Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network.

Purpose: Deep learning-based denoising is promising for myocardial perfusion (MP) SPECT. However, conventional convolutional neural network (CNN)-based methods use fixed-sized convolutional kernels to convolute one region within the receptive field at a time, which would be ineffective for learning the feature dependencies across large regions. The attention mechanism (Att) is able to learn the relationships between the local receptive field and other voxels in the image. In this study, we propose a 3D attention-guided generative adversarial network (AttGAN) for denoising fast MP-SPECT images. Methods: Fifty patients who underwent 1184 MBq 99mTc-sestamibi stress SPECT/CT scan were retrospectively recruited. Sixty projections were acquired over 180° and the acquisition time was 10 s/view for the full time (FT) mode. Fast MP-SPECT projection images (1 s to 7 s) were generated from the FT list mode data. We further incorporated binary patient defect information (0 = without defect, 1 = with defect) into AttGAN (AttGAN-def). AttGAN, AttGAN-def, cGAN, and Unet were implemented using Tensorflow with the Adam optimizer running up to 400 epochs. FT and fast MP-SPECT projection pairs of 35 patients were used for training the networks for each acquisition time, while 5 and 10 patients were applied for validation and testing. Five-fold cross-validation was performed and data for all 50 patients were tested. Voxel-based error indices, joint histogram, linear regression, and perfusion defect size (PDS) were analyzed. Results: All quantitative indices of AttGAN-based networks are superior to cGAN and Unet on all acquisition time images. AttGAN-def further improves AttGAN performance. The mean absolute error of PDS by AttcGAN-def was 1.60 on acquisition time of 1 s/prj, as compared to 2.36, 2.76, and 3.02 by AttGAN, cGAN, and Unet. Conclusion: Denoising based on AttGAN is superior to conventional CNN-based networks for MP-SPECT.

Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT.

BACKGROUND: Low-dose (LD) myocardial perfusion (MP) SPECT suffers from high noise level, leading to compromised diagnostic accuracy. Here we investigated the denoising performance for MP-SPECT using a conditional generative adversarial network (cGAN) in projection-domain (cGAN-prj) and reconstruction-domain (cGAN-recon). METHODS: Sixty-four noisy SPECT projections were simulated for a population of 100 XCAT phantoms with different anatomical variations and 99mTc-sestamibi distributions. Series of LD projections were obtained by scaling the full dose (FD) count rate to be 1/20 to 1/2 of the original. Twenty patients with 99mTc-sestamibi stress SPECT/CT scans were retrospectively analyzed. For each patient, LD SPECT images (7/10 to 1/10 of FD) were generated from the FD list mode data. All projections were reconstructed by the quantitative OS-EM method. A 3D cGAN was implemented to predict FD images from their corresponding LD images in the projection- and reconstruction-domain. The denoised projections were reconstructed for analysis in various quantitative indices along with cGAN-recon, Gaussian, and Butterworth-filtered images. RESULTS: cGAN denoising improves image quality as compared to LD and conventional post-reconstruction filtering. cGAN-prj can further reduce the dose level as compared to cGAN-recon without compromising the image quality. CONCLUSIONS: Denoising based on cGAN-prj is superior to cGAN-recon for MP-SPECT.

Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising.

Background: Myocardial perfusion (MP) SPECT is a well-established method for diagnosing cardiac disease, yet its radiation risk poses safety concern. This study aims to apply and evaluate the use of Pix2Pix generative adversarial network (Pix2Pix GAN) in denoising low dose MP SPECT images. Methods: One hundred male and female patients with different 99mTc-sestamibi activity distributions, organ and body sizes were simulated by a population of digital 4D Extended Cardiac Torso (XCAT) phantoms. Realistic noisy SPECT projections of full dose of 987 MBq injection and 16 min acquisition, and low dose ranged from 1/20 to 1/2 of the full dose, were generated by an analytical projector from the right anterior oblique (RAO) to the left posterior oblique (LPO) positions. Additionally, twenty patients underwent ~1,184 MBq 99mTc-sestamibi stress SPECT/CT scan were also retrospectively recruited for the study. For each patient, low dose SPECT images (7/10 to 1/10 of full dose) were generated from the full dose list mode data. Our Pix2Pix GAN model was trained with full dose and low dose reconstructed SPECT image pairs. Normalized mean square error (NMSE), structural similarity index (SSIM), coefficient of variation (CV), full-width-at-half-maximum (FWHM) and relative defect size differences (RSD) of Pix2Pix GAN processed images were evaluated along with a reference convolutional auto encoder (CAE) network and post-reconstruction filters. Results: NMSE values of 0.0233±0.004 vs. 0.0249±0.004 and 0.0313±0.007 vs. 0.0579±0.016 were obtained on 1/2 and 1/20 dose level for Pix2Pix GAN and CAE in the simulation study, while they were 0.0376±0.010 vs. 0.0433±0.010 and 0.0907±0.020 vs. 0.1186±0.025 on 7/10 and 1/10 dose level in the clinical study. Similar results were also obtained from the SSIM, CV, FWHM and RSD values. Overall, the use of Pix2Pix GAN was superior to other denoising methods in all physical indices, particular in the lower dose levels in the simulation and clinical study. Conclusions: The Pix2Pix GAN method is effective to reduce the noise level of low dose MP SPECT. Further studies on clinical performance are warranted to demonstrate its full clinical effectiveness.

