PET imaging and quantification of small animals using a clinical SiPM-based camera

BACKGROUND

Small-animal PET imaging is an important tool in preclinical oncology. This study evaluated the ability of a clinical SiPM-PET camera to image several rats simultaneously and to perform quantification data analysis.

METHODS

Intrinsic spatial resolution was measured using 18F line sources, and image quality was assessed using a NEMA NU 4-2018 phantom. Quantification was evaluated using a fillable micro-hollow sphere phantom containing 4 spheres of different sizes (ranging from 3.95 to 7.86 mm). Recovery coefficients were computed for the maximum (Amax) and the mean (A50) pixel values measured on a 50% isocontour drawn on each sphere. Measurements were performed first with the phantom placed in the centre of the field of view and then in the off-centre position with the presence of three scattering sources to simulate the acquisition of four animals simultaneously. Quantification accuracy was finally validated using four 3D-printed phantoms mimicking rats with four subcutaneous tumours each. All experiments were performed for both 18F and 68Ga radionuclides.

RESULTS

Radial spatial resolutions measured using the PSF reconstruction algorithm were 1.80 mm and 1.78 mm for centred and off-centred acquisitions, respectively. Spill-overs in air and water and uniformity computed with the NEMA phantom centred in the FOV were 0.05, 0.1 and 5.55% for 18F and 0.08, 0.12 and 2.81% for 68Ga, respectively. Recovery coefficients calculated with the 18F-filled micro-hollow sphere phantom for each sphere varied from 0.51 to 1.43 for Amax and from 0.40 to 1.01 for A50. These values decreased from 0.28 to 0.92 for Amax and from 0.22 to 0.66 for A50 for 68 Ga acquisition. The results were not significantly different when imaging phantoms in the off-centre position with 3 scattering sources. Measurements performed with the four 3D-printed phantoms showed a good correlation between theoretical and measured activity in simulated tumours, with r2 values of 0.99 and 0.97 obtained for 18F and 68Ga, respectively.

CONCLUSION

We found that the clinical SiPM-based PET system was close to that obtained with a dedicated small-animal PET device. This study showed the ability of such a system to image four rats simultaneously and to perform quantification analysis for radionuclides commonly used in oncology.

Deep Learning Denoising Improves and Homogenizes Patient [18F]FDG PET Image Quality in Digital PET/CT

Given the constant pressure to increase patient throughput while respecting radiation protection, global body PET image quality (IQ) is not satisfactory in all patients. We first studied the association between IQ and other variables, in particular body habitus, on a digital PET/CT. Second, to improve and homogenize IQ, we evaluated a deep learning PET denoising solution (Subtle PET^TM) using convolutional neural networks. We analysed retrospectively in 113 patients visual IQ (by a 5-point Likert score in two readers) and semi-quantitative IQ (by the coefficient of variation in the liver, CV_liv) as well as lesion detection and quantification in native and denoised PET. In native PET, visual and semi-quantitative IQ were lower in patients with larger body habitus (p < 0.0001 for both) and in men vs. women (p ≤ 0.03 for CV_liv). After PET denoising, visual IQ scores increased and became more homogeneous between patients (4.8 ± 0.3 in denoised vs. 3.6 ± 0.6 in native PET; p < 0.0001). CV_liv were lower in denoised PET than in native PET, 6.9 ± 0.9% vs. 12.2 ± 1.6%; p < 0.0001. The slope calculated by linear regression of CV_liv according to weight was significantly lower in denoised than in native PET (p = 0.0002), demonstrating more uniform CV_liv. Lesion concordance rate between both PET series was 369/371 (99.5%), with two lesions exclusively detected in native PET. SUV_max and SUV_peak of up to the five most intense native PET lesions per patient were lower in denoised PET (p < 0.001), with an average relative bias of -7.7% and -2.8%, respectively. DL-based PET denoising by Subtle PET^TM allowed [¹⁸F]FDG PET global image quality to be improved and homogenized, while maintaining satisfactory lesion detection and quantification. DL-based denoising may render body habitus adaptive PET protocols unnecessary, and pave the way for the improvement and homogenization of PET modalities.

Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime

Introduction

The yield per elution of a 68Ge/68Ga generator decreases during its lifespan. This affects the number of patients injected per elution or the injected dose per patient, thereby negatively affecting the cost of examinations and the quality of PET images due to increased image noise. We aimed to investigate whether AI-based PET denoising can offset this decrease in image quality parameters.

Methods

All patients addressed to our PET unit for a 68Ga-DOTATOC PET/CT from April 2020 to February 2021 were enrolled. Forty-four patients underwent their PET scans according to Protocol_FixedDose (150 MBq) and 32 according to Protocol_WeightDose (1.5 MBq/kg). Protocol_WeightDose examinations were processed using the Subtle PET software (Protocol_WeightDoseAI). Liver and vascular SUV mean were recorded as well as SUVmax, SUVmean and metabolic tumour volume (MTV) of the most intense tumoural lesion and its background SUVmean. Liver and vascular coefficients of variation (CV), tumour-to-background and tumour-to-liver ratios were calculated.

Results

The mean injected dose of 2.1 (0.4) MBq/kg per patient was significantly higher in the Protocol_FixedDose group as compared to 1.5 (0.1) MBq/kg for the Protocol_WeightDose group. Protocol_WeightDose led to noisier images than Protocol_FixedDose with higher CVs for liver (15.57% ± 4.32 vs. 13.04% ± 3.51, p = 0.018) and blood-pool (28.67% ± 8.65 vs. 22.25% ± 10.37, p = 0.0003). Protocol_WeightDoseAI led to less noisy images than Protocol_WeightDose with lower liver CVs (11.42% ± 3.05 vs. 15.57% ± 4.32, p &lt; 0.0001) and vascular CVs (16.62% ± 6.40 vs. 28.67% ± 8.65, p &lt; 0.0001). Tumour-to-background and tumour-to-liver ratios were lower for protocol_WeightDoseAI: 6.78 ± 3.49 vs. 7.57 ± 4.73 (p = 0.01) and 5.96 ± 5.43 vs. 6.77 ± 6.19 (p &lt; 0.0001), respectively. MTVs were higher after denoising whereas tumour SUVmax were lower: the mean% differences in MTV and SUVmax were + 11.14% (95% CI = 4.84–17.43) and −3.92% (95% CI = −6.25 to −1.59).

Conclusion

The degradation of PET image quality due to a reduction in injected dose at the end of the 68Ge/68Ga generator lifespan can be effectively counterbalanced by using AI-based PET denoising.

Application of a new spectral deconvolution method for in vitro dosimetry in assessment of targeted alpha therapy

BACKGROUND

The improvement of in vitro assessment of targeted alpha therapy (reproducibility, comparability of experiments…) requires precise evaluation of the dose delivered to the cells. To answer this need, a previous study proposed an innovative dosimetry method based on α-spectroscopy and a specific deconvolution process to recover the spatial distribution of ²¹² Pb isotopes inside in vitro culture wells. Nevertheless, although promising, the deconvolution method was time consuming and only tested for a simple isotope decay chain.

PURPOSE

The purpose of this work is to propose a new matrix deconvolution method of α spectra based on a constrained-non-negative-maximum-likelihood decomposition, both faster and offering a greater modelling flexibility, allowing to study independently the kinetics of each of the daughter nuclides of complex decay chains (illustrated here with ²²³ Ra) in in vitro culture wells.

METHODS

Firstly, the performance of the new method was fully evaluated through Monte Carlo simulations of in vitro irradiations. Different spatial distributions of ²¹² Pb and ²²³ Ra, the corresponding α spectra measured by a silicon detector and the doses delivered to the cells were simulated with Geant4. The deconvolution results were then compared to the simulation results. Secondly, measurements were carried out in culture wells without cells containing 15 kBq of ²¹² Pb or 9.3 kBq of ²²³ Ra, placed above silicon detectors recording α spectra in real time. The matrix deconvolution was then applied to determine the spatial and temporal distribution of all α-emitting daughters of studied isotopes.

