EJNMMI Physics最新文献

Background: Accurate attenuation correction (AC) is vital in nuclear medicine, particularly for quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) imaging. This study aimed to establish a CT-free quantification technology in kidney SPECT imaging using deep learning to generate synthetic attenuation maps (μ-maps) from SPECT data, thereby reducing radiation exposure and eliminating the need for CT scans.

Results: A dataset of 1000 Tc-99m DTPA SPECT/CT scans was analyzed for training (n = 800), validation (n = 100), and testing (n = 100) using a modified 3D U-Net for deep learning. The study investigated the use of primary emission and scattering SPECT data, normalization methods, loss function optimization, and up-sampling techniques for optimal μ-map generation. The problem of checkerboard artifacts, unique to μ-map generation from SPECT signals, and the effects of iodine contrast media were evaluated. The addition of scattering SPECT to primary emission SPECT imaging, logarithmic maximum normalization, the combination of absolute difference loss (L₁) and three times the absolute gradient difference loss (3 × L_GDL), and the nearest-neighbor interpolation significantly enhanced AI performance in μ-map generation (p < 0.00001). Checkerboard artifacts were effectively eliminated using the nearest-neighbor interpolation technique. The developed AI algorithm produced μ-maps neutral to the presence of iodine contrast and showed negligible contrast effects on quantitative SPECT measurement, such as glomerular filtration rate (GFR). The potential reduction in radiation exposure by transitioning to AI-based CT-free SPECT imaging ranges from 45.3 to 78.8%.

Conclusion: The study successfully developed and optimized a deep learning algorithm for generating synthetic μ-maps in kidney SPECT images, demonstrating the potential to transition from conventional SPECT/CT to CT-free SPECT imaging for GFR measurement. This advancement represents a significant step towards enhancing patient safety and efficiency in nuclear medicine.

Purpose: To evaluate ¹⁸F-FDG myocardial metabolism imaging (MMI) using a total-body PET/CT scanner and explore the feasible scan duration to guide the clinical practice.

Methods: A retrospective analysis was conducted on 41 patients who underwent myocardial perfusion-metabolism imaging to assess myocardial viability. The patients underwent ¹⁸F-FDG MMI with a total-body PET/CT scanner using a list-mode for 600 s. PET data were trimmed and reconstructed to simulate images of 600-s, 300-s, 120-s, 60-s, and 30-s acquisition time (G600-G30). Images among different groups were subjectively evaluated using a 5-point Likert scale. Semi-quantitative evaluation was performed using standardized uptake value (SUV), myocardial to background activity ratio (M/B), signal to noise ratio (SNR), contrast to noise ratio (CNR), contrast ratio (CR), and coefficient of variation (CV). Myocardial viability analysis included indexes of Mismatch and Scar. G600 served as the reference.

Results: Subjective visual evaluation indicated a decline in the scores of image quality with shortening scan duration. All the G600, G300, and G120 images were clinically acceptable (score ≥ 3), and their image quality scores were 4.9 ± 0.3, 4.8 ± 0.4, and 4.5 ± 0.8, respectively (P > 0.05). Moreover, as the scan duration reduced, the semi-quantitative parameters M/B, SNR, CNR, and CR decreased, while SUV and CV increased, and significant difference was observed in G300-G30 groups when comparing to G600 group (P < 0.05). For myocardial viability analysis of left ventricular and coronary segments, the Mismatch and Scar values of G300-G30 groups were almost identical to G600 group (ICC: 0.968-1.0, P < 0.001).

Conclusion: Sufficient image quality for clinical diagnosis could be achieved at G120 for MMI using a total-body PET/CT scanner, while the image quality of G30 was acceptable for myocardial viability analysis.

Purpose: The problem of image denoising in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a fundamental challenge. Although various image processing techniques have been presented, they may degrade the contrast of denoised images. The proposed idea in this study is to use a deep neural network as the denoising procedure during the iterative reconstruction process rather than the post-reconstruction phase. This method could decrease the background coefficient of variation (COV_bkg) of the final reconstructed image, which represents the amount of random noise, while improving the contrast-to-noise ratio (CNR).

Methods: In this study, a generative adversarial network is used, where its generator is trained by a two-phase approach. In the first phase, the network is trained by a confined image region around the heart in transverse view. The second phase improves the network's generalization by tuning the network weights with the full image size as the input. The network was trained and tested by a dataset of 247 patients who underwent two immediate serially high- and low-noise SPECT-MPI.

Results: Quantitative results show that compared to post-reconstruction low pass filtering and post-reconstruction deep denoising methods, our proposed method can decline the COV_bkg of the images by up to 10.28% and 12.52% and enhance the CNR by up to 54.54% and 45.82%, respectively.