Comparison of Conventional and Radiomic Features between 18F-FBPA PET/CT and PET/MR.

Boron-10-containing positron emission tomography (PET) radio-tracer, 18F-FBPA, has been used to evaluate the feasibility and treatment outcomes of Boron neutron capture therapy (BNCT). The clinical use of PET/MR is increasing and reveals its benefit in certain applications. However, the PET/CT is still the most widely used modality for daily PET practice due to its high quantitative accuracy and relatively low cost. Considering the different attenuation correction maps between PET/CT and PET/MR, comparison of derived image features from these two modalities is critical to identify quantitative imaging biomarkers for diagnosis and prognosis. This study aimed to investigate the comparability of image features extracted from 18F-FBPA PET/CT and PET/MR. A total of 15 patients with malignant brain tumor who underwent 18F-FBPA examinations using both PET/CT and PET/MR on the same day were retrospectively analyzed. Overall, four conventional imaging characteristics and 449 radiomic features were calculated from PET/CT and PET/MR, respectively. A linear regression model and intraclass correlation coefficient (ICC) were estimated to evaluate the comparability of derived features between two modalities. Features were classified into strong, moderate, and weak comparability based on coefficient of determination (r2) and ICC. All of the conventional features, 81.2% of histogram, 37.5% of geometry, 51.5% of texture, and 25% of wavelet-based features, showed strong comparability between PET/CT and PET/MR. With regard to the wavelet filtering, radiomic features without filtering (61.2%) or with low-pass filtering (59.2%) along three axes produced strong comparability between the two modalities. However, only 8.2% of the features with high-pass filtering showed strong comparability. The linear regression models were provided for the features with strong and moderate consensus to interchange the quantitative features between the PET/CT and the PET/MR. All of the conventional and 71% of the radiomic (mostly histogram and texture) features were sufficiently stable and could be interchanged between 18F-FBPA PET with different hybrid modalities using the proposed equations. Our findings suggested that the image features high interchangeability may facilitate future studies in comparing PET/CT and PET/MR.

Fractionated deep-inspiration breath-hold ZTE Compared with Free-breathing four-dimensional ZTE for detecting pulmonary nodules in oncological patients underwent PET/MRI.

The zero echo time (ZTE) technique has improved the detection of lung nodules in PET/MRI but respiratory motion remains a challenge in lung scan. We investigated the feasibility and performance of fractionated deep-inspiration breath-hold (FDIBH) three-dimensional (3D) ZTE FDG PET/MRI for assessing lung nodules in patients with proved malignancy. Sixty patients who had undergone ZTE FDG PET/MRI and chest CT within a three-day interval were retrospectively included. Lung nodules less than 2 mm were excluded for analysis. Two physicians checked the adequacy of FDIBH ZTE and compared the lung nodule detection rates of FDIBH 3D ZTE and free-breathing (FB) four-dimensional (4D) ZTE, with chest CT as the reference standard. FDIBH resolved the effect of respiratory motion in 49 patients. The mean number and size of the pulmonary nodules identified in CT were 15 ± 31.3 per patient and 5.9 ± 4.6 mm in diameter. The overall nodule detection rate was 71% for FDIBH 3D ZTE and 70% for FB 4D ZTE (p = 0.73). FDIBH 3D ZTE significantly outperformed FB 4DZTE in detecting lung base nodules (72% and 68%; p = 0.03), especially for detecting those less than 6 mm (61% and 55%; p = 0.03). High inter-rater reliability for FDIBH 3D ZTE and FB 4D ZTE (k = 0.9 and 0.92) was noted. In conclusion, the capability of FDIBH 3D ZTE in respiratory motion resolution was limited with a technical failure rate of 18%. However, it could provide full expansion of the lung in a shorter scan time which enabled better detection of nodules (< 6 mm) in basal lungs, compared to FB 4D ZTE.

The importance of optimal ROIs delineation for FBPA-PET before BNCT.

One of the eligible criteria for patients to receive boron neutron capture therapy (BNCT) is based on the tumour-to-normal ratio (T/N) measured by FBPA-PET. However, there is no standard protocol for normal region-of-interested delineation. With comparison of contralateral cerebrum, our study revealed the consistency (p < 0.05) and high feasibility using the cerebellum as an alternative normal tissue baseline because of its homogeneous uptake. Following RECIST version 1.1, the standard-operating-procedure (SOP) for the BNCT fulfilled the expected tumour response and tumour shrinkage rate (p < 0.05). Our modified procedure can provide more precise information for BNCT within a reasonable time.

Anti-hypertension effects of traditional Chinese medicine ju-ling-tang on renal hypertensive rats.

This research investigated the anti-hypertension effect of the traditional Chinese medicine (TCM) ju-ling-tang (JLT) on an animal model of hypertension induced by unilateral renal artery ligation. In the study of anti-hypertension effects, 60 minutes after oral administration with NG tube feeding of 240 mg/kg JLT, a significant decrease in blood pressure (p < 0.05) was observed and sustained till 120 minutes. In the group given 50 mg/kg alpha-methyldopa orally, the effect was obvious 90 minutes after medication (p < 0.01), and lasted until 240 minutes. In terms of organ pathology, a significant reduction in the extent of induced glomerular sclerosis was observed in rats given 240 mg/kg JLT compared with the control. From these results, we infer that JLT has a beneficial anti-hypertensive effect on renal hypertension.