The objective of this study is to test the ability of dual-energy CBCT to extract proton stopping power ratio (SPR) values for proton radiotherapy planning and to compare its performance to single-energy CBCT, while optimising the operating parameters of both single- and dual-energy CBCT for SPR extraction. We scanned three phantoms with a clinical CBCT installed in the gantry of a proton therapy facility at different source voltage and current settings. Dual-energy CBCT was emulated by combining scans from different kVps. We employed the Hünemohr-Saito method to predict SPR values for dual-energy CBCT scans, whereas we used a Hounsfield look-up table for predicting SPR values based on single-energy CT scans. The optimum operating parameters for dual-energy CBCT scans were an 80/125 kVp combination with a low/high kVp dose ratio of 75/25. This resulted in a relative mean error (RME) and a relative root mean square error (RRMSE) of (0.1 ± 1.2) % and (2.53±0.73) %, respectively. For single-energy CBCT scans, 125 kVp was determined to be the optimum voltage, yielding a RME and RRMSE value of (1.5±3.3) % and (7.1±6.9) %, respectively. Although limited in scope and in need of further experiments, this study indicates that dual-energy CBCT performs better than single-energy CBCT.

Background: Effective thermal ablation of liver tumors requires precise monitoring of the ablation zone. Computed tomography (CT) thermometry can non-invasively monitor lethal temperatures but suffers from metal artifacts caused by ablation equipment. Purpose: This study assesses spectral CT thermometry's applicability during microwave ablation, comparing the reproducibility, precision, and accuracy of attenuation-based versus physical density-based thermometry. Furthermore, it identifies optimal metal artifact reduction (MAR) methods: O-MAR, deep learning-MAR, spectral CT, and combinations thereof. Methods: Four gel phantoms embedded with temperature sensors underwent a 10- minute, 60 W microwave ablation imaged by dual-layer spectral CT scanner in 23 scans over time. For each scan attenuation-based and physical density-based temperature maps were reconstructed. Attenuation-based and physical density-based thermometry models were tested for reproducibility over three repetitions; a fourth repetition focused on accuracy. MAR techniques were applied to one repetition to evaluate temperature precision in artifact-corrupted slices. Results: The correlation between CT value and temperature was highly linear with an R-squared value exceeding 96 %. Model parameters for attenuation-based and physical density-based thermometry were −0.38 HU/°C and 0.00039 °C⁻¹, with coefficients of variation of 2.3 % and 6.7 %, respectively. Physical density maps improved temperature precision in presence of needle artifacts by 73 % compared to attenuation images. O-MAR improved temperature precision with 49 % compared to no MAR. Attenuation-based thermometry yielded narrower Bland-Altman limits-of-agreement (−7.7 °C to 5.3 °C) than physical density-based thermometry. Conclusions: Spectral physical density-based CT thermometry at 150 keV, utilized alongside O-MAR, enhances temperature precision in presence of metal artifacts and achieves reproducible temperature measurements with high accuracy.

