccRCC) grade.Methods and material: Seventy-one ccRCC patients (31 low-grade and 40 high-grade) were included in the study. All tumors were segmented manually on CT images, and three image preprocessing techniques (Laplacian of Gaussian, wavelet filter, and discretization of the intensity values) were applied on tumor volumes. In total, 2530 radiomics features (tumor shape and size, intensity statistics, and texture) were extracted from each segmented tumor volume. Univariate analysis was performed to assess the association of each feature with the histological condition. In the case of multivariate analysis, the following was implemented: three feature selection including the least absolute shrinkage and selection operator (LASSO), student's t-test and minimum Redundancy Maximum Relevance (mRMR) algorithms. These selected features were then used to construct three classification models (SVM, random forest, and logistic regression) to discriminate the high from low-grade ccRCC at nephrectomy. Lastly, multivariate model performance was evaluated on the bootstrapped validation cohort using the area under receiver operating characteristic curve (AUC).Results: Univariate analysis demonstrated that among different image sets, 128 bin discretized images have statistically significant different (q-value < 0.05) texture parameters with a mean of AUC 0.74±3 (q-value < 0.05). The three ML-based classifier shows proficient discrimination of the high from low-grade ccRCC. The AUC was 0.78 in logistic regression, 0.62 in random forest, and 0.83 in SVM model, respectively. Conclusion:Radiomics features can be a useful and promising non-invasive method for preoperative evaluation of ccRCC Fuhrman grades.

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“…Recent works focused on the generation of pseudo-CT images from T1-weighted [16,21,22], ultra-short echo time (UTE) [23], zero echo time (ZTE) [24] and Dixon [25] MR sequences for AC of 18 F-FDG PET images in the brain and pelvic regions. Two closely related but independent works reported on the direct conversion of non-attenuation corrected brain 18 F-FDG PET images to the attenuation corrected image using convolutional encoder-decoder neural networks [20,26].…”

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Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network

Shiri

Arabi

Geramifar

et al. 2020

Eur J Nucl Med Mol Imaging

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115

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Objective We demonstrate the feasibility of direct generation of attenuation and scatter-corrected images from uncorrected images (PET-nonASC) using deep residual networks in whole-body 18 F-FDG PET imaging. Methods Two-and three-dimensional deep residual networks using 2D successive slices (DL-2DS), 3D slices (DL-3DS) and 3D patches (DL-3DP) as input were constructed to perform joint attenuation and scatter correction on uncorrected whole-body images in an end-to-end fashion. We included 1150 clinical whole-body 18 F-FDG PET/CT studies, among which 900, 100 and 150 patients were randomly partitioned into training, validation and independent validation sets, respectively. The images generated by the proposed approach were assessed using various evaluation metrics, including the root-mean-squared-error (RMSE) and absolute relative error (ARE %) using CT-based attenuation and scatter-corrected (CTAC) PET images as reference. PET image quantification variability was also assessed through voxel-wise standardized uptake value (SUV) bias calculation in different regions of the body (head, neck, chest, liver-lung, abdomen and pelvis). Results Our proposed attenuation and scatter correction (Deep-JASC) algorithm provided good image quality, comparable with those produced by CTAC. Across the 150 patients of the independent external validation set, the voxel-wise REs (%) were − 1.72 ± 4.22%, 3.75 ± 6.91% and − 3.08 ± 5.64 for DL-2DS, DL-3DS and DL-3DP, respectively. Overall, the DL-2DS approach led to superior performance compared with the other two 3D approaches. The brain and neck regions had the highest and lowest RMSE values between Deep-JASC and CTAC images, respectively. However, the largest ARE was observed in the chest (15.16 This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence)

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Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC)

Cited by 83 publications

References 38 publications

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network

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