EJNMMI Physics最新文献

Ischemic heart disease remains a leading cause of mortality worldwide. Myocardial perfusion imaging (MPI) using Rubidium-82 (⁸²Rb) positron emission tomography (PET) is a cornerstone in its evaluation. However, conventional CT-based attenuation correction (AC) is prone to artifacts, with misalignment between PET emission data and the CT-AC being a common problem. This study evaluates the feasibility of introducing a deep learning approach to generate synthetic CT (sCT) images directly from non-attenuation-corrected ⁸²Rb-PET images. To this end, we developed a cGAN using a conditional generative adversarial network (cGAN) with an Attention U-Net generator to produce sCT images to produce sCT-AC maps, based upon 544 PET/CT MPI scans. Image quality was assessed using structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean absolute error (MAE), and mean error (ME). Additionally, attenuation-corrected PET images based on sCT were evaluated in the cardiac region using relative mean error (RME) and relative mean absolute error (RMAE). Cardiac function and perfusion assessments, defined as the ischemic total perfusion deficit (iTPD) and the left ventricular ejection fraction reserve (LVEFR), were compared between sCT-based and conventional CT AC methods. Our sCT-images provided good correlation to the conventional CT-AC (SSIM = 0.91 ± 0.037, PSNR = 29.9 ± 3.2 dB). For the PET images, we report a slight bias in the cardiac region (RME = 4.2 ± 7.8%, RMAE = 6.9 ± 5.9%), likely due to a uniform overestimation of the soft-tissue u-maps within the sCT. Despite the bias, the quantification metrics remained comparable to those obtained with CT AC (mean iTPD: CT 3.73 ± 5.19% vs. sCT 3.67 ± 5.13%; mean LVEFR: CT 5.88 ± 5.96% vs. sCT 5.90 ± 6.11%). Additionally, the sCT-based approach appeared to reduce motion and implant-related artifacts, providing further motivation for its use over CT. This observation was made through visual inspection on a case-by-case basis. These results demonstrate the potential of deep learning-based sCT generation to maintain integrity in PET MPI while helping to mitigate issues related to misalignments and metal-induced artifacts.

Background: Internal mammary lymph node (IMLN) metastases play an important role in breast cancer staging and treatment planning but is often difficult to detect because of their small size and anatomical location. Recent advances in digital time-of-flight (TOF) positron emission tomography (PET)/CT and advanced image reconstruction techniques may improve the visualization of such small lesions. This study aimed to evaluate the performance of advanced reconstruction methods (HYPER Iterative and uAI HYPER DPR) for visualizing IMLN metastases in breast cancer using phantom and clinical data.

Methods: A modified NEMA image quality phantom and a retrospective cohort of breast cancer patients with IMLN metastases were evaluated using a high-resolution digital TOF PET/CT system (uMI 550). Images were reconstructed using ordered subset expectation maximization (OSEM), HYPER Iterative, and uAI HYPER DPR with different reconstruction parameters, and quantitative metrics and visual scores were assessed.

Results: In both phantom and clinical images, smaller RS-values for HYPER Iterative and larger Str-values for uAI HYPER DPR were associated with higher lesion conspicuity and contrast-related metrics, at the expense of increased noise. Images reconstructed with a 256 × 256 matrix showed lower background variability than those reconstructed with a 512 × 512 matrix. In the clinical study, these reconstruction settings resulted in higher SUV_max and tumor-to-background ratios for IMLN metastases, and visual scores for diagnostic confidence were higher for HYPER Iterative (RS = 0.7-0.91) and uAI HYPER DPR (Str = 2-4) than for OSEM.

Background: Reducing acquisition time in PET/CT imaging can degrade image quality and may compromise both diagnostic reliability and the robustness of radiomic features. This study investigates, in a large clinical cohort, whether AI-based denoising can preserve image quality and maintain the accuracy of quantitative and radiomic parameters in [¹⁸F]FDG and [⁶⁸Ga]Ga-PSMA-11 PET/CT scans.

Methods: We reconstructed three sets of images: 100% acquisition time (R100), 75% (R75), and 50% (R50), with their respective denoised versions using SubtlePET® (S75 and S50). On a NEMA phantom, we analysed six contrasts (12:1-2:1) using [¹⁸F]FDG, assessing contrast, background noise, and radiomic features. In a cohort of 282 patients injected with [¹⁸F]FDG and [⁶⁸Ga]Ga-PSMA-11, five nuclear medicine physicians performed a qualitative evaluation of image quality and confidence in the presence of hypermetabolism in 634 lesions. 109 radiomic features from 105 lesions were compared between the original and denoised reconstructions.

