Monte Carlo-based SPECT reconstruction within the SIMIND framework

Future trends for patient-specific dosimetry methodology in molecular radiotherapy

Molecular radiotherapy is rapidly expanding, and new radiotherapeutics are emerging. The majority of treatments is still performed using empirical fixed activities and not tailored for individual patients. Molecular radiotherapy dosimetry is often seen as a promising candidate that would allow personalisation of treatments as outcome should ultimately depend on the absorbed doses delivered and not the activities administered. The field of molecular radiotherapy dosimetry has made considerable progress towards the feasibility of routine clinical dosimetry with reasonably accurate absorbed-dose estimates for a range of molecular radiotherapy dosimetry applications. A range of challenges remain with respect to the accurate quantification, assessment of time-integrated activity and absorbed dose estimation. In this review, we summarise a range of technological and methodological advancements, mainly focussed on beta-emitting molecular radiotherapeutics, that aim to improve molecular radiotherapy dosimetry to achieve accurate, reproducible, and streamlined dosimetry. We describe how these new technologies can potentially improve the often time-consuming considered process of dosimetry and provide suggestions as to what further developments might be required.

Evaluation of reconstruction methods and image noise levels concerning visual assessment of simulated liver lesions in 111In-octreotide SPECT imaging

BACKGROUND

Early cancer detection is crucial for patients' survival. The image quality in ¹¹¹In-octreotide SPECT imaging could be improved by using Monte Carlo (MC)-based reconstruction. The aim of this observational study was to determine the detection rate of simulated liver lesions for MC-based ordered subset expectation maximization (OSEM) reconstruction compared to conventional attenuation-corrected OSEM reconstruction.

METHODS

Thirty-seven SPECT/CT examinations with ¹¹¹In-octreotide were randomly selected. The inclusion criterion was no liver lesions at the time of examination and for the following 3 years. SPECT images of spheres representing lesions were simulated using MC. The raw data of the spheres were added to the raw data of the established healthy patients in 26 of the examinations, and the remaining 11 examinations were not modified. The images were reconstructed using conventional OSEM reconstruction with attenuation correction and post filtering (fAC OSEM) and MC-based OSEM reconstruction without and with post filtering (MC OSEM and fMC OSEM, respectively). The images were visually and blindly evaluated by a nuclear medicine specialist. The criteria evaluated were liver lesion yes or no, including coordinates if yes, with confidence level 1-3. The percentage of detected lesions and accuracy (percentage of correctly classified cases), as well as tumor-to-normal tissue concentration (TNC) ratios and signal-to-noise ratios (SNRs), were evaluated.

RESULTS

The detection rates were 30.8% for fAC OSEM, 42.3% for fMC OSEM, and 50.0% for MC OSEM. The accuracies were 45.9% for fAC OSEM, 45.9% for fMC OSEM, and 54.1% for MC OSEM. The number of false positives was higher for fMC and MC OSEM. The observer's confidence level was higher in filtered images than in unfiltered images. TNC ratios were significantly higher, statistically, with MC OSEM and fMC OSEM than with AC OSEM, but SNRs were similar due to higher noise with MC OSEM.

CONCLUSION

One in two lesions were found using MC OSEM versus one in three using conventional reconstruction. TNC ratios were significantly improved, statistically, using MC-based reconstruction, but the noise levels increased and consequently the confidence level of the observer decreased. For further improvements, image noise needs to be suppressed.

Development of a scatter correction technique for planar 99mTc-MAA imaging to improve accuracy in lung shunt fraction estimation

PURPOSE

Prior to ⁹⁰Y selective internal radiation therapy (SIRT) treatment, ^99mTc-MAA scintigraphy imaging is used in the estimation of the lung shunt fraction (LSF). Planar imaging is recommended for determining a LSF ratio. However, the estimate may be affected by scatter contributions, attenuation and respiratory motion. The objective of this study was to correct for the effects of scatter in the LSF, towards the determination of a more accurate estimation method of LSF derived from planar scintigraphy imaging, which is recommended by international guidelines.

METHODS

The open access SIMIND Monte Carlo modelling software was used to estimate an optimum scatter window (SW) for scatter correction. The uncertainties associated with scatter and scatter contributions from the liver on the LSF were evaluated using an anthropomorphic thorax phantom and a virtual Vox-Man phantom. A brief retrospective examination of patient scans and tumour location investigated the impact that the inclusion of the simulated scatter corrections had on the LSF estimation.