RESULTS

The matrix deconvolution was proved to recover the simulated distribution gradients, ensuring simulated doses within 3 % for both tested radionuclides, with errors on dose normally distributed around the reference value (consequently not exhibiting any bias), even in the case of complex decay chains as ²²³ Ra. The experimental study of ²¹² Pb and ²²³ Ra showed highly inhomogeneous distributions and time evolution of the concentration gradients, consistent with the previous study. Furthermore, it highlighted the complex kinetics of ²²³ Ra with different distributions of its α-emitting daughters (²¹⁹ Rn, ²¹⁵ Po, ²¹⁵ At, ²¹¹ Bi, ²¹¹ Po).

CONCLUSIONS

This study validates a new deconvolution method, fast and flexible, that proved to be accurate and reliable. This method allowed to reveal the complexity of isotopes kinetics in in vitro experiments, especially with complex decay chains. Experimental dosimetry, necessary to improve reliability of in vitro studies in targeted alpha therapy, is demonstrated to be feasible with the proposed method.

Feasibility of Imaging Small Animals on a 360° Whole-Body Cadmium Zinc Telluride SPECT Camera: a Phantom Study

PURPOSE

Single-photon emission computed tomography has found an important place in preclinical cancer research. Nevertheless, the cameras dedicated to small animals are not widely available. The present study aimed to assess the feasibility of imaging small animals by a newly released 360° cadmium zinc telluride camera (VERITON, Spectrum Dynamics, Israel) dedicated to human patients.

PROCEDURES

A cylindrical phantom containing hot spheres was used to evaluate the intrinsic performance of the camera first without the presence of background activity and then with two contrasts between background and hot spheres (1/4 and 1/10). Acquisitions were repeated with different scan durations (10 and 20 min), two tested radioisotopes (Tc-99 m and I-123), and a set of reconstruction parameters (10 iterations [i] 8 subsets [s], 10i16s, 10i32s). A 3D-printed phantom mimicking a rat with four subcutaneous tumours was then used to test the camera under preclinical conditions.

RESULTS

The results obtained from the micro-hollow sphere phantom showed that it was possible to visualize spheres with an inner diameter of 3.95 mm without background activity. Moreover, spheres with diameters of 4.95 mm can be seen in the condition of high contrast between background and spheres (1/10) and 7.86 mm with lower contrast (1/4). The rat-sized phantom acquisitions showed that 10- and 8-mm subcutaneous tumours were visible with a good contrast obtained for the two radioisotopes tested in this study. Both Tc-99 m and I-123 measurements demonstrated that a 10-min acquisition reconstructed with an ordered subset expectation maximization algorithm applying 10i32s was optimal to obtain sufficient image quality in terms of noise, resolution, and contrast.

CONCLUSION

Phantom results showed the ability of the system to detect sub-centimetre lesions for various radioisotopes. It seemed feasible to image small animals using a 360° cadmium zinc telluride gamma camera for preclinical cancer research purposes.

The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI

Simple Summary

Due to the central role of magnetic resonance Imaging (MRI) in the management of patients with cancer, waiting lists exceed clinically relevant delays. For this reason, many research groups and MRI manufacturers develop algorithms as resampling and denoising models to allow faster acquisition time without deterioration in image quality. Whereas these algorithms are available in all new MRI, it is not clear how they will impact image features as well as the validity of statistical model of radiomics which use deep images characteristics to predict treatment outcome. The aim of this study was to develop resampling and denoising deep learning (DL) models and evaluate their impact on radiomics from post-Gd-T1w-MRI brain images with brain metastases. We show that resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast acquisition loses most of the radiomic-features and invalidates predictive radiomic models, DL models restore these parameters.

Abstract

Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied. The aim of this study was to evaluate the impact of resampling and denoising DL models on radiomics. Methods: Resampling and denoising DL model was developed on 14,243 T1 brain images from 1.5T-MRI. Radiomics were extracted from 40 brain metastases from 11 patients (2049 images). A total of 104 texture features of DL images were compared to original images with paired t-test, Pearson correlation and concordance-correlation-coefficient (CCC). Results: When two times shorter image acquisition shows strong disparities with the originals concerning the radiomics, with significant differences and loss of correlation of 79.81% and 48.08%, respectively. Interestingly, DL models restore textures with 46.15% of unstable parameters and 25.96% of low CCC and without difference for the first-order intensity parameters. Conclusions: Resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast MRI acquisition loses most of the radiomic features, DL models restore these parameters.

