EJNMMI Physics最新文献

Purpose: Measuring the ortho-positronium (oPs) lifetime in human tissue bears the potential of adding clinically relevant information about the tissue microenvironment to conventional positron emission tomography (PET). Through phantom measurements, we investigate the voxel-wise measurement of oPs lifetime using a commercial long-axial field-of-view (LAFOV) PET scanner.

Methods: We prepared four samples with mixtures of Amberlite XAD4, a porous polymeric adsorbent, and water and added between 1.12 and 1.44 MBq of ¹²⁴I. The samples were scanned in two different setups: once with a couple of centimeters between each sample (15 min scan time) and once with all samples taped together (40 min scan time). For each scan, we determine the oPs lifetime for the full samples and at the voxel level. The voxel sizes under consideration are 10.0³ mm³, 7.1³ mm³ and 4.0³ mm³.

Results: Amberlite XAD4 allows the preparation of samples with distinct oPs lifetime. Using a Bayesian fitting procedure, the oPs lifetimes in the whole samples are 2.52 ± 0.03 ns, 2.37 ± 0.03 ns, 2.27 ± 0.04 ns and 1.82 ± 0.02 ns, respectively. The voxel-wise oPs lifetime fits showed that even with 4.0³ mm³ voxels the samples are clearly distinguishable and a central voxels have good count statistics. However, the situation with the samples close together remains challenging with respect to the spatial distinction of regions with different oPs lifetimes.

Conclusions: Our study shows that positronium lifetime imaging on a commercial LAFOV PET/CT is feasible using ¹²⁴I.

Background: This study investigated, mainly for quantitative ^99mTc explorations with a ring CZT SPECT system (GE HealthCare Starguide), the use of a narrow symmetric or a fully asymmetric energy window to reject scattered photons. The results were compared with the manufacturer's post-acquisition dual energy window approach.

Methods: Two uniform and two cold and hot rod contrast cylindrical phantoms of various sizes were scanned with the Starguide system to acquire a very high number of counts. After rebinning the list-mode files for different energy windows, data were reconstructed with manufacturer's iterative algorithm including attenuation correction, resolution recovery and eventually scatter correction, but without any regularization technique. Cold rod residual scatter fraction, hot and cold rod contrast recovery coefficient, coefficient of variation in phantom uniform areas and quantification accuracy using calibration with one of the homogeneous phantoms were, among others, computed.

Results: Narrow symmetric photopeak-centred windows or fully asymmetric (≥ 140 keV) window led, on one hand, to decreased scatter residual fraction and sensitivity and, on the other hand, to increased noise, cold and hot recovery coefficients when compared to a standard 15-20% wide symmetric window. With a 6-7% wide symmetric window we obtained very comparable results to the dual energy window scatter correction used by the manufacturer for all measured parameters, but larger recovery coefficients especially for small hot objects in a cold background. Similar results were obtained with the fully asymmetric window at the cost of a higher noise level resulting from a drastic reduction of the sensitivity.

Conclusions: Narrow symmetric or asymmetric energy windows were found an interesting alternative to the standard dual energy window method to reject ^99mTc scattered photons. As a key feature, they allowed to avoid the erasing of small hot objects in a null background that was observed with the standard dual energy window scatter correction.

Background: In this study, we further developed an artificial intelligence (AI)-based method for the detection and quantification of tumours in the prostate, lymph nodes and bone in prostate-specific membrane antigen (PSMA)-targeting positron emission tomography with computed tomography (PET-CT) images.

Methods: A total of 1064 [¹⁸F]PSMA-1007 PET-CT scans were used (approximately twice as many compared to our previous AI model), of which 120 were used as test set. Suspected lesions were manually annotated and used as ground truth. A convolutional neural network was developed and trained. The sensitivity and positive predictive value (PPV) were calculated using two sets of manual segmentations as reference. Results were also compared to our previously developed AI method. The correlation between manually and AI-based calculations of total lesion volume (TLV) and total lesion uptake (TLU) were calculated.

Results: The sensitivities of the AI method were 85% for prostate tumour/recurrence, 91% for lymph node metastases and 61% for bone metastases (82%, 86% and 70% for manual readings and 66%, 88% and 71% for the old AI method). The PPVs of the AI method were 85%, 83% and 58%, respectively (63%, 86% and 39% for manual readings, and 69%, 70% and 39% for the old AI method). The correlations between manual and AI-based calculations of TLV and TLU ranged from r = 0.62 to r = 0.96.

Conclusion: The performance of the newly developed and fully automated AI-based method for detecting and quantifying prostate tumour and suspected lymph node and bone metastases increased significantly, especially the PPV. The AI method is freely available to other researchers ( www.recomia.org ).

Purpose: The lack of standardized dosimetry workflows in lutetium-177 ( $_{}^{177}$ Lu) radiopharmaceutical therapies results in inconsistent absorbed doses and limits treatment planning. This study aims to evaluate the accuracy and variability of a single-time-point ¹⁷⁷Lu SPECT/CT commercial workflow to help harmonize its protocol.

