EJNMMI Physics最新文献

Purpose: This study aims to evaluate the accuracy of four kidney depth measurement methods-nuclear medicine tomography, nuclear medicine lateral scanning, ultrasound, and Tonnesen's formula-based estimation-using CT measurements as the reference standard. Additionally, it investigates the feasibility of utilizing nuclear medicine tomography and lateral scanning for measuring kidney depth in ^99mTc-DTPA renal dynamic imaging.

Methods: Hollow kidney phantoms mimicking the shape and volume of adult kidneys were 3D printed and filled with ^99mTcO₄^- solution. These phantoms were then subjected to lateral scanning and nuclear medicine tomography using CZT (cadmium-zinc-telluride) SPECT/CT to determine the optimal post-processing method. Forty patients who underwent renal dynamic imaging were recruited for the study. Renal depths were derived from ultrasound, lateral imaging, nuclear medicine tomography, formula-based estimation, and CT measurements. The renal depths obtained through these four methods were for correlation with CT-measured renal depths. Additionally, the absolute differences between renal depths obtained by each method and the CT standard were analyzed and compared across groups.

Results: Using kidney phantoms, nuclear medicine tomography images were processed with a Butterworth filter (cutoff frequency = 0.6), and renal outlines in lateral images was manually delineated. In the clinical validation phase, correlation coefficients indicated strong associations between renal depths measured by nuclear medicine tomography (left kidney: R = 0.885, P < 0.05; right kidney: R = 0.927, P < 0.05) and lateral scanning (left kidney: R = 0.933, P < 0.05; right kidney: R = 0.956, P < 0.05) compared to CT measurements. The difference in kidney depth between nuclear medicine tomography and CT measurements were the smallest and statistically significant (left kidney: 0.69 ± 0.51; right kidney: 0.58 ± 0.41, P < 0.05).

Conclusion: Using ordered subset expectation maximization (OSEM) in conjunction with a Butterworth filter (fc = 0.6) as the post-processing method, nuclear medicine tomography enables more accurate renal depth measurements without increasing the radiation dose to patients.

Background: The radiation exposure of nuclear medicine personnel, especially concerning extremity doses, has been a significant focus over the past two decades. This study addresses the evolving practice of NM, particularly with the rise of radionuclide therapy and theranostic procedures, which involve a variety of radionuclides such as ⁶⁸Ga, ¹⁷⁷Lu, and ¹³¹I. Traditional studies have concentrated on common radioisotopes like ^99mTc, ¹⁸F, and ⁹⁰Y, but there is limited data on these radionuclides, which are more and more frequently used. This study, part of the European SINFONIA project, aims to fill this gap by providing new dosimetry data through a multicenter approach. The research monitors extremity doses to hands, eye lens doses, and whole-body doses in nuclear medicine staff handling ⁶⁸Ga, ¹⁷⁷Lu, and ¹³¹I. It examines the type of activities performed and the protective measures used. The study extrapolates measured doses to annual doses, comparing them with annual dose limits, and assesses the contribution of these specific procedures to the overall occupational dose of nuclear medicine personnel.

Results: Measurements were conducted from November 2020 to August 2023 across nine hospitals. The highest whole-body, eye lens and extremity doses were observed for ⁶⁸Ga. Average maximum extremity doses, normalized per manipulated activity, were found of 6200 µSv/GBq, 30 µSv/GBq and 260 µSV/GBq for ⁶⁸Ga, ¹⁷⁷Lu and ¹³¹I, respectively. Average whole-body doses stayed below 60 µSv/GBq for all 3 isotopes and below 200 µSv/GBq for the eye lens dose. The variation in doses also depends on the task performed. For ⁶⁸Ga there is a risk of reaching the annual dose limit for skin dose during synthesis and dispensing.

Conclusions: This study's measurement campaigns across various European countries have provided new and extensive occupational dosimetry data for nuclear medicine staff handling ⁶⁸Ga, ¹⁷⁷Lu and ¹³¹I radiopharmaceuticals. The results indicate that ⁶⁸Ga contributes significantly to the global occupational dose, despite its relatively low usage compared to other isotopes. Staff working in radiopharmacy hot labs, labeling and dispensing ¹⁷⁷Lu contribute less to the finger dose compared to other isotopes.