Conclusion: The iterative deep denoising method outperforms 2D low-pass Gaussian filtering with an 8.4-mm FWHM and post-reconstruction deep denoising approaches.

Radiomics is an emerging field of medical imaging that aims at improving the accuracy of diagnosis, prognosis, treatment planning and monitoring non-invasively through the automated or semi-automated quantitative analysis of high-dimensional image features. Specifically in the field of nuclear medicine, radiomics utilizes imaging methods such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) to evaluate biomarkers related to metabolism, blood flow, cellular activity and some biological pathways. Lung cancer ranks among the leading causes of cancer-related deaths globally, and radiomics analysis has shown great potential in guiding individualized therapy, assessing treatment response, and predicting clinical outcomes. In this review, we summarize the current state-of-the-art radiomics progress in lung cancer, highlighting the potential benefits and existing limitations of this approach. The radiomics workflow was introduced first including image acquisition, segmentation, feature extraction, and model building. Then the published literatures were described about radiomics-based prediction models for lung cancer diagnosis, differentiation, prognosis and efficacy evaluation. Finally, we discuss current challenges and provide insights into future directions and potential opportunities for integrating radiomics into routine clinical practice.

Background: Deep learning (DL)-based denoising has been proven to improve image quality and quantitation accuracy of low dose (LD) SPECT. However, conventional DL-based methods used SPECT images with mixed frequency components. This work aims to develop an integrated multi-frequency denoising network to further enhance LD myocardial perfusion (MP) SPECT denoising.

Methods: Fifty anonymized patients who underwent routine ^99mTc-sestamibi stress SPECT/CT scans were retrospectively recruited. Three LD datasets were obtained by reducing the 10 s acquisition time of full dose (FD) SPECT to be 5, 2 and 1 s per projection based on the list mode data for a total of 60 projections. FD and LD projections were Fourier transformed to magnitude and phase images, which were then separated into two or three frequency bands. Each frequency band was then inversed Fourier transformed back to the image domain. We proposed a 3D integrated attention-guided multi-frequency conditional generative adversarial network (AttMFGAN) and compared with AttGAN, and separate AttGAN for multi-frequency bands denoising (AttGAN-MF).The multi-frequency FD and LD projections of 35, 5 and 10 patients were paired for training, validation and testing. The LD projections to be tested were separated to multi-frequency components and input to corresponding networks to get the denoised components, which were summed to get the final denoised projections. Voxel-based error indices were measured on the cardiac region on the reconstructed images. The perfusion defect size (PDS) was also analyzed.

Results: AttGAN-MF and AttMFGAN have superior performance on all physical and clinical indices as compared to conventional AttGAN. The integrated AttMFGAN is better than AttGAN-MF. Multi-frequency denoising with two frequency bands have generally better results than corresponding three-frequency bands methods.

Conclusions: AttGAN-MF and AttMFGAN are promising to further improve LD MP SPECT denoising.

Background: [¹⁸F]MK-6240 is a neurofibrillary tangles PET radiotracer that has been broadly used in aging and Alzheimer's disease (AD) studies. Majority of [¹⁸F]MK-6240 PET studies use dynamic acquisitions longer than 60 min to assess the tracer kinetic parameters. As of today, no consensus has been established on the optimum dynamic PET scan time. In this study, we assess the reproducibility of [¹⁸F]MK-6240 quantitative metrics using shortest dynamic PET protocols in cognitively normal subjects. PET metrics were measured through two-tissue compartment model (2TCM) and Logan model to estimate VT and DVR, as well as SUVR from 90 to 120 min (SUVR_{90 - 120 min}) post-tracer injection for brain regions. 2TCM was carried out using the 120 min dynamic coffee break dataset (first scan from 0 to 60 min p.i., second scan from 90 to 120 min p.i.) and then repeated after stepwise shortening it by 5 min. The dynamic scan length that reproduced the 120 min dynamic scans-based VT to within 10% error was defined as the shortest acquisition time (SAT). The SAT SUVR_{90 - 120 min} was deduced from the SAT dataset by extrapolation of each image pixel time-activity curve to 120 min. The reproducibility of the 120 min dynamic scans-based VT_2TCM, DVR_2TCM, DVR_Logan, and SUVR using the SAT was assessed using Passing-Bablock analysis. The limits of reproducibility of each PET metrics were determined using Bland-Altman analysis.