Methods: The dosimetry workflow evaluated in this study predominatly followed that of MIM SurePlan^TM MRT. ¹⁷⁷Lu SPECT/CT images of a Jaszczak and a NEMA phantoms were acquired in GE 670 DR scanner. Absorbed dose (Gy/MBq/s) was calculated in the background and sphere inserts with varied reconstruction iterations, calibrations, voxel-based dosimetry methods, and target volume segmentations. Ground truth absorbed doses were created using CT images and voxel S-value (VSV) water kernels. The validity of the density-corrected (DC) kernel for use in ground-truth dosimetry evaluations was further investigated. The accuracy and variability of the dosimetry workflow were evaluated using percent error and the coefficient of variation (CV) of mean absorbed doses.

Results: Mean absorbed dose accuracy improved for both the voxel-based VSV and local deposition (LD) methods until 480 equivalent iterations for all target volumes. DC kernel was found viable for creating reference absorbed doses. The calibration CV was 5.18% when phantom and calibration regions were varied. The VSV method demonstrated absorbed doses that were 10 to 150% higher than those calculated with the LD method. The overall variability in absorbed dose reached up to 84% when reconstruction, calibration, dosimetry, and segmentation methods were varied.

Conclusions: A single dosimetry workflow has demonstrated markedly large variability in absorbed dose accuracy. By evaluating the accuracy of absorbed dose, our study helped to propose a harmonized MIM SurePlan^TM MRT workflow for single-time-point $_{}^{177}$ Lu SPECT/CT-based therapies.

Background: Simultaneous PET/MR imaging enables precise anatomical localization and PET quantification by reducing PET-to-MR misalignments. However, involuntary motion during scans may still cause misalignment and quantification imprecision. Current mutual information (MI)-based co-registration methods do not account for the tissue-specific uptake patterns of PET and therefore could result in suboptimal alignment. To address this, we proposed a novel image co-registration method, namely the tracer characteristic-based co-registration (TCBC) method, which takes advantage of specific PET uptake patterns within a selected anatomical region to improve the image alignment and PET quantification.

Results: TCBC was evaluated using simulation and in vivo ¹⁸F-Florbetapir PET/MR data from the OASIS-3 dataset. In simulations, TCBC demonstrated superior alignment accuracy with lower root mean square error and higher R-squared values compared to the conventional MI-based co-registration from FreeSurfer in recovering the simulated patient motion. In the retrospective human study, we evaluated the detectability of age-related amyloid burden in healthy controls under different co-registration methods as a demonstrative use case. TCBC significantly enhanced the detectability of age-related amyloid burden with stronger correlations across all five regions of evaluation, such as the medial orbitofrontal cortex (p < 0.001), precuneus (p = 0.004), and early amyloid-β composite (p = 0.002), compared to FSMC (p = 0.004, 0.007, and 0.006, respectively) and uncorrected (p = 0.378, 0.023, and 0.039, respectively) methods. Bootstrap analyses also confirmed TCBC's robustness in smaller samples, yielding tighter confidence intervals and lower means of p-values, such as 0.032 (95% CI: 0.029-0.035) in the precuneus and 0.008 (CI: 0.007-0.010) in the medial orbitofrontal cortex, outperforming FSMC (p = 0.046 with CI: 0.042-0.049, and p = 0.040 with CI: 0.036-0.044, respectively).

Conclusions: The TCBC method reduces image misalignment, improves PET quantification, and may have a good potential for being applied to both research and clinical studies with simultaneous brain PET/MR.

Clinical trial number: Not applicable.

Purpose: Dopamine transporter (DAT) SPECT is a powerful tool for early diagnosis of Parkinson's disease (PD), while digital phantoms and Monte Carlo (MC) simulations can serve as important research tools. This study aims to develop a novel digital brain phantom population for ^99mTc-TRODAT-1 (^99mTc) and ¹²³I-ioflupane (¹²³I) brain SPECT, and to assess attenuation correction (AC) and scatter correction (SC) in DAT SPECT.

Methods: Striatum, brain background (gray and white matter), and cold regions (skull and cerebrospinal fluid) were segmented from 200 T1 MRI brain images from the PPMI dataset. Striatal binding ratio (SBR) values were retrospectively collected from 200 ¹²³I and 100 ^99mTc DAT SPECT patients with suspected PD symptoms from PPMI and a local hospital, respectively. Various activity values were assigned to the randomly paired segmented regions according to a range of SBR values based on the SPECT Visual Interpretation (VI) assessment scheme. The new phantom population was combined with MC simulation tool SIMIND to generate realistic noisy projections. Quantitative accuracy of reconstructed images with attenuation correction (AC) and scatter correction (SC) was assessed.

Results: A population of 1000 normal and abnormal PD phantoms was generated for both tracers. Visual comparisons and quantitative analyses demonstrated that simulated data exhibited high similarity to clinical data. Reconstructed images with AC + SC achieved the best quantitative results, followed by AC only, without AC and SC, and SC only.

Conclusion: The developed digital DAT SPECT phantom population can be served for a wide range of PD applications. Attenuation impacts image quality the most in DAT SPECT, while AC + SC is effective to enhance image quality and quantitative accuracy of DAT SPECT.

