True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation

Chen, Kevin T.; Toueg, Tyler; Koran, Mary Ellen I.; Davidzon, Guido; Zeineh, Michael; Holley, Dawn; Gandhi, Harsh; Halbert, Kim; Boumis, Athanasia; Kennedy, Gabriel; Mormino, Elizabeth C.; Khalighi, Mehdi; Zaharchuk, Greg

doi:10.1007/s00259-020-05151-9

Cited by 36 publications

(17 citation statements)

References 38 publications

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“…Later, Wang et al (21) performed similar work by improving wholebody PET quality using a CNN with corresponding MR images. Previous works also showed the strength of CNN with a U-net structure in synthesizing high-quality PET images (22)(23)(24). There is a tendency for CNNs to produce blurry results (25), but generative adversarial networks (GANs) may solve this by using a structural loss (26).…”

Section: Related Workmentioning

confidence: 97%

High-quality PET image synthesis from ultra-low-dose PET/MRI using bi-task deep learning

Sun¹,

Jiang²,

Yuan³

et al. 2022

Quant Imaging Med Surg

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Background: Lowering the dose for positron emission tomography (PET) imaging reduces patients' radiation burden but decreases the image quality by increasing noise and reducing imaging detail and quantifications. This paper introduces a method for acquiring high-quality PET images from an ultra-lowdose state to achieve both high-quality images and a low radiation burden.Methods: We developed a two-task-based end-to-end generative adversarial network, named bi-c-GAN, that incorporated the advantages of PET and magnetic resonance imaging (MRI) modalities to synthesize high-quality PET images from an ultra-low-dose input. Moreover, a combined loss, including the mean absolute error, structural loss, and bias loss, was created to improve the trained model's performance.Real integrated PET/MRI data from 67 patients' axial heads (each with 161 slices) were used for training and validation purposes. Synthesized images were quantified by the peak signal-to-noise ratio (PSNR), normalized mean square error (NMSE), structural similarity (SSIM), and contrast noise ratio (CNR). The improvement ratios of these four selected quantitative metrics were used to compare the images produced by bi-c-GAN with other methods.Results: In the four-fold cross-validation, the proposed bi-c-GAN outperformed the other three selected methods (U-net, c-GAN, and multiple input c-GAN). With the bi-c-GAN, in a 5% low-dose PET, the image quality was higher than that of the other three methods by at least 6.7% in the PSNR, 0.6% in the SSIM, 1.3% in the NMSE, and 8% in the CNR. In the hold-out validation, bi-c-GAN improved the image quality compared to U-net and c-GAN in both 2.5% and 10% low-dose PET. For example, the PSNR using bi-C-GAN was at least 4.46% in the 2.5% low-dose PET and at most 14.88% in the 10% low-dose PET.Visual examples also showed a higher quality of images generated from the proposed method, demonstrating the denoising and improving ability of bi-c-GAN.Conclusions: By taking advantage of integrated PET/MR images and multitask deep learning (MDL), the proposed bi-c-GAN can efficiently improve the image quality of ultra-low-dose PET and reduce radiation exposure.

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Section: Related Workmentioning

confidence: 97%

High-quality PET image synthesis from ultra-low-dose PET/MRI using bi-task deep learning

Sun¹,

Jiang²,

Yuan³

et al. 2022

Quant Imaging Med Surg

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“…AI also achieved the amyloid PET staging (Kim et al 2020). The machine learning methods are potentially helpful in developing algorithms to get sufficient information from those ultra-low-count (to reduce the scan time) and ultra-low-dose (to reduce the injected radiotracer dose) amyloid PET, which could help to decrease image quality degradation caused by patient's head motion without sacrificing image quality and diagnostic power, enhancing the patient-throughput of imaging room (Chen et al 2019(Chen et al , 2020(Chen et al , 2021.…”

Section: Ai-based Analysesmentioning

confidence: 99%

International Nuclear Medicine Consensus on the Clinical Use of Amyloid Positron Emission Tomography in Alzheimer’s Disease