The Impact of Artificial Intelligence CNN Based Denoising on FDG PET Radiomics

Background

With a constantly increasing number of diagnostic images performed each year, Artificial Intelligence (AI) denoising methods offer an opportunity to respond to the growing demand. However, it may affect information in the image in an unknown manner. This study quantifies the effect of AI-based denoising on FDG PET textural information in comparison to a convolution with a standard gaussian postfilter (EARL1).

Methods

The study was carried out on 113 patients who underwent a digital FDG PET/CT (VEREOS, Philips Healthcare). 101 FDG avid lesions were segmented semi-automatically by a nuclear medicine physician. VOIs in the liver and lung as reference organs were contoured. PET textural features were extracted with pyradiomics. Texture features from AI denoised and EARL1 versus original PET images were compared with a Concordance Correlation Coefficient (CCC). Features with CCC values ≥ 0.85 threshold were considered concordant. Scatter plots of variable pairs with R2 coefficients of the more relevant features were computed. A Wilcoxon signed rank test to compare the absolute values between AI denoised and original images was performed.

Results

The ratio of concordant features was 90/104 (86.5%) in AI denoised versus 46/104 (44.2%) with EARL1 denoising. In the reference organs, the concordant ratio for AI and EARL1 denoised images was low, respectively 12/104 (11.5%) and 7/104 (6.7%) in the liver, 26/104 (25%) and 24/104 (23.1%) in the lung. SUVpeak was stable after the application of both algorithms in comparison to SUVmax. Scatter plots of variable pairs showed that AI filtering affected more lower versus high intensity regions unlike EARL1 gaussian post filters, affecting both in a similar way. In lesions, the majority of texture features 79/100 (79%) were significantly (p<0.05) different between AI denoised and original PET images.

Conclusions

Applying an AI-based denoising on FDG PET images maintains most of the lesion’s texture information in contrast to EARL1-compatible Gaussian filter. Predictive features of a trained model could be thus the same, however with an adapted threshold. Artificial intelligence based denoising in PET is a very promising approach as it adapts the denoising in function of the tissue type, preserving information where it should.

Radioimmunotherapy for Brain Metastases: The Potential for Inflammation as a Target of Choice

Brain metastases (BM) are frequently detected during the follow-up of patients with malignant tumors, particularly in those with advanced disease. Despite a major progress in systemic anti-cancer treatments, the average overall survival of these patients remains limited (6 months from diagnosis). Also, cognitive decline is regularly reported especially in patients treated with whole brain external beam radiotherapy (WBRT), due to the absorbed radiation dose in healthy brain tissue. New targeted therapies, for an earlier and/or more specific treatment of the tumor and its microenvironment, are needed. Radioimmunotherapy (RIT), a combination of a radionuclide to a specific antibody, appears to be a promising tool. Inflammation, which is involved in multiple steps, including the early phase, of BM development is attractive as a relevant target for RIT. This review will focus on the (1) early biomarkers of inflammation in BM pertinent for RIT, (2) state of the art studies on RIT for BM, and (3) the importance of dosimetry to RIT in BM. These two last points will be addressed in comparison to the conventional EBRT treatment, particularly with respect to the balance between tumor control and healthy tissue complications. Finally, because new diagnostic imaging techniques show a potential for the detection of BM at an early stage of the disease, we focus particularly on this therapeutic window.

Upfront F18-choline PET/CT versus Tc99m-sestaMIBI SPECT/CT guided surgery in primary hyperparathyroidism: the randomized phase III diagnostic trial APACH2

BACKGROUND

The common endocrine disorder primary hyperparathyroidism (PHPT) can be cured by surgery. Preoperative localization of parathyroid adenoma (PTA) by imaging is a prerequisite for outpatient minimally invasive parathyroidectomy (MIP). Compared to inpatient bilateral cervical exploration (BCE) which is performed if imaging is inconclusive, MIP is superior in terms of cure and complication rates and less costly. The imaging procedure F18-choline (FCH) PET/CT outperforms Tc99m-sestaMIBI (MIBI) SPECT/CT for PTA localization, but it is much costlier. The aim of this study is to identify the most efficient first-line imaging modality for optimal patient care in PHPT without added cost to society.

