EJNMMI Physics最新文献

Background: Treatment with Ra-223 dichloride is approved for the therapy of castration resistant prostate cancer (CRPC) with symptomatic bone metastases and no known visceral metastases in Europe since 2013, and Ra-223 is under discussion for labelling other molecules and nanoparticles. The direct progeny of Ra-223 is Rn-219, also known as actinon, a radioactive noble gas with a half-life of 3.98 s. This Rn-219 can be exhaled by patients while Ra-223 is present in the blood. Hence, direct measurements for assessing the exhalation of Rn-219 were performed for the first time in the context of the non-interventional multicenter study "RAPSODY", a substudy to the international early access program, which aimed at assessing the radiation exposure of relatives of patients suffering from castration resistant prostate cancer with bone metastases and treated with Ra-223 dichloride in an outpatient setting, in order to investigate if this kind of method is functional and tolerated by the patients.

Methods: Rn-219 was measured directly in patients' exhalations using Alphaguard radon monitors (Saphymo, formerly Genitron, Frankfurt, Germany), originally intended for the measurement of Rn-222, and custom-made breath-test kits. Measurements were performed 20-30 min p. i. and 3-4 h p. i. In total, datasets from 21 administrations in 14 patients were obtained.

Results: Although 75% of the measurement data 20-30 min p. i. and 35% of the measurement data 3-4 h p. i., respectively, were censored due to exceedance of the upper limit of the Alphaguards' measurement range in the applied measurement setup, statistical data were derived based on the assumption of lognormal distributions. For measurements 3-4 h p. i., mean activity concentrations of Rn-219 in exhaled breath of approx. 4.4 kBq/l were obtained. In measurements 20-30 min p. i., the expectation value of the activity concentration of approx. 6 kBq/l, derived by statistical methods, was higher.

Conclusions: Direct measurements using Alphaguard instruments are suitable for assessing the exhalation of Rn-219 by patients treated with Ra-223. The measurement method is well tolerated by the patients. Rn-219 is present in patients' exhalations. Our results are in accordance with published data obtained using other, indirect measurement methods.

Aim: The combination of positron emission tomography (PET) and magnetic resonance imaging (MRI) provides an innovation leap in the use of fertilized chicken eggs (in ovo model) in preclinical imaging as PET/MRI enables the investigation of the chick embryonal organ-specific distribution of PET-tracers. However, hybrid PET/MRI inheres technical challenges in quantitative in ovo PET such as attenuation correction (AC) for the object as well as for additional hardware parts present in the PET field-of-view, which potentially contribute to quantification biases in the PET images if not accounted for. This study aimed to investigate the influence of the different sources of attenuation on in ovo PET/MRI and assess the accuracy of MR-based AC for in ovo experiments.

Method: An in-house made chicken egg phantom was used to investigate the magnitude of self-attenuation and the influence of the MRI hardware on the PET signal. The phantom was placed in a preclinical PET/MRI system and PET acquisitions were performed without, and after subsequently adding the different hardware parts to the setup. Reconstructions were performed without any AC for the different setups and with subsequently incorporating the hardware parts into the AC. In addition, in ovo imaging was performed using [¹⁸F]FDG and [⁶⁸Ga]Ga-Pentixafor, and PET data was reconstructed with the different AC combinations. Quantitative accuracy was assessed for the phantom and the in ovo measurements.

Results: In general, not accounting for the self-attenuation of the egg and the hardware parts caused an underestimation of the PET signal of around 49% within the egg. Accounting for all sources of attenuation allowed a proper quantification with global offsets of 2% from the true activity. Quantification based on % injected dose per cc (%ID/cc) was similar for the in ovo measurements, regardless of whether hardware parts were included in AC or not, when the injected activity was extracted from the PET images. However, substantial quantification biases were found when the self-attenuation of the egg was not taken into account.

Conclusion: Self-attenuation of the egg and PET signal attenuation within the hardware parts of the MRI substantially influence quantitative accuracy in in ovo measurements. However, when compensating for the self-attenuation of the egg by a respective AC, a reliable quantification using %ID/cc can be performed even if not accounting for the attenuation of the hardware parts.