Tian

Zuo²,

Civelek³

et al. 2022

Phenomics

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Alzheimer’s disease (AD) is the main cause of dementia, with its diagnosis and management remaining challenging. Amyloid positron emission tomography (PET) has become increasingly important in medical practice for patients with AD. To integrate and update previous guidelines in the field, a task group of experts of several disciplines from multiple countries was assembled, and they revised and approved the content related to the application of amyloid PET in the medical settings of cognitively impaired individuals, focusing on clinical scenarios, patient preparation, administered activities, as well as image acquisition, processing, interpretation and reporting. In addition, expert opinions, practices, and protocols of prominent research institutions performing research on amyloid PET of dementia are integrated. With the increasing availability of amyloid PET imaging, a complete and standard pipeline for the entire examination process is essential for clinical practice. This international consensus and practice guideline will help to promote proper clinical use of amyloid PET imaging in patients with AD.

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“…Forty-eight participants (32 for the pretrained network presented in Chen et al 11 and 16 scanned with the true ultra-low-dose protocol presented in Chen et al; 7 diagnoses can be found in Table 1) were recruited for this study. The study was approved by the Stanford University institutional review board, and written informed consent for imaging was obtained from all participants or an authorized surrogate decision-maker.…”

Section: Patient Characteristicsmentioning

confidence: 99%

“…With the advent of deep learning-based machine learning methods such as convolutional neural networks (CNNs), it is possible to generate diagnostic-quality amyloid PET images using an actual ultra-low injected radiotracer dose and simultaneously acquired MR imaging inputs. 7 Because most patients with dementia undergo MR imaging routinely as part of their work-up to exclude a focal cause, a single PET/MR imaging examination provides a "one-stop shop" for functional and structural information. 8 In terms of logistics, a single scan also provides convenience and cost-effectiveness for both the imager and the imaged.…”

mentioning

confidence: 99%

Investigating Simultaneity for Deep Learning–Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging

Chen

Adeyeri

Toueg

et al. 2022

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-lowdose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging-assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions. MATERIALS AND METHODS:We recruited 48 participants: Thirty-two (20 women; mean, 67.7 [SD, 7.9] years) were used for pretraining; 328 (SD, 32) MBq of [ 18 F] florbetaben was injected. Sixteen participants (6 women; mean, 71.4 [SD. 8.7] years of age) were scanned in 2 sessions, with 6.5 (SD, 3.8) and 300 (SD, 14) MBq of [ 18 F] florbetaben injected, respectively. Structural MR imaging was acquired simultaneously with PET (90-110 minutes postinjection) on integrated PET/MR imaging in 2 sessions. Multiple U-Net-based deep networks were trained to create diagnostic PET images. For each method, training was done with the ultra-low-dose PET as input combined with MR imaging from either the ultra-low-dose session (simultaneous) or from the standard-dose PET session (nonsimultaneous). Image quality of the enhanced and ultra-low-dose PET images was evaluated using quantitative signal-processing methods, standardized uptake value ratio correlation, and clinical reads. RESULTS:Qualitatively, the enhanced images resembled the standard-dose image for both simultaneous and nonsimultaneous conditions. Three quantitative metrics showed significant improvement for all networks and no differences due to simultaneity. Standardized uptake value ratio correlation was high across different image types and network training methods, and 31/32 enhanced image pairs were read similarly.CONCLUSIONS: This work suggests that accurate amyloid PET images can be generated using enhanced ultra-low-dose PET and either nonsimultaneous or simultaneous MR imaging, broadening the utility of ultra-low-dose amyloid PET imaging.

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True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation

Cited by 36 publications

References 38 publications

High-quality PET image synthesis from ultra-low-dose PET/MRI using bi-task deep learning

High-quality PET image synthesis from ultra-low-dose PET/MRI using bi-task deep learning

International Nuclear Medicine Consensus on the Clinical Use of Amyloid Positron Emission Tomography in Alzheimer’s Disease

Investigating Simultaneity for Deep Learning–Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging

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