Dedicated brain positron emission tomography (PET) devices can provide higher-resolution images with much lower doses compared to conventional whole-body PET systems, which is important to support PET neuroimaging and particularly useful for the diagnosis of neurodegenerative diseases. However, when a dedicated brain PET scanner does not come with a combined CT or transmission source, there is no direct solution for accurate attenuation and scatter correction, both of which are critical for quantitative PET. To address this problem, we propose joint attenuation and scatter correction (ASC) in image space for non-corrected PET (PET _NC ) using deep convolutional neural networks (DCNNs). This approach is a one-step process, distinct from conventional methods that rely on generating attenuation maps first that are then applied to iterative scatter simulation in sinogram space . For training and validation, time-of-flight PET/MR scans and additional helical CTs were performed for 35 subjects (25/10 split for training and test dataset). A DCNN model was proposed and trained to convert PET _NC to DCNN-based ASC PET (PET _DCNN ) directly in image space . For quantitative evaluation, uptake differences between PET _DCNN and reference CT-based ASC PET (PET _CT-ASC ) were computed for 116 automated anatomical labels (AALs) across 10 test subjects (1160 regions in total). MR-based ASC PET (PET _MR-ASC ), a current clinical protocol in PET/MR imaging, was another reference for comparison. Statistical significance was assessed using a paired t test. The performance of PET _DCNN was comparable to that of PET _MR-ASC , in comparison to reference PET _CT-ASC . The mean SUV differences (mean ± SD) from PET _CT-ASC were 4.0% ± 15.4% ( P < 0.001) and −4.2% ± 4.3% ( P < 0.001) for PET _DCNN and PET _MR-ASC , respectively. The overall larger variation of PET _DCNN (15.4%) was prone to the subject with the highest mean difference (48.5% ± 10.4%). The mean difference of PET _DCNN excluding the subject was substantially improved to −0.8% ± 5.2% ( P < 0.001), which was lower than that of PET _MR-ASC (−5.07% ± 3.60%, P < 0.001). In conclusion, we demonstrated the feasibility of directly producing PET images corrected for attenuation and scatter using a DCNN (PET _DCNN ) from PET _NC in image space without requiring conventional attenuation map generation and time-consuming scatter correction. Additionally, our DCNN-based method provides a possible alternative to MR-ASC for simultaneous PET/MRI.

Purpose: Dedicated cardiac SPECT scanners with cadmium-zinc-telluride (CZT) cameras have shown capabilities of shortened scan times or reduced radiation doses as well as improved image quality. Since most of the dedicated scanners do not have an integrated CT, image quantification with attenuation correction (AC) is challenging and artifacts are routinely encountered in daily clinical practice. In this work, we demonstrate a direct AC technique using deep learning (DL) for myocardial perfusion imaging (MPI). Methods: In an IRB-approved retrospective study, 100 cardiac SPECT/CT datasets with ^99mTc-tetrofosmin using a GE Discovery NM/CT 570c scanner were collected at the Yale New Haven Hospital. A U-Net-based network was used for generating attenuation-corrected SPECT (SPECT_DL) directly from non-corrected SPECT (SPECT_NC) without undergoing an additional image reconstruction step. The accuracy of SPECT_DL was evaluated by voxel-wise and segment-wise analyses against the reference CT-based AC (SPECT_CTAC) using American Heart Association 17 segments in the myocardium. Polar maps of representative (best/median/worst) cases were visually compared for illustrating potential benefits and pitfalls of the DL approach. Results: The voxel-wise correlations with SPECT_CTAC were 92.2% ± 3.7 (slope = 0.87; R2 = 0.81) and 97.7% ± 1.8 (slope = 0.94; R2 = 0.91) for SPECT_NC and SPECT_DL, respectively. The segmental errors of SPECT_NC scattered from -35% up to 21% (P < 0.001); while, the errors of SPECT_DL stayed mostly within ±10% (P < 0.001). The average segmental errors (mean±SD) were -6.11 ± 8.06% and 0.49 ± 4.35% for SPECT_NC and SPECT_DL, respectively. The average absolute segmental errors were 7.96 ± 6.23% and 3.31 ± 2.87% for SPECT_NC and SPECT_DL, respectively. Review of polar maps revealed successful demonstration of reduced attenuation artifacts; however, the performance of SPECT_DL was not consistent for all subjects likely due to different amount of attenuation and uptake patterns. Conclusion: We demonstrated the feasibility of direct AC using DL for SPECT MPI. Overall, our DL approach reduced attenuation artifacts substantially compared to SPECT_NC, justifying further studies to establish safety and consistency for clinical applications in stand-alone SPECT systems suffered from attenuation artifacts.

“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review ...