METHODS

We will conduct a multicenter open diagnostic intervention randomized phase III trial comparing two diagnostic strategies in patients with PHPT: upfront FCH PET/CT versus MIBI SPECT/CT. The primary endpoint is the proportion of patients in whom the first-line imaging method results in successful MIP and cure. Follow-up including biological tests will be performed 1 and 6 months after surgery. The main secondary endpoint is the social cost of both strategies. Other secondary endpoints are as follows: FCH PET/CT and MIBI SPECT/CT diagnostic performance, performance of surgical procedure and complication rate, FCH PET/CT inter- and intra-observer variability and optimization of FCH PET/CT procedure. Fifty-eight patients will be enrolled and randomized 1:1.

DISCUSSION

FCH PET/CT is a highly efficient but expensive imaging test for preoperative PTA localization and costs three to four times more than MIBI SPECT/CT. Whether FCH PET/CT improves patient outcomes compared to the reference standard MIBI SPECT/CT is unknown. To justify its added cost, FCH PET/CT-guided parathyroid surgery should lead to improved patient management, resulting in higher cure rates and fewer BCEs and surgical complications. In the previous phase II APACH1 study, we showed that second-line FCH PET/CT led to a cure in 88% of patients with negative or inconclusive MIBI SPECT/CT. BCE could be avoided in 75% of patients and surgical complication rates were low. We therefore hypothesize that upfront FCH PET/CT would improve patient care in PHPT and that the reduction in clinical costs would offset the increase in imaging costs.

TRIAL REGISTRATION

NCT04040946 , registered August 1, 2019.  Protocol version Version 2.1 dated from 2020/04/23.

Gated 18F-FDG PET/CT of the Lung Using a Respiratory Spirometric Gating Device: A Feasibility Study

Spirometric gating devices (SGDs) can measure the respiratory signal with high temporal resolution and accuracy. The primary objective of this study was to assess the feasibility and tolerance of a gated lung PET/CT acquisition using an SGD. The secondary objective was to compare the technical quality, accuracy, and interoperability of the SGD with that of a standard respiratory gating device, Real-Time Position Management (RPM), based on measurement of vertical thoracoabdominal displacement. Methods: A prospective phase I monocentric clinical study was performed on patients undergoing ¹⁸F-FDG PET/CT for assessment of a solitary lung nodule, staging of lung malignancy, or planning of radiotherapy. After whole-body PET/CT, a centered gated acquisition of both PET and CT was simultaneously obtained with the SGD and RPM during normal breathing. Results: Of the 46 patients who were included, 6 were prematurely excluded (1 because of hyperglycemia and 5 because of distant metastases revealed by whole-body PET/CT, leading to an unjustified extra gated acquisition). No serious adverse events were observed. Of the 40 remaining patients, the gated acquisition was prematurely stopped in 1 patient because of mask discomfort (2.5%; confidence interval [CI], 0.1%-13.2%). This event was considered patient tolerance failure. The SGD generated accurately gated PET/CT images, with more than 95% of the breathing cycle detected and high temporal resolution, in 34 of the 39 patients (87.2%; 95% CI, 60.0%-100.0%) and failed to generate a biologic tumor volume in 1 of 21 patients with increased ¹⁸F-FDG uptake (4.8%; 95% CI, 0.1%-26.5%). The quality and accuracy of respiratory signal detection and synchronization were significantly better than those obtained with RPM (P < 0.05). Conclusion: This trial supports the use of an SGD for gated lung PET/CT because of its high patient tolerance and accuracy. Although this technique seems to technically outperform RPM for gated PET/CT, further assessment of its superiority and the clinical benefit is warranted. We believe that this technique could be used as a gold standard to develop innovative approaches to eliminate respiration-induced blurring artifacts.

