Amyloid PET Quantification Via End-to-End Training of a Deep Learning

Kim, Jiyoung; Suh, Hoon Young; Ryoo, Hyun Gee; Oh, Dongkyu; Choi, Hongyoon; Paeng, Jin Chul; Cheon, Gi Jeong; Kang, Keon Wook; Lee, Dong Hoon; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.1007/s13139-019-00610-0

Cited by 27 publications

(19 citation statements)

References 27 publications

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“…In most areas of nuclear medicine, DL-based image processing and analysis techniques have received significant research attention [184,195,196]; namely, the DL-based image reconstruction and denoising for radiation dose reduction [197][198][199][200], automatic segmentation of various organs and structures for quantitative image analyses [201,202], image spatial normalization [203,204], voxel-based internal dosimetry [205], and the image-to-image transition for PET AC. Moreover, the DL-based lesion detection and image interpretation received significant research attention [206][207][208][209].…”

Section: Artificial Intelligence In Nuclear Medicinementioning

confidence: 99%

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Lee

2021

IEEE Trans. Radiat. Plasma Med. Sci.

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Attenuation correction (AC) is essential for the generation of artifact-free and quantitaAtively accurate positron emission tomography (PET) images. PET AC based on computed tomography (CT) frequently results in artifacts in attenuationcorrected PET images, and these artifacts mainly originate from CT artifacts and PET-CT mismatches. The AC in PET combined with a magnetic resonance imaging (MRI) scanner (PET/MRI) is more complex than PET/CT, given that MR images do not provide direct information on high energy photon attenuation. Deep learning (DL)-based methods for the improvement of PET AC have received significant research attention as alternatives to conventional AC methods. Many DL studies were focused on the transformation of MR images into synthetic pseudo-CT or attenuation maps. Alternative approaches that are not dependent on the anatomical images (CT or MRI) can overcome the limitations related to current CT-and MRI-based ACs and allow for more accurate PET quantification in stand-alone PET scanners for the realization of low radiation doses. In this article, a review is presented on the limitations of the PET AC in current dual-modality PET/CT and PET/MRI scanners, in addition to the current status and progress of DL-based approaches, for the realization of improved performance of PET AC.

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Section: Artificial Intelligence In Nuclear Medicinementioning

confidence: 99%

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Lee

2021

IEEE Trans. Radiat. Plasma Med. Sci.

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“…Our best regression model (0.0466 MAE loss) achieved an accuracy of 96.1%. Our proposed regression model (EfficientV2S and LightGBM) improved by 22.3% compared to the network in the Kim et al (2019) study and improved by 13.7% compared to the Reith et al (2020) study.…”

Section: Discussionmentioning

confidence: 87%

Using Deep Learning to Determine Amyloid Deposition through PET and Clinical Data for Alzheimer’s Prognosis

Maddury

Desai

2022

Preprint

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Amyloid deposition is a vital biomarker in the process of Alzheimer's diagnosis. Florbetapir PET scans can provide valuable imaging data to determine cortical amyloid quantities. However the process is labor and doctor intensive, requiring extremely specialized education and resources that may not be accessible to everyone, making the amyloid calculation process inefficient. Deep learning is a rising tool in Alzheimer's research which could be used to determine amyloid deposition. Using data from the Alzheimer's Disease Neuroimaging Initiative, we identified 2980 patients with PET imaging, clinical, and genetic data. We tested various ResNet and EfficientNet convolutional neural networks and later combined them with Gradient Boosting Decision Tree algorithms to predict standardized uptake value ratio (SUVR) of amyloid in each patient session. We tried several configurations to find the best model tuning for regression-to-SUVR. We found that the EfficientNetV2-Small architecture combined with a grid search-tuned Gradient Boosting Decision Tree with 3 axial input slices and clinical and genetic data achieved the lowest loss. Using the mean-absolute-error metric, the loss converged to an MAE of 0.0466, equating to 96.11% accuracy across the 596 patient test set. We showed that this method is more consistent and accessible in comparison to human readers from previous studies, with lower margins of error and substantially faster calculation times. Deep learning algorithms could be used in hospitals and clinics with resource limitations for amyloid deposition, and shows promise for more imaging tasks as well.

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“…It also facilitates the assessment of the relationships of measures to well-described pathological and clinical stages of the disease. Other approaches combine different amyloid tracers [ 40 ], are mixed with other PET targets to achieve higher accuracy [ 41 ], or focus on visually equivocal cases [ 39 ] and use the standard cortical SUVr mask, which our spatial model outperforms.…”

Section: Discussionmentioning

confidence: 99%

Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support

et al. 2021

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Background Amyloid-β (Aβ) PET has emerged as clinically useful for more accurate diagnosis of patients with cognitive decline. Aβ deposition is a necessary cause or response to the cellular pathology of Alzheimer’s disease (AD). Usual clinical and research interpretation of amyloid PET does not fully utilise all information regarding the spatial distribution of signal. We present a data-driven, spatially informed classifier to boost the diagnostic power of amyloid PET in AD. Methods Voxel-wise k-means clustering of amyloid-positive voxels was performed; clusters were mapped to brain anatomy and tested for their associations by diagnostic category and disease severity with 758 amyloid PET scans from volunteers in the AD continuum from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A machine learning approach based on this spatially constrained model using an optimised quadratic support vector machine was developed for automatic classification of scans for AD vs non-AD pathology. Results This classifier boosted the accuracy of classification of AD scans to 81% using the amyloid PET alone with an area under the curve (AUC) of 0.91 compared to other spatial methods. This increased sensitivity to detect AD by 15% and the AUC by 9% compared to the use of a composite region of interest SUVr. Conclusions The diagnostic classification accuracy of amyloid PET was improved using an automated data-driven spatial classifier. Our classifier highlights the importance of considering the spatial variation in Aβ PET signal for optimal interpretation of scans. The algorithm now is available to be evaluated prospectively as a tool for automated clinical decision support in research settings.

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Amyloid PET Quantification Via End-to-End Training of a Deep Learning

Cited by 27 publications

References 27 publications

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Using Deep Learning to Determine Amyloid Deposition through PET and Clinical Data for Alzheimer’s Prognosis

Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support

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