Background: The topic of the effect of the patient table on attenuation in myocardial perfusion imaging (MPI) SPECT is gaining new relevance due to deep learning methods. Existing studies on this effect are old, rare and only consider phantom measurements, not patient studies. This study investigates the effect of the patient table on attenuation based on the difference between reconstructions of phantom scans and polar maps of patient studies.

Methods: Jaszczak phantom scans are acquired according to quality control and MPI procedures. An algorithm is developed to automatically remove the patient table from the CT for attenuation correction. The scans are then reconstructed with attenuation correction either with or without the patient table in the CT. The reconstructions are compared qualitatively and on the basis of their percentage difference. In addition, a small retrospective cohort of 15 patients is examined by comparing the resulting polar maps. Polar maps are compared qualitatively and based on the segment perfusion scores.

Results: The phantom reconstructions look qualitatively similar in both the quality control and MPI procedures. The percentage difference is highest in the lower part of the phantom, but it always remains below 17.5%. Polar maps from patient studies also look qualitatively similar. Furthermore, the segment scores are not significantly different (p=0.83).

Conclusions: The effect of the patient table on attenuation in MPI SPECT is negligible.

Background: A recently released digital solid-state positron emission tomography/x-ray CT (PET/CT) scanner with bismuth germanate (BGO) scintillators provides an artificial intelligence (AI) based system for automatic patient positioning. The efficacy of this digital-BGO system in patient placement at the isocenter and its impact on image quality and radiation exposure was evaluated.

Method: The digital-BGO PET/CT with AI-based auto-positioning was compared (χ², Mann-Whitney tests) to a solid-state lutetium-yttrium oxyorthosilicate (digital-LYSO) PET/CT with manual patient positioning (n = 432 and 343 studies each, respectively), with results split into groups before and after the date of a recalibration of the digital-BGO auto-positioning camera. To measure the transverse displacement of the patient center from the scanner isocenter (off-centering), CT slices were retrospectively selected and automatically analyzed using in-house software. Noise was measured as the coefficient of variation within the liver of absolute Hounsfield units referenced to air. Radiation exposure was recorded as dose-length product (DLP). Off-centering measurements were validated by a phantom study.

Results: The phantom validation study gave < 1.6 mm error in 15 off-centering measurements. Patient off-centering was biased 1.92 ± 1.79 cm (mean ± standard deviation) in the posterior direction which was significantly different from the 0.22 ± 1.21 cm bias in the left lateral direction (p < 0.0001, Wilcoxon). After recalibration, 27% (38/140) of the studies had off-centering results > 2.5cm for the digital-BGO, which was significantly better than the 49% (143/292, p < 0.001) before recalibration and better than for the digital-LYSO: 54% (119/222, p < 0.001) before and 55% (66/121, p < 0.001) after. On average, CT image quality was superior for non-obese patients who were most closely aligned with the isocenter: noise increased by 3.2 ± 0.1% for every 1 cm increase in off-centering. DLP increased by 144 ± 22 Gy cm for every 1 cm increase in anterior off-centering.

Conclusion: AI-based automatic patient positioning in a digital-BGO PET/CT scanner significantly reduces patient off-centering, thereby improving image quality and ensuring proper radiation exposure.

Background: The renewed interest in BGO scintillators for TOF-PET is driven by the improved Cherenkov photon detection with new blue-sensitive SiPMs. However, the slower scintillation light from BGO causes significant time walk with leading edge discrimination (LED), which degrades the coincidence time resolution (CTR). To address this, a time walk correction (TWC) can be done by using the rise time measured with a second threshold. Deep learning, particularly convolutional neural networks (CNNs), can also enhance CTR by training with digitized waveforms. It remains to be explored how timing estimation methods utilizing one (LED), two (TWC), or multiple (CNN) waveform data points compare in CTR performance of BGO scintillators.