Background: In clinical nuclear medicine, reconstruction methods incorporating regularization terms have been widely investigated. However, searching for optimal hyperparameters for the entire examination is time-consuming and arduous because the optimal hyperparameters need to be determined experimentally and vary depending on factors, including the acquisition condition, reconstruction condition, and so on. In this study, we propose a row-action type automatic regularized expectation maximization method (RAREM). This method considers the acquisition conditions and object structure for determining the hyperparameters and does not require the user to set the hyperparameters experimentally. This study was conducted using numerical simulations and a real SPECT system METHODS: Total variation-expectation maximization (TV-EM) and modified-block sequential regularized EM (BSREM) were compared with RAREM, with the optimal hyperparameters of the two conventional reconstruction methods determined in advance from normalized root mean square error (NRMSE) results. This simulation examination utilized three types of phantoms with the number of counts and projections being examined in six ways each, resulting in a total of 108 conditions. The NRMSE and structural similarity index measure (SSIM) were used to evaluate of the simulation examination, and the Mann-Whitney U test was used for statistical analysis. In the real examination, two types of phantoms were used, and the number of projections was examined in three ways, for a total of six conditions. Contrast recovery coefficient (CRC) and specific binding ratio (SBR) were used to evaluate the real examination RESULTS: The NRMSE, CRC, and SBR of RAREM were equivalent to those of the conventional methods, and the SSIM of RAREM was equivalent to or better than that of the conventional methods, with significant differences in some cases. The results indicated that RAREM worked well with the evaluated object structure and considered the acquisition conditions CONCLUSION: In this study, an automatically controlled regularization reconstruction method was proposed. The proposed method does not require the user to set hyperparameters experimentally and can avoid the investigation of optimal hyperparameters; it is an alternative to conventional regularized methods in clinical.

Background: Single-photon emission computed tomography (SPECT) plays a crucial role in detecting bone metastases from lung cancer. However, its low spatial resolution and lesion similarity to benign structures present significant challenges for accurate segmentation, especially for lesions of varying sizes.

Methods: We propose a deep learning-based segmentation framework that integrates conditional adversarial learning with a multi-scale feature extraction generator. The generator employs cascade dilated convolutions, multi-scale modules, and deep supervision, while the discriminator utilizes multi-scale L1 loss computed on image-mask pairs to guide segmentation learning.

Results: The proposed model was evaluated on a dataset of 286 clinically annotated SPECT scintigrams. It achieved a Dice Similarity Coefficient (DSC) of 0.6671, precision of 0.7228, and recall of 0.6196 - outperforming both classical and recent adversarial segmentation models in multi-scale lesion detection, especially for small and clustered lesions.

Conclusion: Our results demonstrate that the integration of multi-scale feature learning with adversarial supervision significantly improves the segmentation of bone metastasis in SPECT imaging. This approach shows potential for clinical decision support in the management of lung cancer.

Background: Clinical PET scanners have long been explored for preclinical imaging, but limited spatial resolution and sensitivity have restricted their use for preclinical studies. The recent availability of total-body (TB) PET/CT scanners with extended axial fields of view (FOVs) has largely overcome sensitivity limitations, enabling potential new opportunities for small-animal imaging. This study evaluated the feasibility and performance of the Biograph Vision Quadra TB-PET/CT for rodent imaging compared to the dedicated small-animal PET scanner Inveon DPET.

Material and methods: Recovery coefficients (RC), image noise, and optimum image reconstruction parameters were assessed using the preclinical NEMA NU 4-2008 image quality phantom and a sub-cohort of three anesthetized mice as a proof-of-concept demonstrating the feasibility of the setup. In vivo quantification accuracy was evaluated by scanning nine frozen mice simultaneously in three different arrangements with the Quadra compared with individual scans at the Inveon. To ensure comparability, all mice were snap-frozen after 1 h uptake of [¹⁸F]FDG, scanned sequentially and individually at the Inveon (90 min p.i.), and subsequently scanned at the Quadra with decay-corrected acquisition times. SUV_mean and SUV_max values were determined for liver, muscle and brain regions on both systems. To evaluate potential position-dependent effects within the extended axial FOV, a single frozen mouse was scanned at multiple positions.

Results: Phantom rods ≥ 2 mm could be resolved with the Quadra, showing a comparable RC for larger structures, e.g. for the 5 mm rod of 1.17 compared to 1.09 (Inveon) when using point-spread-function modeling, whilst having lower noise of 5.1%SD vs 9.0%SD. No substantial position-dependent effects were detected in the phantom or single-mouse scan across the axial FOV. SUV_mean values were consistent between both scanner across all investigated organs, with liver and muscle uptake remaining comparable for frame durations down to 5 s. SUV_max values exhibited greater variability, with significant differences observed in muscle and brain regions.

Conclusion: Despite the lower spatial resolution of the clinical TB-PET/CT scanner (~ 3-4 mm) compared to the dedicated preclinical scanner (~ 1.5 mm), robust SUV_mean quantification was achievable. Together with successful in vivo imaging of anesthetized mice, these findings support the feasibility of using clinical TB-PET/CT for preclinical research, acknowledging spatial resolution as a limiting factor.