Results: The phantom study showed no difference in sphere contrast, a reduction in background noise variability, and excellent preservation of radiomic features. In the clinical population, S75 images showed improvements across all criteria evaluated, except for diagnostic confidence, which remained higher with R75 (p = 0.555 when compared to R100) for [¹⁸F]FDG. For [⁶⁸Ga]Ga-PSMA-11, only S50 images showed a significant degradation in liver image quality. A decrease in SUVmax was observed in denoised images (- 7.73% for [¹⁸F]FDG; - 11.46% for [⁶⁸Ga]Ga-PSMA-11, p < 0.0001). The radiomic analysis demonstrated excellent correlation, with a concordance correlation coefficient (CCC) > 0.8 for 90% of radiomic features.

Conclusions: SubtlePET® improves the image quality of PET acquisitions performed with reduced acquisition times using [¹⁸F]FDG and [⁶⁸Ga]Ga-PSMA. However, clinician confidence remains limited and, while denoised acquisitions preserve most radiomic features, others are altered, potentially limiting model transposability.

Purpose: The objective of this study is to generate reliable K_i parametric images from short-duration total-body PET scan for clinical applications using a simple population-based Patlak model.

Methods: We proposed a population-based Patlak model named Patlak-PBNT, which incorporates a population-based "normalized time" (PBNT) into the traditional Patlak plot. This model does not require long-duration PET scans to obtain an image-derived input function (IDIF) of full measured kinetics, thereby generating reliable K_i images from short-duration total-body PET scans. We evaluated the effectiveness of Patlak-PBNT based on 60-minute full-dynamic total-body [⁶⁸Ga]Ga-PSMA-11 PET data collected from 20 subjects PET scans. For comparison, the K_i images generated by the traditional Patlak plot (t* = 20 min post-injection) with measured IDIF was used as the gold standard. The differences between Patlak-PBNT-generated K_i images and the gold standard were evaluated at both the voxel and volume of interest (VOI) levels.

Results: Compared to the traditional Patlak, Patlak-PBNT effectively reduces PET scan duration while maintaining the quality of generated K_i images. For [⁶⁸Ga]Ga-PSMA-11, K_i images generated by Patlak-PBNT using only 40 minute dynamic PET images (20-60 min post-injection) show negligible differences compared to those generated by traditional Patlak with the same 40 minute dynamic PET images and a 60 minute full-duration IDIF, with a normalized mean squared error (NMSE) of 0.01, a Pearson correlation coefficient (Pearson's r) of 0.99, and a peak signal-to-noise ratio (PSNR) of 75.27 dB. It is important to note that K_i images generated by Patlak-PBNT, using only 20 minute dynamic PET images (40-60 min post-injection), exhibit a high correlation with the gold standard in predefined VOIs, achieving a coefficient of determination (R²) of 0.92.

Conclusion: The proposed Patlak-PBNT model reduces the dependency on a complete input function, thereby avoiding the need for long-duration PET scans typically required to obtain a full input function. When utilizing dynamic PET images of identical scan durations (20-60 min post-injection), the K_i images generated by Patlak-PBNT and traditional Patlak are essentially identical. Furthermore, even when the scan duration is further reduced, the Patlak-PBNT method is capable of quantifying 20 minute dynamic [⁶⁸Ga]Ga-PSMA-11 total-body PET images.

Background: Monitoring the external dose rate (EDR) attenuation serves as a key consideration in supporting discharge decisions for patients with differentiated thyroid cancer (DTC) who have undergone radioiodine therapy. We aimed to study the EDR attenuation and its related factors in DTC patients during I-131 therapy.

Methods: This study enrolled 886 DTC patients who first underwent I-131 therapy at the Third Bethune Hospital of Jilin University, China. We measured the EDR at approximately 2, 24, 48, and 72 h post-therapy. Two formulas were established to represent the EDR decay with time: 1) EDR =[Formula: see text] and EDR_% = [Formula: see text], where EDR is the absolute external dose rate (µSv/h), EDR_% is the percentage EDR relative to the initial EDR (100%), SI (speed index, μSv/h²) is the absolute decay rate of I-131 with the time, SI_% (%/h) is the relative decay rate with the time, and b is a constant.