Background: Gamma camera imaging, including single photon emission computed tomography (SPECT), is crucial for research, diagnostics, and radionuclide therapy. Gamma cameras are predominantly based on arrays of photon multipliers tubes (PMTs) that read out NaI(Tl) scintillation crystals. In this way, standard gamma cameras can localize ɣ-rays with energies typically ranging from 30 to 360 keV. In the last decade, there has been an increasing interest towards gamma imaging outside this conventional clinical energy range, for example, for theragnostic applications and preclinical multi-isotope positron emission tomography (PET) and PET-SPECT. However, standard gamma cameras are typically equipped with 9.5 mm thick NaI(Tl) crystals which can result in limited sensitivity for these higher energies. Purpose: Here we investigate to what extent thicker scintillators can improve the photopeak sensitivity for higher energy isotopes while attempting to maintain spatial resolution. Methods: Using Monte Carlo simulations, we analyzed multiple PMT-based configurations of gamma detectors with monolithic NaI (Tl) crystals of 20 and 40 mm thickness. Optimized light guide thickness together with 2-inch round, 3-inch round, 60 × 60 mm² square, and 76 × 76 mm² square PMTs were tested. For each setup, we assessed photopeak sensitivity, energy resolution, spatial, and depth-of-interaction (DoI) resolution for conventional (140 keV) and high (511 keV) energy ɣ using a maximum-likelihood algorithm. These metrics were compared to those of a “standard” 9.5 mm-thick crystal detector with 3-inch round PMTs. Results: Estimated photopeak sensitivities for 511 keV were 27% and 53% for 20 and 40 mm thick scintillators, which is respectively, 2.2 and 4.4 times higher than for 9.5 mm thickness. In most cases, energy resolution benefits from using square PMTs instead of round ones, regardless of their size. Lateral and DoI spatial resolution are best for smaller PMTs (2-inch round and 60 × 60 mm² square) which outperform the more cost-effective larger PMT setups (3-inch round and 76 × 76 mm² square), while PMT layout and shape have negligible (< 10%) effect on resolution. Best spatial resolution was obtained with 60 × 60 mm² PMTs; for 140 keV, lateral resolution was 3.5 mm irrespective of scintillator thickness, improving to 2.8 and 2.9 mm for 511 keV with 20 and 40 mm thick crystals, respectively. Using the 3-inch round PMTs, lateral resolutions of 4.5 and 3.9 mm for 140 keV and of 3.5 and 3.7 mm for 511 keV were obtained with 20 and 40 mm thick crystals respectively, indicating a moderate performance degradation compared to the 3.5 and 2.9 mm resolution obtained by the standard detector for 140 and 511 keV. Additionally, DoI resolution for 511 keV was 7.0 and 5.6 mm with 20 and 40 mm crystals using 60 × 60 mm² square PMTs, while with 3-inch round PMTs 12.1 and 5.9 mm were obtained. Conclusion: Depending on PMT size and shape, the use of thicker scintillator crystals can substantially improve detector sensitivity at high gamma energies, while spatial resolution is slightly improved or mildly degraded compared to standard crystals.

Cone-beam computed tomography (CBCT) and X-ray projection radiography are commonly used in the proton therapy workflow for the verification of patient positioning. The prospect of using the CBCT images for dose calculation purposes is attractive but currently hampered by the poorer image quality compared to the planning (fan-beam) CT. Ideally, the CBCT scan with the patient's anatomy of the day would provide sufficiently accurate proton stopping power ratios (SPR) to directly replan the treatment if needed. Dual-energy fan-beam CT has been proven to increase the accuracy of calculated SPR values compared to single-energy CT. A similar outcome may therefore be expected for dual-energy/spectral CBCT. This work aims to compare two possible realizations of dual-energy CBCT, namely a rapid kVp-switching source CBCT and a photon-counting detector (PCD) CBCT with two energy bins, with respect to their suitability for extracting SPR values. To perform this comparison, we determine the Cramér-Rao Lower Bound on the variance of the estimated electron density and effective atomic number. In our simulation study, we find that for the rapid kVp-switching setup the optimum voltage pair is 80/140 kVp, and the optimum ratio of the source current at 80 kVp to the source current at 140 kVp is 2:1 (4:1) for extracting the electron density (effective atomic number). In case of the PCD-based setup, a 140 kVp (100 kVp) spectrum and energy bins of [20; 50), [50; 150) keV appear best suited for extracting electron density (effective atomic number), outperforming the kVp-switching setup by a factor of 3.8 (4.9).