RESULTS

The percentage overestimation of the manufacturer recommended method of LSF estimation was 192%. SW corrections improved the uncertainty to within 19% for the range of known LSFs. Similar findings were observed for our patient and tumour location studies.

CONCLUSION

The incorporated scatter corrections can significantly improve the accuracy of the LSF estimation, thereby providing a robust gamma camera, patient and tumour depth specific correction which is easily implementable. This is supported by Monte Carlo, phantom and preliminary patient studies.

Monte Carlo modelling of a compact CZT-based gamma camera with application to 177Lu imaging

Background

Semiconductor gamma-camera systems based on cadmium zinc telluride (CZT) detectors present new challenges due to an energy-response that includes effects of low-energy tailing. In particular, such energy tails produce effects that need to be considered when imaging radionuclides with multiple emissions such as $$^{177}{\mathrm {Lu}}$$ 177 Lu . Monte Carlo simulation can be used to investigate the behaviour of such systems and optimise their use, provided that the detector model closely reflects the real physical detector. The aim of this work is to develop a CZT model applicable for simulation of CZT-based gamma cameras.

Methods

The equations describing the charge transport and signal induction are considered in three dimensions and are solved numerically, and the CZT model is then realised by coupling the detector-response to the photon-transport handled by the SIMIND Monte Carlo program. The CZT model is tuned to reproduce experimentally measured energy spectra of a hand-held gamma camera system for multiple radionuclides ($$^{99\mathrm {m}}{\mathrm {Tc}}$$ 99 m Tc , $$^{123}{\mathrm {I}}$$ 123 I and $$^{177}{\mathrm {Lu}}$$ 177 Lu ) and parallel-hole collimators (MEGP, LEHR) as well as an uncollimated system.

Results

Overall, the model results agree well with measurements across the range of experimental conditions. The applicability of the model is demonstrated by separating energy spectra into components to investigate the interference of high-energy photons on lower energy-windows, where pronounced effects of low-energy tailing for $$^{177}{\mathrm {Lu}}$$ 177 Lu are observed.

Conclusions

The developed model provides understanding of the specifics of the camera response and is expected to be helpful for future optimisation of gamma camera applications.

IMPROVEMENTS OF 111IN SPECT IMAGES RECONSTRUCTED WITH SPARSELY ACQUIRED PROJECTIONS BY DEEP LEARNING GENERATED SYNTHETIC PROJECTIONS

The aim was to improve single-photon emission computed tomography (SPECT) quality for sparsely acquired 111In projections by adding deep learning generated synthetic intermediate projections (SIPs). Method: The recently constructed deep convolutional network for generating synthetic intermediate projections (CUSIP) was used for improving 20 sparsely acquired 111In-octreotide SPECTs. Reconstruction was performed with 120 (120P) or 30 (30P) projections, or 120 projections with 90 SIPs generated from 30 projections (30-120SIP). The SPECT reconstructions were performed with attenuation, scatter and collimator response corrections. Postfiltered 30P reconstructed SPECT was also analyzed. Image quality were quantitatively evaluated with root-mean-square error, peak signal-to-noise ratio and structural similarity index metrics. Result: The 30-120SIP reconstructed SPECT had statistically significant improved image quality parameters compared to 30P reconstructed SPECT with and without post filtering. The images visual appearance was similar to slightly filtered 120P SPECTs. Thereby, substantial acquisition time reduction with SIPs seems possible without image quality degradation.

Application of Monte Carlo Algorithms to Cardiac Imaging Reconstruction

Monte Carlo algorithms have a growing impact on nuclear medicine reconstruction processes. One of the main limitations of myocardial perfusion imaging (MPI) is the effective mitigation of the scattering component, which is particularly challenging in Single Photon Emission Computed Tomography (SPECT). In SPECT, no timing information can be retrieved to locate the primary source photons. Monte Carlo methods allow an event-by-event simulation of the scattering kinematics, which can be incorporated into a model of the imaging system response. This approach was adopted in the late Nineties by several authors, and recently took advantage of the increased computational power made available by high-performance CPUs and GPUs. These recent developments enable a fast image reconstruction with improved image quality, compared to deterministic approaches. Deterministic approaches are based on energy-windowing of the detector response, and on the cumulative estimate and subtraction of the scattering component. In this paper, we review the main strategies and algorithms to correct the scattering effect in SPECT and focus on Monte Carlo developments, which nowadays allow the threedimensional reconstruction of SPECT cardiac images in a few seconds.