Results: In this work, we compare classical experimental timing estimation methods (LED, TWC) with a CNN-based method using the signals from BGO crystals read out by NUV-HD-MT SiPMs and high-frequency electronics. For $2\times 2\times 3{\text{mm}}^3$ crystals, implementing TWC results in a CTR of 129 ± 2 ps FWHM, while employing the CNN yields 115 ± 2 ps FWHM, marking improvements of 18 % and 26 %, respectively, relative to the standard LED estimator. For $2\times 2\times 20{\text{mm}}^3$ crystals, both methods yield similar CTR (around 240 ps FWHM), offering a $\sim$ 15 % gain over LED. The CNN, however, exhibits better tail suppression in the coincidence time distribution.

Conclusions: The higher complexity of waveform digitization needed for CNNs could potentially be mitigated by adopting a simpler two-threshold approach, which appears to currently capture most of the essential information for improving CTR in longer BGO crystals. Other innovative deep learning models and training strategies may nonetheless contribute further in a near future to harnessing increasingly discernible timing features in TOF-PET detector signals.

Purpose: The aim of the study was to investigate the value of SwiftScan Step-and-Shoot Continuous (SSC) scanning mode in enhancing image quality and to explore appropriate scanning parameters for reducing scan time.

Methods: This study was composed of a phantom study and two clinical tests. The differences in visual image quality scores, coefficient of variance (COV) of the background, image signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and recovery coefficient (RC) of the sphere were compared between SSC mode and traditional Step-and-Shoot (SS) mode in the phantom study. Various "shoot" acquisition times (5s, 10s, 15s) and "step" angles (3-degree, 6-degree, 9-degree) were evaluated and verified. In the clinical tests, bone tomography and parathyroid tomography were performed on 30 patients each. Differences in visual image quality scores, background COV, image SNR, CNR, and standardized uptake value (SUV) of lesions were compared between the two modes.

Results: In the phantom study, SSC mode demonstrated higher visual scores and significantly reduced background COV (P < 0.05), and significantly increased SNR and CNR (P < 0.05) compared to SS mode. No significant alteration in RC was observed (P > 0.05). In the clinical tests, no significant differences were found between the optimal SSC scan combination (10s "shoot" and 6-degree "step")/ (10s "shoot" and 3-degree "step") and the traditional SS scan combination (15s "shoot" and 6-degree "step")/ (15s "shoot" and 3-degree "step") in visual image quality scores, background COV, image SNR, CNR, and SUV of bone and parathyroid high uptake lesions (P > 0.05).

Conclusion: The SwiftScan SSC mode can reduce acquisition time by 33% while maintaining similar image quality and quantification accuracy compared to SS mode. An SSC scanning protocol with a 10s "shoot" acquisition and 6-degree "step" or with a 10s "shoot" acquisition and 3-degree "step" over a 360-degree rotation, is recommended for clinical use.

Background: There is a need for faster amyloid PET scans to reduce patients' discomfort, minimize movement artifacts, and increase throughput. The recently introduced uMI Panorama PET/CT system featuring enhanced spatial resolution and sub-200ps TOF offers the potential for shorter scan duration without sacrificing image quality or efficacy to detect Aβ deposition. The study aims to establish a faster acquisition protocol for [¹⁸F]florbetapir PET imaging using digital PET/CT scanner uMI Panorama, while ensuring adequate image quality and amyloid-β (Aβ) detectability comparable to the standard 10-minute scan.

Methods: Thirty-eight participants (29 Aβ positive and 9 Aβ negative) from a prospective dementia cohort at Peking Union Medical University Hospital underwent routine [¹⁸F]florbetapir PET scans using the uMI Panorama PET/CT scanner and a T1-weighted brain MRI scan. List-mode PET data were reconstructed into durations of 10 min, 2 min, 1 min, 45 s, and 30 s (G10min, G2min, G1min, G45s, G30s). Two trained nuclear medicine physicians independently evaluated the image quality using a 5-point scale and provided binary diagnosis. Standardized uptake value ratios (SUVr) of the composite cortex (frontal, lateral parietal, lateral temporal, and cingulate cortices) were calculated to discriminate Aβ status and coefficient of variation assessed objective image quality. Comparisons of image quality and Aβ detectability between various fast scan groups and G10min group were conducted.