Microscopic nuclear imaging down to spatial resolutions of a few hundred microns can already be achieved using low-energy gamma emitters (e.g. ¹²⁵I, ∼30 keV) and a basic single micro-pinhole gamma camera. This has been applied to in vivo mouse thyroid imaging, for example. For clinically used radionuclides such as ^99mTc, this approach fails due to penetration of the higher-energy gamma photons through the pinhole edges. To overcome these resolution degradation effects, we propose a new imaging approach: scanning focus nuclear microscopy (SFNM). We assess SFNM using Monte Carlo simulations for clinically used isotopes. SFNM is based on the use of a 2D scanning stage with a focused multi-pinhole collimator containing 42 pinholes with narrow pinhole aperture opening angles to reduce photon penetration. All projections of different positions are used to iteratively reconstruct a three-dimensional image from which synthetic planar images are generated. SFNM imaging was tested using a digital Derenzo resolution phantom and a mouse ankle joint phantom containing ^99mTc (140 keV). The planar images were compared with those obtained using a single-pinhole collimator, either with matched pinhole diameter or with matched sensitivity. The simulation results showed an achievable ^99mTc image resolution of 0.04 mm and detailed ^99mTc bone images of a mouse ankle with SFNM. SFNM has strong advantages over single-pinhole imaging in terms of spatial resolution.

X-ray detectors with photon-counting capabilities promise to revolutionise medical imaging. For an efficient comparison of detectors of various materials and with different setup choices, reliable detector performance measures are needed. The detector point spread function (PSF) is a commonly used measure, which describes the spatial response of an X-ray detector to the irradiation of a single pixel, given the energy spectrum of the source. In the case of an energy-resolving PCD, the detector PSF is typically derived for each energy bin and characterises its resolution. Moreover, it is commonly determined under low count rate conditions, to avoid dead time and pile-up related distortions. Under these assumptions, the PSF can be determined in a straightforward manner, but does not fully characterise the detector under all conditions encountered in clinical practice. This is especially true since the number of registered counts per energy bin depends on both the incident spectrum and the fluence rate, due to pile-up and dead time. We therefore propose a new metric, the differential point spread function (dPSF), which describes the change in the output count rate due to a small change in the input spectrum, for a given combination of incident spectrum and fluence rate. The dPSF can be used to characterize the spectral and spatial performance of a PCD under high-fluence conditions, i.e. when its response becomes non-linear. We illustrate the use of the dPSF by performing a Monte-Carlo study in which we compare the response of direct-conversion and scintillationbased PCDs at different fluence rates.

Despite improvements in small animal PET instruments, many tracers cannot be imaged at sufficiently high resolutions due to positron range, while multi-tracer PET is hampered by the fact that all annihilation photons have equal energies. Here we realize multi-isotope and sub-mm resolution PET of isotopes with several mm positron range by utilizing prompt gamma photons that are commonly neglected. A PET-SPECT-CT scanner (VECTor/CT, MILabs, The Netherlands) equipped with a high-energy cluster-pinhole collimator was used to image 124I and a mix of 124I and 18F in phantoms and mice. In addition to positrons (mean range 3.4 mm) 124I emits large amounts of 603 keV prompt gammas that - aided by excellent energy discrimination of NaI - were selected to reconstruct 124I images that are unaffected by positron range. Photons detected in the 511 keV window were used to reconstruct 18F images. Images were reconstructed iteratively using an energy dependent matrix for each isotope. Correction of 18F images for contamination with 124I annihilation photons was performed by Monte Carlo based range modelling and scaling of the 124I prompt gamma image before subtracting it from the 18F image. Additionally, prompt gamma imaging was tested for 89Zr that emits very high-energy prompts (909 keV). In Derenzo resolution phantoms 0.75 mm rods were clearly discernable for 124I, 89Zr and for simultaneously acquired 124I and 18F imaging. Image quantification in phantoms with reservoirs filled with both 124I and 18F showed excellent separation of isotopes and high quantitative accuracy. Mouse imaging showed uptake of 124I in tiny thyroid parts and simultaneously injected 18F-NaF in bone structures. The ability to obtain PET images at sub-mm resolution both for isotopes with several mm positron range and for multi-isotope PET adds to many other unique capabilities of VECTor's clustered pinhole imaging, including simultaneous sub-mm PET-SPECT and theranostic high energy SPECT.

