EJNMMI Physics最新文献

Background: This study focuses on modeling a population-based input function (PBIF) in dynamic ⁶⁸Ga-DOTATOC PET/CT exams, with the aim of developing clinically adoptable protocols. The PBIF is derived from an image-derived input function (IDIF), ensuring a non-invasive and standardized approach to tracer kinetic modeling.

Methods: Patients with well-differentiated neuroendocrine tumors were included from the GAPETNET clinical trial (n = 37), divided into a PBIF modeling group (n = 20) and an independent validation group (n = 17). Dynamic whole-body (dWB) PET/CT imaging was performed using a Vision 600 PET/CT system. A population-based input function (PBIF) was modeled using the Feng approach and scaled to individual patient-specific IDIFs over two different time windows (sPBIF_3 - 7: 25-55 min, sPBIF_5 - 7: 40-55 min). The scaled PBIF was normalized to IDIF data from 6 to 55 min post-injection. A full individual patient-specific IDIF using data from 0 to 70 min post-injectionwas used as the reference for AUC and Ki comparisons. IDIFs and scaled PBIFs were compared by assessing the area under the curve (AUC) and radiotracer influx rate (Ki). Linear correlation and Bland-Altman analyses were conducted for AUC and Ki comparisons. Additionally, Mann-Whitney tests were performed to compare Ki values obtained with IDIF and sPBIF in both tumoral lesions and physiological organs.

Results: The lowest mean relative AUC bias was observed with sPBIF_3 - 7, calculated to be 2.7 ± 7.9%, and was slightly higher with sPBIF_5 - 7 (7.35 ± 8.58%). The correlation coefficient (R²) with the sPBIFs was high, with a minimum of 0.95 for the sPBIF_5 - 7. When analyzing K_i metrics, biases tended to be lower with the 40-55 min time window (Mean ± SD bias = 1.61 ± 3.33 for K_{i max} and 1.64 ± 2.96 for K_{i mean}). No significant differences in K_i values was observed with the sPBIFs compared to the IDIF (p > 0.05), for either tumoral lesion or physiological organs.

Conclusion: Our study has demonstrated the feasibility the PBIF approach to estimate tumor or physiological Ki values from a shortened dWB ⁶⁸Ga-DOTATOC PET acquisition, using the Feng model.

Purpose: While data-driven motion correction (DDMC) techniques have proven to enhance the visibility of lesions affected by motion, their impact on overall detectability remains unclear. This study investigates whether DDMC improves lesion detectability in PET-CT using FDG-18F.

Method: A moving platform simulated respiratory motion in a NEMA-IEC body phantom with varying amplitudes (0, 7, 10, 20, 30 mm) and target-to-background ratios (2, 5, 10.5). Scans were reconstructed with and without DDMC, and the spherical targets' maximal and mean recovery coefficient (RC) and contrast-to-noise ratio (CNR) were measured.

Results: DDMC results in higher RC values in the target spheres. CNR values increase for small, high-motion affected targets but decrease for larger spheres with smaller amplitudes. A sub-analysis shows that DDMC increases the contrast of the sphere along with a 36% increase in background noise.

Conclusion: While DDMC significantly enhances contrast (RC), its impact on detectability (CNR) is less profound due to increased background noise. CNR improves for small targets with high motion amplitude, potentially enhancing the detectability of low-uptake lesions. Given that the increased background noise may reduce detectability for targets unaffected by motion, we suggest that DDMC reconstructions are used best in addition to non-DDMC reconstructions.

Segmentation of the whole-kidney parenchyma (WKP) is considered the reference method for kidney dosimetry of radiopharmaceuticals, as it provides the average absorbed dose to the fully delineated WKP. However manual segmentation of the WKP is time consuming, and automated segmentation requires operator verification and potential manual adjustments to the VOI. The aim is to determine if a small volume of interest (SV) method can generate similar kidney absorbed doses as the WKP method.

Methods: We obtained SPECT/CT of 18 patients at 24, 48, and 168 h after injection of [¹⁷⁷Lu]Lu-DOTATATE (7.3-7.8 GBq). The SPECTs were corrected for attenuation, scatter, and collimator detector response with Monte Carlo-based OSEM reconstruction (ASCC-SPECT) and post-filtered with a 0- to 12-mm Gaussian filter or were only attenuation corrected with a Hann post-filter (AC-SPECT). Kidney dosimetry based on the manually segmented WKP was used as reference method. Recovery coefficients (RCs) for each WKP were determined by Monte Carlo simulations, and normalisation factors, NFs, for SVs were determined relative to the WKP method. Kidney absorbed doses were estimated based on measured activity concentrations fitted using the mono-exponential function. The accuracy of the absorbed dose estimates for the SV methods, corrected with the NFs, were assessed using the standard deviation of the percentage difference in agreement with the reference method across all kidneys. Accuracy for kidney dosimetry using the SV method was calculated based on 1-5 VOIs with volumes of 4 mL (SV₄), 2 mL (SV₂), and 0.6 mL (SV_0.6).