In brain PET/MRI, the major challenge of zero-echo-time (ZTE)–based attenuation correction (ZTAC) is the misclassification of air/tissue/bone mixtures or their boundaries. Our study aimed to evaluate a sinus/edge-corrected (SEC) ZTAC (ZTAC_SEC), relative to an uncorrected (UC) ZTAC (ZTAC_UC) and a CT atlas-based attenuation correction (ATAC). Methods: Whole-body ¹⁸F-FDG PET/MRI scans were obtained for 12 patients after PET/CT scans. Only data acquired at a bed station that included the head were used for this study. Using PET data from PET/MRI, we applied ZTAC_UC, ZTAC_SEC, ATAC, and reference CT-based attenuation correction (CTAC) to PET attenuation correction. For ZTAC_UC, the bias-corrected and normalized ZTE was converted to pseudo-CT with air (−1,000 HU for ZTE < 0.2), soft-tissue (42 HU for ZTE > 0.75), and bone (−2,000 × [ZTE − 1] + 42 HU for 0.2 ≤ ZTE ≤ 0.75). Afterward, in the pseudo-CT, sinus/edges were automatically estimated as a binary mask through morphologic processing and edge detection. In the binary mask, the overestimated values were rescaled below 42 HU for ZTAC_SEC. For ATAC, the atlas deformed to MR in-phase was segmented to air, inner air, soft tissue, and continuous bone. For the quantitative evaluation, PET mean uptake values were measured in twenty 1-mL volumes of interest distributed throughout brain tissues. The PET uptake was compared using a paired t test. An error histogram was used to show the distribution of voxel-based PET uptake differences. Results: Compared with CTAC, ZTAC_SEC achieved the overall PET quantification accuracy (0.2% ± 2.4%, P = 0.23) similar to CTAC, in comparison with ZTAC_UC (5.6% ± 3.5%, P < 0.01) and ATAC (−0.9% ± 5.0%, P = 0.03). Specifically, a substantial improvement with ZTAC_SEC (0.6% ± 2.7%, P < 0.01) was found in the cerebellum, in comparison with ZTAC_UC (8.1% ± 3.5%, P < 0.01) and ATAC (−4.1% ± 4.3%, P < 0.01). The histogram of voxel-based uptake differences demonstrated that ZTAC_SEC reduced the magnitude and variation of errors substantially, compared with ZTAC_UC and ATAC. Conclusion: ZTAC_SEC can provide an accurate PET quantification in brain PET/MRI, comparable to the accuracy achieved by CTAC, particularly in the cerebellum.

Attenuation correction (AC) is important for the quantitative merits of positron emission tomography (PET). However, attenuation coefficients cannot be derived from magnetic resonance (MR) images directly for PET/MR systems. In this work, we aimed to derive continuous AC maps from Dixon MR images without the requirement of MR and computed tomography (CT) image registration. To achieve this, a 3-D generative adversarial network with both discriminative and cycle-consistency loss (Cycle-GAN) was developed. The modified 3-D U-net was employed as the structure of the generative networks to generate the pseudo-CT/MR images. The 3-D patch-based discriminative networks were used to distinguish the generated pseudo-CT/MR images from the true CT/MR images. To evaluate its performance, datasets from 32 patients were used in the experiment. The Dixon segmentation and atlas methods provided by the vendor and the convolutional neural network (CNN) method which utilized registered MR and CT images were employed as the reference methods. Dice coefficients of the pseudo-CT images and the regional quantification in the reconstructed PET images were compared. Results show that the Cycle-GAN framework can generate better AC compared to the Dixon segmentation and atlas methods, and shows comparable performance compared to the CNN method.

Our results demonstrated that patient-specific anatomic differences from the atlas caused bone attenuation differences and misclassification of sinuses, resulting in patient-dependent performance variation of atlas-based attenuation correction. PurposeTo assess the patient-dependent accuracy of atlas-based attenuation correction (ATAC) for brain positron emission tomography (PET) in an integrated time-of-flight (TOF) PET/magnetic resonance (MR) imaging system.Materials and MethodsThirty recruited patients provided informed consent in this institutional review board–approved study. All patients underwent whole-body fluorodeoxyglucose PET/computed tomography (CT) followed by TOF PET/MR imaging. With use of TOF PET data, PET images were reconstructed with four different attenuation correction (AC) methods: PET with patient CT-based AC (CTAC), PET with ATAC (air and bone from an atlas), PET with ATACpatientBone (air and tissue from the atlas with patient bone), and PET with ATACboneless (air and tissue from the atlas without bone). For quantitative evaluation, PET mean activity concentration values were measured in 14 1-mL volumes of interest (VOIs) distributed throughout the brain and statistical significance was tested with a paired t test.Results...