Results: The finally fitted SI and SI_% from patients' data were -0.020 μSv/h² and -0.026%/h, respectively. EDR_% exhibited a stronger correlation with administration time than EDR (R²: 0.951 vs. 0.829). Body mass index (BMI), smoking, history of type 2 diabetes mellitus, Follicular Thyroid Carcinoma (FTC) subtype, increasing residual thyroid tissue grading, FT3 and Tg levels positively associated with SI. The factors negatively associated with SI were female sex, a higher N stage and a higher I-131 dose. SI_% was positively associated with smoking history, history of type 2 diabetes mellitus, and FTC pathological subtype, and negatively with female sex and higher I-131 dose.

Conclusions: EDR_% had better correlation than EDR with I-131 administration time. The related factors for SI and SI_% included I-131 dose, sex, BMI, thyroid cancer pathology, medical history and thyroid function. These findings provide a reference for radiation protection officers in evaluating radioactive activity during I-131 therapy.

Background: Follicular lymphoma (FL) is the second most common subtype of non-Hodgkin lymphoma. Currently, [¹⁸F]FDG PET-CT is used for staging, response evaluation, and remission assessment. While advances in quantitative PET-CT are promising for prognostic assessment, they depend on reproducible tumor delineation. Various segmentation methods have been proposed, but their application to FL PET is less established, despite known differences in uptake patterns across lymphoma subtypes. This study aims to evaluate the performance of several single-threshold and multi-threshold methods for FL [¹⁸F]FDG PET-CT lesion segmentation on segmentation quality, interobserver variability, and ease-of-use.

Methods: Baseline PET-CT data of 25 second-line FL patients from the HOVON110 trial and 12 first-line FL patients from the PETAL trial were selected. Two observers applied 13 different semi-automatic methods, of which six used a single threshold and seven combined thresholds (multi-threshold). Methods include, SUV threshold methods, an AI-based method, majority vote and lesion-based selection methods. The segmentation process comprises four steps: step 1 and 2 involved generating a preselection, while step 3 and 4 applied an automatic method followed by manual adjustments. To assess segmentation quality, both observers gave a score (1-3) ranging from undersegmentation to oversegmentation. For interobserver variability, the difference in total metabolic tumor volume between observers was determined. The ease-of-use was assessed based on manually added and removed volume in step 4.

Results: A total of 962 segmentations were made by two observers. Differences in results between the methods were limited across all characteristics, indicating an overall satisfactory performance of all methods. The multi-threshold method scored better for segmentation quality in comparison to single-threshold methods, indicating less under- or oversegmentation. The single-threshold method SUV4.0 demonstrated lower median (0.3 mL) and inter quartile range (2.0 mL) concerning interobserver variability in comparison to lesion-based methods.

Conclusion: Among the single threshold methods, SUV4.0 is preferred regarding ease-of-use, observer variability and segmentation quality. While the multi-threshold lesion-based methods showed the a higher segmentation quality, SUV4.0 has the benefit of easy implementation, wide availability and is in-line with the currently set benchmark for lymphoma PET analysis. We identified SUV4.0 and a lesion-based method as the candidate methods preferred for further clinical performance evaluation.

Background: Photon scatter significantly degrades Single Photon Emission Computed Tomography (SPECT) image quality, with scattered photons accounting for 30-40% of detected counts within standard energy windows. While conventional scatter correction methods face limitations including noise amplification and computational demands, wavelet transforms offer promising capabilities for sinogram-domain correction. However, comprehensive parameter optimization remains unexplored.

Methods: We evaluated 96 mother wavelets across seven families, implementing three decomposition levels and five thresholding strategies in a Monte Carlo simulation framework. Scatter-contaminated sinograms were processed using discrete wavelet transforms and reconstructed via filtered backprojection. Quantitative assessment employed Universal Image Quality Index (UIQI) with varying block sizes (3 × 3, 25 × 25, 128 × 128) and Root Mean Square Error (RMSE). Three nuclear medicine physicians performed blinded qualitative assessment of the processed images.

Results: Among 94 viable wavelets (excluding outliers db45 and rbio3.1), global optimization identified Rigrsure and Heursure thresholding at decomposition level 1 as optimal for maximizing UIQI (0.559 ± 0.002), while per-slice optimization favored Minimaxi thresholding at level 2. Strong positive correlation existed between UIQI (25 × 25) and UIQI (128 × 128) (r = 0.887, p < 0.01), with both metrics inversely related to RMSE error (r≈ - 0.73, p < 0.01). Despite UIQI optimization, RMSE-optimized images received significantly higher visual quality rankings from physicians (69% improvement, p < 0.001), revealing critical divergence between quantitative metrics and diagnostic utility.