Results: The subjective image quality evaluation and Aβ detectability results from the two physicians showed both good intra-reader and inter-reader agreements (Cohen's kappa coefficient: 0.759-1.000). The subjective and objective image qualities of the G2min scans were comparable to the G10min scans, whereas adequate image quality was achieved with the G1min and G45s scans (5-point score ≥ 3). Subjective visual diagnosis by two physicians yielded consistent accuracy for G10min, G2min, and G1min groups, but lower specificity for G45s and G30s groups. The objective detection of Aβ status by cortex SUVr across all scan durations maintained perfect discriminatory efficiency and relatively high effect size (Hedge's G: 2.48-2.54).

Conclusions: A 1-min ultra-fast scan is feasible for [¹⁸F]florbetapir PET imaging using uMI Panorama PET/CT, while maintaining adequate image quality and Aβ diagnostic efficiency.

Clinical trial registration: NCT05023564. Registered September 2022 https://clinicaltrials.gov/search?term=NCT05023564 .

Background: In the back projection step of the 3D PET reconstruction, all Lines of Responses (LORs) that go through a given voxel need to be identified and included in an integral. The standard Monte Carlo solution to this task samples stochastically the surfaces of the detector crystals and the volume of the voxel to search for valid LORs. To get a low noise Monte Carlo estimate, the number of samples needs to be very high, making the computational cost of the projection significant. In this paper, a novel deterministic projection algorithm called trapezoidal back projection (TBP) is proposed that replaces the extensive Monte Carlo sampling. Its goal is to determine all LORs that contribute to a given voxel together with their exact contribution weights. This is achieved by trapezoidal rasterization and a pre-computed look-up table.

Results: The precision and speed of the proposed TBP algorithm were compared to that of the Monte Carlo back projection of 1000, 10,000 and 100,000 samples. Measurements were run on a National Electrical Manufacturers Association (NEMA) NU 4-2008 image quality phantom as well as on a mouse acquisition. Results show that the TBP algorithm achieves the same low noise level (2.5 Uniformity %STD) as the Monte Carlo method with the highest sample number, but 13 times faster-the highest-precision Monte Carlo back projection takes 31.3 s, while TBP takes only 2.3 s on the NEMA NU 4-2008 image quality phantom of $200\times 200\times 333$ voxels.

Conclusion: The proposed deterministic TBP algorithm achieves a low noise level in a short runtime, thus it can be a promising solution for the back projection of the 3D PET reconstruction. Its performance advantage could be used to reduce either the reconstruction time, the data acquisition time, or the noise level of the image.

Purpose: PET imaging is a pivotal tool for biomarker research aimed at personalized medicine. Leveraging the quantitative nature of PET requires knowledge of plasma radiotracer concentration. Typically, the arterial input function (AIF) is obtained through arterial cannulation, an invasive and technically demanding procedure. A less invasive alternative, especially for [¹⁸F]FDG, is the image-derived input function (IDIF), which, however, often requires correction for partial volume effect (PVE), usually performed via venous blood samples. The aim of this paper is to present EMATA: Extraction and Modeling of Arterial inputs for Tracer kinetic Analysis, an open-source MATLAB toolbox. EMATA automates IDIF extraction from [¹⁸F]FDG brain PET images and additionally includes a PVE correction procedure that does not require any blood sampling.

Methods: To assess the toolbox generalizability and present example outputs, EMATA was applied to brain [¹⁸F]FDG dynamic data of 80 subjects, extracted from two distinct datasets (40 healthy controls, 40 glioma patients). Additionally, to compare with the reference standard, quantification using both IDIF and AIF was carried out on a third open-access dataset of 18 healthy individuals.

Results: EMATA consistently performs IDIF extraction across all datasets, despite differences in scanners and acquisition protocols. Remarkably high agreement is observed when comparing Patlak's K_i between IDIF and AIF (R²: 0.98 ± 0.02).

Conclusion: EMATA proved adaptability to different datasets characteristics and the ability to provide arterial input functions that can be used for reliable PET quantitative analysis.