The use of multi-pinhole collimation has enabled ultra-high-resolution imaging of SPECT and PET tracers in small animals. Key for obtaining high-quality images is the use of statistical iterative image reconstruction with accurate energy-dependent photon transport modelling through collimator and detector. This can be incorporated in a system matrix that contains the probabilities that a photon emitted from a certain voxel is detected at a specific detector pixel. Here we introduce a fast Monte-Carlo based (FMC-based) matrix generation method for pinhole imaging that is easy to apply to various radionuclides. The method is based on accelerated point source simulations combined with model-based interpolation to straightforwardly change or combine photon energies of the radionuclide of interest. The proposed method was evaluated for a VECTor PET-SPECT system with (i) a HE-UHR-M collimator and (ii) an EXIRAD-3D 3D autoradiography collimator. Both experimental scans with 99mTc, 111In, and 123I, and simulated scans with 67Ga and 90Y were performed for evaluation. FMC was compared with two currently used approaches, one based on a set of point source measurements with 99mTc (dubbed traditional method), and the other based on an energy-dependent ray-tracing simulation (ray-tracing method). The reconstruction results show better image quality when using FMC-based matrices than when applying the traditional or ray-tracing matrices in various cases. FMC-based matrices generalise better than the traditional matrices when imaging radionuclides with energies deviating too much from the energy used in the calibration and are computationally more efficient for very-high-resolution imaging than the ray-tracing matrices. In addition, FMC has the advantage of easily combining energies in a single matrix which is relevant when imaging radionuclides with multiple photopeak energies (e.g. 67Ga and 111In) or with a continuous energy spectrum (e.g. 90Y). To conclude, FMC is an efficient, accurate, and versatile tool for creating system matrices for ultra-high-resolution pinhole SPECT.

SPECT imaging with 123I-FP-CIT is used for diagnosis of neurodegenerative disorders like Parkinson's disease. Attenuation correction (AC) can be useful for quantitative analysis of 123I-FP-CIT SPECT. Ideally, AC would be performed based on attenuation maps (μ-maps) derived from perfectly registered CT scans. Such μ-maps, however, are most times not available and possible errors in image registration can induce quantitative inaccuracies in AC corrected SPECT images. Earlier, we showed that a convolutional neural network (CNN) based approach allows to estimate SPECT-aligned μ-maps for full brain perfusion imaging using only emission data. Here we investigate the feasibility of similar CNN methods for axially focused 123I-FP-CIT scans. We tested our approach on a high-resolution multi-pinhole prototype clinical SPECT system in a Monte Carlo simulation study. Three CNNs that estimate μ-maps in a voxel-wise, patch-wise and image-wise manner were investigated. As the added value of AC on clinical 123I-FP-CIT scans is still debatable, the impact of AC was also reported to check in which cases CNN based AC could be beneficial. AC using the ground truth μ-maps (GT-AC) and CNN estimated μ-maps (CNN-AC) were compared with the case when no AC was done (No-AC). Results show that the effect of using GT-AC versus CNN-AC or No-AC on striatal shape and symmetry is minimal. Specific binding ratios (SBRs) from localized regions show a deviation from GT-AC ≤ 2.5% for all three CNN-ACs while No-AC systematically underestimates SBRs by 13.1%. A strong correlation (r ≥ 0.99) was obtained between GT-AC based SBRs and SBRs from CNN-ACs and No-AC. Absolute quantification (in kBq ml-1) shows a deviation from GT-AC within 2.2% for all three CNN-ACs and of 71.7% for No-AC. To conclude, all three CNNs show comparable performance in accurate μ-map estimation and 123I-FP-CIT quantification. CNN-estimated μ-map can be a promising substitute for CT-based μ-map.