Background: ¹⁷⁷Lu-based radiopharmaceuticals (RPs) are the most used for targeted radionuclide therapy (TRT) due to their good response rates. However, the worldwide availability of ¹⁷⁷Lu is limited. ¹⁶¹Tb represents a potential alternative for TRT, as it emits photons for SPECT imaging, β^--particles for therapy, and also releases a significant yield of internal conversion (IE) and Auger electrons (AE). This research aimed to evaluate cell dosimetry with the MIRDcell code considering a realistic localization of three ¹⁶¹Tb- and ¹⁷⁷Lu-somatostatin (SST) analogs in different subcellular regions as reported in the literature, various cell cluster sizes (25-1000 µm of radius) and percentage of labeled cells. Experimental values of the α- and β-survival coefficients determined by external beam photon irradiation were used to estimate the survival fraction (SF) of AR42J pancreatic cell clusters and micrometastases.

Results: The different localization of RPs labeled with the same radionuclide within the cells, resulted in only slight variations in the dose absorbed by the nuclei (AD_N) of the labeled cells with no differences observed in either the unlabeled cells or the SF. AD_N of labeled cells (MDLC) produced by ¹⁶¹Tb-RPs were from 2.8-3.7 times higher than those delivered by ¹⁷⁷Lu-RPs in cell clusters with a radius lower than 0.1 mm and 10% of labeled cells, due to the higher amount of energy emitted by ¹⁶¹Tb-disintegration in form of IE and AE. However, the ¹⁶¹Tb-RPs/¹⁷⁷Lu-RPs MDLC ratio decreased below 1.6 in larger cell clusters (0.5-1 mm) with > 40% labeled cells, due to the significantly higher ¹⁷⁷Lu-RPs cross-irradiation contribution. Using a fixed number of disintegrations, SFs of ¹⁶¹Tb-RPs in clusters with > 40% labeled cells were lower than those of ¹⁷⁷Lu-RPs, but when the same amount of emitted energy was used no significant differences in SF were observed between ¹⁷⁷Lu- and ¹⁶¹Tb-RPs, except for the smallest cluster sizes.

Conclusions: Despite the emissions of IE and AE from ¹⁶¹Tb-RPs, their localization within different subcellular regions exerted a negligible influence on the AD_N. The same cell damage produced by ¹⁷⁷Lu-RPs could be achieved using smaller quantities of ¹⁶¹Tb-RPs, thus making ¹⁶¹Tb a suitable alternative for TRT.

Background: The limited spatial resolution in SPECT images leads to partial volume effect (PVE), degrading the subsequent dosimetric accuracy. We aim to quantitatively evaluate PVE and partial volume corrections (PVC), i.e., recovery coefficient (RC)-PVC (RC-PVC), reblurred Van-Cittert (RVC) and iterative Yang (IY), in ¹⁷⁷Lu-PSMA-617 SPECT images.

Methods: We employed a geometrical cylindrical phantom containing five spheres (diameters ranging from 20 to 40 mm) and 40 XCAT phantoms with various anatomical variations and activity distributions. SIMIND Monte Carlo code was used to generate realistic noisy projections. In the clinical study, sequential quantitative SPECT/CT imaging at 4 time-points post ¹⁷⁷Lu-PSMA-617 injections were analyzed for 10 patients. Iterative statistical reconstruction methods were used for reconstruction with attenuation, scatter and geometrical collimator detector response corrections, followed by post-filters. The RC-curves were fit based on the geometrical phantom study and applied for XCAT phantom and clinical study in RC-PVC. Matched and 0.5-2.0 voxels (2.54-10.16 mm) mismatched sphere masks were deployed in IY. The coefficient of variation (CoV) was measured on a uniform background on the geometrical phantom. RCs of spheres and mean absolute activity error (MAE) of kidneys and tumors were evaluated in simulation data, while the activity difference was evaluated in clinical data before and after PVC.

Results: In the simulation study, the spheres experienced significant PVE, i.e., 0.26 RC and 0.70 RC for the 20 mm and 40 mm spheres, respectively. RVC and IY improved the RC of the 20 mm sphere to 0.37 and 0.75 and RC of the 40 mm sphere to 0.96 and 1.04. Mismatch in mask increased the activity error for all spheres in IY. RVC increased noise and caused Gibbs ringing artifacts. For XCAT phantoms, both RVC and IY performed comparably and were superior to RC-PVC in reducing the MAE of the kidneys. However, IY and RC-PVC outperformed RVC for tumors. The XCAT phantom study and clinical study showed a similar trend in the kidney and tumor activity differences between non-PVC and PVC.