Results: The mean RCs of the WKP volumes (31-243 mL) in non-filtered ASCC-SPECT and AC-SPECT were 0.85 (0.73-0.90) and 0.62 (0.46-0.51), respectively. In non-filtered images, the absorbed dose was overestimated by a factor of 1.22. However, applying a Gaussian filter with a kernel size of approximately 5 mm yielded absorbed dose estimates comparable to the reference WKP method. The accuracy of kidney dosimetry calculation based on one SV₄ on each SPECT data-point was 12%. The accuracy improved as the number of VOIs increased from 1 to 5. With the SV₂ method, using a mean of 5 VOIs per kidney parenchyma, the accuracy was 8.3%.

Conclusion: The small volume of interest (SV) method can provide absorbed dose estimates comparable to the whole-kidney parenchyma (WKP) method when optimized. Non-filtered images overestimated doses by 1.22, but applying a 5 mm Gaussian filter aligned SV results with the WKP method. Using multiple VOIs improved accuracy, with five 2 mL SVs achieving 8.3%. The SV method provides a less time-consuming alternative to WKP; however, its implementation is recommended to be validated and adjusted against a reference method.

Background: ²²⁵Ac is a radionuclide that can be utilized in targeted alpha therapy (TAT). To accurately assess the absorbed dose and radiation effects in TAT, it is necessary to calculate the relative biological effectiveness (RBE). This study aims to calculate the RBE of ²²⁵Ac and its decay daughters with a Monte Carlo method.

Methods: This study employed the NASIC program to perform microdosimetric simulations of ¹⁷⁷Lu, ²²⁵Ac and its decay daughters in a cell population. Absorbed doses and lineal energy spectra in the cell nucleus were obtained for eight different radionuclides, three different cells, and six radionuclide spatial distribution. The RBE was then calculated using a modified stochastic microdosimetric kinetic model (mSMKM).

Results: The results indicated that variations in radionuclide distribution had a greater impact on the absorbed dose in the cell nucleus. Taking ²²⁵Ac in V79 cells as an example, the maximum differences in RBE and absorbed dose due to different distributions were 10% and 80%, respectively. For V79 cells, with a uniform distribution of radionuclides within the cell, the RBE_M, i.e. RBE at zero dose, of ²²⁵Ac was 6.91 ± 0.04. In its decay chain, the RBE_M was 6.81 ± 0.04 for ²²¹Fr, 6.67 ± 0.02 for ²¹⁷At, 6.43 ± 0.05 for ²¹³Po, and 5.91 ± 0.09 for ²¹³Bi. The β-emitting radionuclides ²⁰⁹Tl and ²⁰⁹Pb had RBE close to 1.

Conclusions: RBE of each radionuclide in ²²⁵Ac decay chain was evaluated separately with a Monte Carlo track structure code. The RBE of ²²⁵Ac and its decay daughters was found to be influenced by absorbed dose, radionuclide distribution, and cell type. The intracellular distribution of radionuclides had influence on the magnitude of RBE, but was less significant than its impact on the absorbed dose. Additionally, there were differences in the RBE of each radionuclide in the ²²⁵Ac decay chain that could not be neglected. These findings contribute to the calculation of RBE-weighted doses and the assessment of biological effects in ²²⁵Ac-based TAT.

Background: ¹⁶⁶Ho-poly-L-lactic acid microspheres (¹⁶⁶Ho-PLLA) offer the advantage of using the same microspheres for both Scout and Therapeutic Administrations (SA and TA) in radioembolization compared to ⁹⁰Y. This study aimed to quantify and correct dead time (DT) effects in dose estimation and assess the predictive power of SA on TA.