In clinical brain SPECT, correction for photon attenuation in the patient is essential to obtain images which provide quantitative information on the regional activity concentration per unit volume (kBq). This correction generally requires an attenuation map (map) denoting the attenuation coefficient at each voxel which is often derived from a CT or MRI scan. However, such an additional scan is not always available and the method may suffer from registration errors. Therefore, we propose a SPECT-only-based strategy for map estimation that we apply to a stationary multi-pinhole clinical SPECT system (G-SPECT-I) for 99mTc-HMPAO brain perfusion imaging. The method is based on the use of a convolutional neural network (CNN) and was validated with Monte Carlo simulated scans. Data acquired in list mode was used to employ the energy information of both primary and scattered photons to obtain information about the tissue attenuation as much as possible. Multiple SPECT reconstructions were performed from different energy windows over a large energy range. Locally extracted 4D SPECT patches (three spatial plus one energy dimension) were used as input for the CNN which was trained to predict the attenuation coefficient of the corresponding central voxel of the patch. Results show that Attenuation Correction using the Ground Truth maps (GT-AC) or using the CNN estimated maps (CNN-AC) achieve comparable accuracy. This was confirmed by a visual assessment as well as a quantitative comparison; the mean deviation from the GT-AC when using the CNN-AC is within 1.8% for the standardized uptake values in all brain regions. Therefore, our results indicate that a CNN-based method can be an automatic and accurate tool for SPECT attenuation correction that is independent of attenuation data from other imaging modalities or human interpretations about head contours.

Brain perfusion SPECT can be used in the diagnosis of various neurologic or psychiatric disorders, e.g. stroke, epilepsy, dementia and posttraumatic stress disorder. As traditional SPECT provides limited resolution and sensitivity, we recently proposed a high resolution focusing multi-pinhole clinical SPECT scanner dubbed G-SPECT-I (Beekman et al 2015, Eur. J. Nucl. Med. Mol. Imaging 42 S209). G-SPECT-I achieves data completeness in the scan region of interest (ROI) by making small translations of the patient bed while using projections from all bed positions together for image reconstruction. A strategy to restrict the number of bed translations is desired to minimize overhead time. Previously we presented optimized bed translation paths for focused partial brain imaging, while here we focus on whole brain imaging which is the common procedure in perfusion studies. Thus, a series of noise-free scans using a reduced number of bed positions were simulated and compared to an oversampled reference scan acquired with 128 bed positions. Noisy simulations were included to validate the utility of the optimized sequences in more realistic situations. Brain uptake ratios (BURs) and left-right Asymmetry Indices (AIs) in 51 selected regions of interest (ROIs) were calculated for assessment. Results show that images were barely affected by decreasing the number of bed positions from 128 down to 18 (mean deviation from the reference of only 2.2% and 1.5% for the BUR and AI, respectively) while slightly larger deviations (2.9% and 2.7%, respectively) were obtained when using 12 positions. For both 18- and 12-position sequences these deviations due to sampling were much smaller than those induced by noise (mean deviation of 6.5% and 8.6%, respectively). Given an associated total overhead for bed movement of half a minute (18 positions) or 20 s (12 positions), G-SPECT-I can be a clinical platform that brings new protocols for fast (dynamic) whole brain SPECT and motion correction into reach.

Today, versatile emission computed tomography (VECTor) technology using dedicated high-energy collimation enables simultaneous positron emission tomography (PET) and single photon emission computed tomography (SPECT) down to 0.6 mm and 0.4 mm resolution in mice, respectively. We recently showed that for optimal resolution and quantitative accuracy of PET images the long tails of the 511 keV point spread functions (PSFs) need to be fully modelled during image reconstruction. This, however, leads to very time consuming reconstructions and thus significant acceleration in reconstruction speed is highly desirable. To this end we propose and validate a combined dual-matrix dual-voxel (DM-DV) approach: for the forward projection the slowly varying PSF tails are modelled on a three times rougher voxel grid than the central parts of the PSFs, while in the backprojection only parts of the PSF tails are included. DM-DV reconstruction is implemented in pixel-based ordered subsets expectation maximization (POSEM) and in a recently proposed accelerated pixel-based similarity-regulated ordered subsets expectation maximization (SROSEM). Both a visual assessment and a quantitative contrast-noise analysis confirm that images of a hot-rod phantom are practically identical when reconstructed with standard POSEM, DM-DV-POSEM or DM-DV-SROSEM. However, compared to POSEM, DM-DV-POSEM can reach the same contrast 5.0 times faster, while with DM-DV-SROSEM this acceleration factor increases to 11.5. Furthermore, mouse cardiac and bone images reconstructed with DM-DV-SROSEM are visually almost indistinguishable from POSEM reconstructed images but typically need an order of magnitude less reconstruction time.