Conclusions: PVE greatly impacts activity quantification, especially for small objects. All PVC methods improve the quantification accuracy in ¹⁷⁷Lu-PSMA SPECT.

Purpose: Total-body dynamic positron emission tomography (PET) imaging with total-body coverage and ultrahigh sensitivity has played an important role in accurate tracer kinetic analyses in physiology, biochemistry, and pharmacology. However, dynamic PET scans typically entail prolonged durations ([Formula: see text]60 minutes), potentially causing patient discomfort and resulting in artifacts in the final images. Therefore, we propose a dynamic frame prediction method for total-body PET imaging via deep learning technology to reduce the required scanning time.

Methods: On the basis of total-body dynamic PET data acquired from 13 subjects who received [⁶⁸Ga]Ga-FAPI-04 (⁶⁸Ga-FAPI) and 24 subjects who received [⁶⁸Ga]Ga-PSMA-11 (⁶⁸Ga-PSMA), we propose a bidirectional dynamic frame prediction network that uses the initial and final 10 min of PET imaging data (frames 1-6 and frames 25-30, respectively) as inputs. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed as evaluation metrics for an image quality assessment. Moreover, we calculated parametric images (⁶⁸Ga-FAPI: [Formula: see text], ⁶⁸Ga-PSMA: [Formula: see text]) based on the supplemented sequence data to observe the quantitative accuracy of our approach. Regions of interest (ROIs) and statistical analyses were utilized to evaluate the performance of the model.

Results: Both the visual and quantitative results illustrate the effectiveness of our approach. The generated dynamic PET images yielded PSNRs of 36.056 ± 0.709 dB for the ⁶⁸Ga-PSMA group and 33.779 ± 0.760 dB for the ⁶⁸Ga-FAPI group. Additionally, the SSIM reached 0.935 ± 0.006 for the ⁶⁸Ga-FAPI group and 0.922 ± 0.009 for the ⁶⁸Ga-PSMA group. By conducting a quantitative analysis on the parametric images, we obtained PSNRs of 36.155 ± 4.813 dB (⁶⁸Ga-PSMA, [Formula: see text]) and 43.150 ± 4.102 dB (⁶⁸Ga-FAPI, [Formula: see text]). The obtained SSIM values were 0.932 ± 0.041 (⁶⁸Ga-PSMA) and 0.980 ± 0.011 (⁶⁸Ga-FAPI). The ROI analysis conducted on our generated dynamic PET sequences also revealed that our method can accurately predict temporal voxel intensity changes, maintaining overall visual consistency with the ground truth.

Conclusion: In this work, we propose a bidirectional dynamic frame prediction network for total-body ⁶⁸Ga-PSMA and ⁶⁸Ga-FAPI PET imaging with a reduced scan duration. Visual and quantitative analyses demonstrated that our approach performed well when it was used to predict one-hour dynamic PET images. https://github.com/OPMZZZ/BDF-NET .

Background: Accurate pharmacokinetic modelling in PET necessitates measurements of an input function, which ideally is acquired non-invasively from image data. For hepatic pharmacokinetic modelling two input functions need to be considered, to account for the blood supply from the hepatic artery and portal vein. Image-derived measurements at the portal vein are challenging due to its small size and image artifacts caused by respiratory motion. In this work we seek to demonstrate, using phantom experiments, how a dedicated PET/MR protocol can tackle these challenges and potentially provide input function measurements of the portal vein in a clinical setup.

Methods: A custom 3D printed PET/MR phantom was constructed to mimic the liver and portal vein. PET/MR acquisitions were made with emulated respiratory motion. The PET/MR imaging protocol consisted of high-resolution anatomical MR imaging of the portal vein, followed by a PET acquisition in parallel to a dedicated motion-tracking MR sequence. Motion tracking and deformation information were extracted from PET data and subsequently used in PET reconstruction to produce dynamic series of motion-free PET images. Anatomical MR images were used post PET reconstruction for partial volume correction of the input function measurements.

Results: Reconstruction of dynamic PET data with motion-compensation provided nearly motion-free series of PET frame data, suitable for image derived input function measurements of the portal vein. After partial volume correction, the individual input function measurements were within a 16.1% error range from the true activity in the portal vein compartment at the time of PET acquisition.