Results: A dynamic SAT of 40 min yielded < 10% error in [¹⁸F]MK-6240 VT_2TCM's for all brain regions, compared to those measured using the 120 min datasets. SAT-based analysis did not show statistically significant systemic or proportional biases in VT_2TCM, DVR_2TCM, DVR_Logan, or SUVR compared to those deduced from the full dynamic dataset of 120 min. A mean difference between the 120 min- and SAT-based analysis of less than 4%, 10%, 15%, and 20% existed in the VT_2TCM, DVR_2TCM, DVR_Logan, and SUVR respectively.

Conclusion: Kinetic modeling of [¹⁸F]MK-6240 PET can be accurately performed using dynamic scan times as short as 40 min. This can facilitate studies with [¹⁸F]MK-6240 PET and improve patients accrual. Further work would be necessary to confirm the reproducibility of these results for patients in dementia spectra.

Background: Quantitative imaging is a crucial step for dosimetry in radionuclide therapies. Traditionally, SPECT/CT imaging is quantified based on scanner-specific conversion factors or self-calibration, but recently absolute quantification methods have been introduced in commercial SPECT reconstruction software (Broad Quantification, Siemens Healthineers). In this phantom study we investigate the accuracy of three quantification methods for holmium-166 SPECT/CT imaging, and provide recommendations for clinical dosimetry.

Methods: One cylindrical phantom, filled with a homogeneous holmium-166-chloride activity concentration solution, was imaged at one time point to determine a scanner-specific conversion factor, and to characterize the spatial dependency of the activity concentration recovery. One Jaszczak phantom with six fillable spheres, 10:1 sphere-to-background ratio, was imaged over a large range of holmium-166 activities (61-3130 MBq). The images were reconstructed with either an ordered subset expectation maximization (OSEM, Flash3D-reconstruction; scanner-specific quantification or self-calibration quantification) or an ordered subset conjugate gradient (OSCG, xSPECT-reconstruction; Broad Quantification) algorithm. These three quantification methods were compared for the data of the Jaszczak phantom and evaluated based on whole phantom recovered activity, activity concentration recovery coefficients (ACRC), and recovery curves.

Results: The activity recovery in the Jaszczak phantom was 28-115% for the scanner-specific, and 57-97% for the Broad Quantification quantification methods, respectively. The self-calibration-based activity recovery is inherently always 100%. The ACRC for the largest sphere (Ø60 mm, ~ 113 mL) ranged over (depending on the activity level) 0.22-0.89, 0.76-0.86, 0.39-0.72 for scanner-specific, self-calibration and Broad Quantification, respectively.

Conclusion: Of the three investigated quantification methods, the self-calibration technique produces quantitative SPECT images with the highest accuracy in the investigated holmium-166 activity range.

Purposes: To explore the clinical feasibility and efficacy of a deep inspiration breath-hold (BH) PET/CT using [¹⁸F]AlF-NOTA-LM3 on upper abdominal lesions in patients with neuroendocrine tumors (NETs).

Methods: Twenty-three patients underwent a free-breath (FB) whole-body PET/CT, including a 10 min/bed scan for the upper abdomen with a vital signal monitoring for respiratory gating (RG) followed by a 20-second BH PET/CT covering the same axial range. For the upper abdomen bed, the following PET series was reconstructed: a 2-min FB PET; RG PET (6 bins); a 20-second and 15-second BH PET (BH_15 and BH_20). Semi-quantitative analysis was performed to compare liver SUV_mean, lesion SUV_max, MTV, its percentage difference and target-to-background ratio (TBR) between both BH PET and RG PET images. Subgroup analysis considered lesion location, MTV and SUV_max. A 5-point Likert scale was used to perform visual analysis and any missed or additional lesions were identified compared with RG PET.

Results: Quantitative analysis on overall lesions (n = 78) revealed higher SUV_max and TBR, and smaller MTV for both BH PET compared to FB and RG PET, with lesion location-specific variations. Neither significant difference was observed in all metrics between RG and FB PET in larger lesions, nor in MTV in lower-uptake lesions. However, both BH PET significantly enhanced these measurements. In the visual analysis, both BH PET showed noninferior performance to RG PET, and were evaluated clinically acceptable. Additional and missed lesions were observed in FB and both BH PET compared with RG PET, but didn't alter the clinical management. The BH_15 PET showed comparable performance to BH_20 PET in any comparison.

Conclusion: The BH PET/CT using [¹⁸F]AlF-NOTA-LM3 is effective in detecting upper abdominal lesions, offering more accurate quantitative measurements. Using a novel PET/CT scanner, a 15-second BH PET can provide comparable and superior performance to RG PET, indicating potential feasibility in clinical routines.