Conclusion: This study establishes wavelet-based scatter correction as a viable approach for SPECT image enhancement through systematic parameter mapping. The marked preference for RMSE-optimized images over UIQI-optimized ones underscores the necessity of aligning algorithmic optimization with clinical perception rather than technical metrics alone. These findings provide a foundation for standardizing wavelet implementation in SPECT scatter correction, directly connecting mathematical optimization to diagnostic relevance in nuclear medicine imaging.

Purpose: The rapid expansion of radiopharmaceutical therapy (RPT) development demands scalable preclinical dosimetry methods. While PET and SPECT remain the gold standards, their low throughput and high cost limit large-cohort studies. Cherenkov luminescence imaging (CLI) offers a high-throughput alternative but suffers from depth-dependent attenuation and photon scatter that compromise quantitative accuracy. This work develops and validates a quantitative CLI methodology incorporating attenuation and scatter corrections to enable accurate preclinical dosimetry.

Methods: Depth-dependent attenuation was characterized using a tissue-mimicking phantom to derive calibration coefficients. Photon scatter was modeled using GEANT4-generated Cherenkov spread functions (CSFs), applied in a depth-weighted iterative Richardson-Lucy deconvolution/reconvolution framework. The method was evaluated in NU/NU mice (n = 4) bearing MC38 tumors after injection of ⁸⁶Y-NM600, an isotope suitable for both PET and CLI. Liver and tumor activities were quantified at four timepoints using PET and the proposed CLI method. Voxelized Monte Carlo dosimetry was performed for both modalities.

Results: CLI-PET activity quantification yielded mean errors of 15.4% (liver) and 10.3% (tumor) over the first three timepoints. Tumor absorbed doses from CLI-derived synthetic PET images (3.4 ± 0.3 Gy/MBq) were statistically indistinguishable from PET-based estimates (3.2 ± 0.2 Gy/MBq, p = 0.31). Discrepancies increased at late timepoints due to low activity and background auto-luminescence.

Conclusions: With appropriate depth-dependent optical attenuation calibration and Monte Carlo-derived ionizing scatter correction, CLI can provide quantitative biodistribution and dosimetry estimates comparable to PET. This approach enables high-throughput, low-cost in vivo dosimetry, expanding the feasibility of large-scale preclinical RPT studies and supporting translational radiopharmaceutical development.

Objectives: To compare two deep learning (DL) approaches for low-count PET/CT: deep progressive reconstruction (DPR), a scanner-integrated reconstruction-level method, and a deep-learning image-domain post-processing enhancement (POST; RaDynPET).

Methods: Sixty-seven patients who underwent whole-body ¹⁸F-FDG PET/CT were enrolled. PET images were reconstructed with ordered-subsets expectation maximization (OSEM) at 30/60/120 s/bed (O30, O60, O120 [clinical reference]) and with DPR at 30/60/90/120 s/bed (D30, D60, D90, D120). POST (RaDynPET) was applied to the unaltered O30 /O60 images to yield P30/P60. Two nuclear medicine physicians rated image quality using 5-point Likert scales. Liver signal-to-noise ratio (SNR), lesion tumour-to-background ratio (TBR), and contrast-to-noise ratio (CNR) were calculated. Non-inferiority (NI) versus O120 was prespecified for overall quality (Δ = -0.5) and lesion CNR (ratio lower bound 0.90). Time-matched DPR versus POST and DL versus OSEM were also assessed. Agreement with O120 was evaluated using Lin's concordance correlation coefficient (CCC) and Bland-Altman analysis.

Results: Both DPR and POST achieved higher reader scores than time-matched OSEM. Inter-reader agreement was substantial to almost perfect. POST was superior at 30 s, whereas DPR was at 60 s. D60 and P30 met both NI margins, whereas D30 failed overall quality and P60 failed CNR. Concordance with O120 was excellent by CCC, and Bland-Altman showed small biases with limited proportional effects. CNR and SNR increased monotonically with DPR, while POST yielded gains at 30 s that attenuated at 60 s. TBR improvements were confined to DPR.

Conclusion: Both DPR and POST improved or preserved image quality while enabling scan-time reduction, with excellent agreement with the clinical reference. POST is supported for 1/4 acquisition time, whereas DPR is favored from 1/2 time onward.