Conclusion: The proposed protocol demonstrates clinically feasible PET/MR imaging of the liver for pharmacokinetic studies with accurate quantification of the portal vein input function, including correction for respiratory motion and partial volume effects.

Background: The aim was to compare bias and precision for ¹⁷⁷Lu-SPECT activity-concentration estimation using a dual-headed Anger SPECT system and a ring-configured CZT SPECT system. This was investigated for imaging at 208 keV and 113 keV, respectively.

Methods: Phantom experiments were performed on a GE Discovery 670 system with 5/8'' NaI(Tl) crystal (dual-headed Anger system) and a GE StarGuide (ring-configured CZT system). Six spheres (1.2 mL to 113 mL) in a NEMA PET body phantom were filled with ^99mTc and ¹⁷⁷Lu, separately. Mean relative errors and coefficients of variation (CV) in estimated sphere activity concentration were studied over six timeframes of 10 min each for the two systems. For ¹⁷⁷Lu, similar acquisitions were also performed for an anthropomorphic phantom with two spheres (10 mL and 25 mL) in a liver with non-radioactive background and a sphere-to-background ratio of 15:1. Tomographic reconstruction was performed using OS-EM with 10 subsets with compensation for attenuation, scatter, and distance-dependent spatial resolution. For the Anger system, up to 40 iterations were used and for the ring-configured CZT system up to 30 iterations were used.

Results: The two systems showed similar mean relative errors and CVs for ¹⁷⁷Lu when using an energy window around 208 keV, while the ring-configured system demonstrated a lower bias for a similar CV compared to the Anger system for ^99mTc and for ¹⁷⁷Lu when using an energy window around 113 keV. However, total activity in the phantom tended to be overestimated in both systems for these cases.

Conclusions: The ring-configured CZT system is a viable alternative to the dual-headed Anger system equipped with medium-energy collimators for ¹⁷⁷Lu-SPECT and shows a potential advantage for activity-concentration estimation when operated at 113 keV. However, further consideration of the preservation of total activity is warranted.

Misregistration between CT and PET in PET/CT is mainly caused by respiratory motion or irregular respiration during the CT scan in PET/CT. Other than repeat CT, repeat PET/CT, or data-driven gated (DDG) CT, there is no practical approach to mitigate the misregistration artifacts and subsequent CT attenuation correction (CTAC) of the PET data. DDG PET derives a respiratory motion model based on the multiple phases of PET images without hardware gating and it allows for a potential correction of the misregistration artifacts based on the respiratory motion model. The purpose of this commentary was to compare the recent two publications on matching the random phase of helical CT with one of the PET phases derived from the motion model of DDG PET and warping the misregistered helical CT for CTAC of and registration with PET or DDG PET. The two publications were similar in methodology. However, the data sets used for the comparison were different and could potentially impact their conclusions.

Background: This study investigates the integration of Artificial Intelligence (AI) in compensating the lack of time-of-flight (TOF) of the GE Omni Legend PET/CT, which utilizes BGO scintillation crystals.

Methods: The current study evaluates the image quality of the GE Omni Legend PET/CT using a NEMA IQ phantom. It investigates the impact on imaging performance of various deep learning precision levels (low, medium, high) across different data acquisition durations. Quantitative analysis was performed using metrics such as contrast recovery coefficient (CRC), background variability (BV), and contrast to noise Ratio (CNR). Additionally, patient images reconstructed with various deep learning precision levels are presented to illustrate the impact on image quality.

Results: The deep learning approach significantly reduced background variability, particularly for the smallest region of interest. We observed improvements in background variability of 11.8 $\%$ , 17.2 $\%$ , and 14.3 $\%$ for low, medium, and high precision deep learning, respectively. The results also indicate a significant improvement in larger spheres when considering both background variability and contrast recovery coefficient. The high precision deep learning approach proved advantageous for short scans and exhibited potential in improving detectability of small lesions. The exemplary patient study shows that the noise was suppressed for all deep learning cases, but low precision deep learning also reduced the lesion contrast (about -30 $\%$ ), while high precision deep learning increased the contrast (about 10 $\%$ ).

Conclusion: This study conducted a thorough evaluation of deep learning algorithms in the GE Omni Legend PET/CT scanner, demonstrating that these methods enhance image quality, with notable improvements in CRC and CNR, thereby optimizing lesion detectability and offering opportunities to reduce image acquisition time.