Limits of radiomic-based entropy as a surrogate of tumor heterogeneity: ROI-area, acquisition protocol and tissue site exert substantial influence

Entropy is a promising quantitative imaging biomarker for characterizing cancer imaging phenotype. Entropy has been associated with tumor gene expression, tumor metabolism, tumor stage, patient prognosis, and treatment response. Our hypothesis states that tumor-specific biomarkers such as entropy should be correlated between synchronous metastases. Therefore, a significant proportion of the variance of entropy should be attributed to the malignant process. We analyzed 112 patients with matched/paired synchronous metastases (SM#1 and SM#2) prospectively enrolled in the MOSCATO-01 clinical trial. Imaging features were extracted from Regions Of Interest (ROI) delineated on CT-scan using TexRAD software. We showed that synchronous metastasis entropy was correlated across 5 Spatial Scale Filters: Spearman's Rho ranged between 0.41 and 0.59 (P = 0.0001, Bonferroni correction). Multivariate linear analysis revealed that entropy in SM#1 is significantly associated with (i) primary tumor type; (ii) entropy in SM#2 (same malignant process); (iii) ROI area size; (iv) metastasis site; and (v) entropy in the psoas muscle (reference tissue). Entropy was a logarithmic function of ROI area in normal control tissues (aorta, psoas) and in mathematical models (P < 0.01). We concluded that entropy is a tumor-specific metric only if confounding factors are corrected.

The retrospective binning method improves the consistency of phase binning in respiratory-gated PET/CT

This study assesses the accuracy of prospective phase-gated PET/CT data binning and presents a retrospective data binning method that improves image quality and consistency. Respiratory signals from 17 patients who underwent 4D PET/CT were analysed to evaluate the reproducibility of temporal triggers used for the standard phase-based gating method. Breathing signals were reprocessed to implement retrospective PET data binning. The mean and standard deviation of time lags between automatic triggers provided by the Real-time Position Management (RPM, Varian) gating device and inhalation peaks derived from respiratory curves were computed for each patient. The total number of respiratory cycles available for 4D PET/CT according to the binning mode (prospective versus retrospective) was compared. The maximum standardized uptake value (SUV(max)), biological tumour volume (BTV) and tumour trajectory measures were determined from the PET/CT images of five patients. Compared to retrospective binning (RB), prospective gating approach led to (i) a significant loss in breathing cycles (15%) and (ii) the inconsistency of data binning due to temporal dispersion of triggers (average 396 ms). Consequently, tumour characterization could be impacted. In retrospective mode, SUV(max) was up to 27% higher, where no significant difference appeared in BTV. In addition, prospective mode gave an inconsistent spatial location of the tumour throughout the bins. Improved consistency with breathing patterns and greater motion amplitude of the tumour centroid were observed with retrospective mode. The detection of the tumour motion and trajectory was improved also for small temporal dispersion of triggers. This study shows that the binning mode could have a significant impact on 4D PET images. The consistency of triggers with breathing signals should be checked before clinical use of gated PET/CT images, and our RB method improves 4D PET/CT image quantification.

Multiple quantum oscillations in the de Haas-van Alphen spectra of the underdoped high-temperature superconductor YBa2Cu3O6.5

By improving the experimental conditions and extensive data accumulation, we have achieved very high precision in the measurements of the de Haas-van Alphen effect in the underdoped high-temperature superconductor YBa2Cu3O6.5. We find that the main oscillation, so far believed to be single frequency, is composed of three closely spaced frequencies. We attribute this to bilayer splitting and warping of a single quasi-2D Fermi surface, indicating that c axis coherence is restored at low temperature in underdoped cuprates. Our results do not support the existence of a larger frequency of the order of 1650 T reported recently in the same compound [S. E. Sebastian, Nature (London) 454, 200 (2008)].

de Haas-van Alphen oscillations in the underdoped high-temperature superconductor YBa2Cu3O6.5

The de Haas-van Alphen effect was observed in the underdoped cuprate YBa2Cu3O6.5 via a torque technique in pulsed magnetic fields up to 59 T. Above a field of approximately 30 T the magnetization exhibits clear quantum oscillations with a single frequency of 540 T and a cyclotron mass of 1.76 times the free electron mass, in excellent agreement with previously observed Shubnikov-de Haas oscillations. The oscillations obey the standard Lifshitz-Kosevich formula of Fermi-liquid theory. This thermodynamic observation of quantum oscillations confirms the existence of a well-defined, closed, and coherent, Fermi surface in the pseudogap phase of cuprates.