Methods: A 1.9 GBq ¹⁶⁶Ho-PLLA activity source was placed in a CIRS phantom and imaged over a week until activity reached 83 MBq, assessing DT effects. Fifteen patients with a single hepatic lesion underwent SA and TA two weeks apart with following SPECT/CT imaging. The mean absorbed dose (AD) and distribution were calculated using the Local Energy Deposition (LED) method for liver, healthy liver (HL) and tumor contours. Three methods were compared for TA AD estimation: no DT correction (M1), whole-image DT correction (M2), and DT correction only for tumor ROI counts (M3). Linear correlation and percentage differences (ΔD%) between SA and TA AD were analyzed. AD distributions in SA and TA were rigidly registered for gamma index analysis (Dose Difference of 10% and Distance to Agreement of 10 mm).

Results: DT effects were significant for activity above 250 MBq (> 11.5%). Strong linear correlations between mean AD values in SA and TA were observed across methods. ΔD% between SA and TA for the liver contour was - 8.6% (M1), 21.5% (M2), and 8.2% (M3). For the HL contour, ΔD% was 8.1% (M1) and 39.0% (M2), while for the tumor contour, it was - 20.1% (M1) and 0.0% (M2). Gamma index pass rates for the liver contour were 76% (M1), 89% (M2), and 92% (M3); for the HL contour, 80% (M1) and 75% (M2); and for the tumor contour, 70% (M1) and 87% (M2).

Conclusion: DT significantly affects TA dose estimation, particularly in tumors. Proper DT correction improves the accuracy of dosimetric evaluation of ¹⁶⁶Ho-PLLA for TA in liver and metastases, yielding dose values closer to those obtained in SA, despite the latter not being corrected for DT.

Background: Previous [⁶⁸Ga]Ga-DOTA-TATE PET/CT studies using ordered subset expectation maximization (OSEM3D) based reconstruction algorithms, demonstrated non-linear relations between body weight and image quality. Block Sequential Regularized Expectation Maximization (BSREM) algorithm reduces noise amplification during reconstruction. The impact of the reconstruction algorithm on the relation between image quality and patient size in [⁶⁸Ga]Ga-DOTA-TATE PET/MR may differ from PET/CT and OSEM3D. Therefore, the aim of this study is to investigate the relation between patient size and image quality in OSEM3D and BSREM [⁶⁸Ga]Ga-DOTA-TATE PET/MR reconstructions.

Methods: [⁶⁸Ga]Ga-DOTA-TATE PET/MR images of 55 patients were included. Images were reconstructed using OSEM3D (VUE Point FX SharpIR, 4 iterations, 28 subsets and 7 mm Gaussian filter) and BSREM (Q.Clear, β = 300). Liver signal-to-noise ratio (SNRliver) normalized for injected activity and acquisition time (SNRliver,norm) was measured to perform curve fitting with patient-dependent parameters using fixed, linear and non-linear fit models, followed by Akaike's corrected information criterion (AICc) model selection.

Results: BSREM mean SNRliver was significantly (p < 0.001) higher than OSEM3D mean SNRliver. Body mass, the best patient-dependent parameter for both algorithms, clarified 40% (linear model) and 53% (non-linear model) of the variability in SNRliver,norm for OSEM3D and 20% (linear model) and 21% (non-linear model) for BSREM. AICc preferred a non-linear model for OSEM3D and a linear model for BSREM.

Conclusion: The image quality predictor body weight is a weaker predictor for BSREM than for OSEM3D image quality in [⁶⁸Ga]Ga-DOTA-TATE PET/MR. Therefore, a linear dosage regimen based on body weight is preferable for BSREM, whereas a quadratic dosage regimen based on body weight is optimal for OSEM3D.

Background: The nuclear medicine treatment based on ¹⁷⁷Lu-PSMA-617 has achieved excellent therapeutic effects on Prostate cancer (PCa), but there is a lack of three-dimensional dosimetry analysis of organs at risk (OARs) and distant metastatic lesions.

Results: This work establishes an accurate and personalized three-dimensional dose calculation method based on Monte Carlo simulation and multi-modal images of PCa patients with skull metastasis, and analyzes the three-dimensional dose distribution of metastatic lesions and OARs in the brain. Results show that due to the targeting characteristics of ¹⁷⁷Lu-PSMA-617, metastatic brain lesions of all patients can receive high radiation doses with the average dose of 0.05 mGy/MBq, and the dose distribution are relatively uniform with the average homogeneity index of 1.23. In addition, the radiation dose received by most OARs are much lower than that of metastatic lesions, but for parotid glands, the dose deposited are only 3.99 times less than that of metastatic lesions due to the high absorption of ¹⁷⁷Lu-PSMA-617.

Conclusion: Therefore, it is necessary to clarify the three-dimensional dose distribution of OARs and metastatic lesions, so as to optimize the activity injected and achieve the precise killing of metastatic lesions and protection of OARs.