Modeling complex particles phase space with GAN for Monte Carlo SPECT simulations: a proof of concept

A method is proposed to model by a generative adversarial network the distribution of particles exiting a patient during Monte Carlo simulation of emission tomography imaging devices. The resulting compact neural network is then able to generate particles exiting the patient, going towards the detectors, avoiding costly particle tracking within the patient. As a proof of concept, the method is evaluated for single photon emission computed tomography (SPECT) imaging and combined with another neural network modeling the detector response function (ARF-nn). A complete rotating SPECT acquisition can be simulated with reduced computation time compared to conventional Monte Carlo simulation. It also allows the user to perform simulations with several imaging systems or parameters, which is useful for imaging system design.

Scientific Developments in Imaging and Dosimetry for Molecular Radiotherapy

Molecular radiotherapy is a rapidly developing field with new vector and isotope combinations continually added to market. As with any radiotherapy treatment, it is vital that the absorbed dose and toxicity profile are adequately characterised. Methodologies for absorbed dose calculations for radiopharmaceuticals were generally developed to characterise stochastic effects and not suited to calculations on a patient-specific basis. There has been substantial scientific and technological development within the field of molecular radiotherapy dosimetry to answer this challenge. The development of imaging systems and advanced processing techniques enable the acquisition of accurate measurements of radioactivity within the body. Activity assessment combined with dosimetric models and radiation transport algorithms make individualised absorbed dose calculations not only feasible, but commonplace in a variety of commercially available software packages. The development of dosimetric parameters beyond the absorbed dose has also allowed the possibility to characterise the effect of irradiation by including biological parameters that account for radiation absorbed dose rates, gradients and spatial and temporal energy distribution heterogeneities. Molecular radiotherapy is in an exciting time of its development and the application of dosimetry in this field can only have a positive influence on its continued progression.

Deep-Learning Generation of Synthetic Intermediate Projections Improves 177Lu SPECT Images Reconstructed with Sparsely Acquired Projections

The aims of this study were to decrease the ¹⁷⁷Lu-SPECT acquisition time by reducing the number of projections and to circumvent image degradation by adding deep-learning-generated synthesized projections. Methods: We constructed a deep convolutional U-net-shaped neural network for generation of synthetic intermediate projections (CUSIPs). The number of SPECT investigations was 352 for training, 37 for validation, and 15 for testing. The input was every fourth projection of 120 acquired SPECT projections, that is, 30 projections. The output was 30 synthetic intermediate projections (SIPs) per CUSIP. SPECT images were reconstructed with 120 or 30 projections, or with 120 projections when 90 SIPs were generated from 30 projections (30-120SIPs), using 3 CUSIPs. The reconstructions were performed with 2 ordered-subset expectation maximization (OSEM) algorithms: attenuation-corrected (AC) OSEM, and attenuation, scatter, and collimator response-corrected (ASCC) OSEM. The quality of the SIPs and SPECT images was quantitatively evaluated with root-mean-square error, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) index metrics. From a Jaszczak SPECT phantom, the recovery and signal-to-noise ratio (SNR) were determined. In addition, an experienced observer qualitatively assessed the SPECT image quality of the test set. Kidney activity concentrations, as determined from the different SPECT images, were compared. Results: The generated SIPs had a mean SSIM value of 0.926 (SD, 0.061). For AC-OSEM, the reconstruction with 30-120SIPs had higher SSIM (0.993 vs. 0.989, P < 0.001) and PSNR (49.5 vs. 47.2, P < 0.001) values than the reconstruction with 30 projections. ASCC-OSEM had higher SSIM and PSNR values than AC-OSEM (P < 0.001). There was a minor loss in recovery for 30-120SIPs, but SNR was clearly improved compared with 30 projections. The observer assessed 27 of 30 images reconstructed with 30 projections as having unacceptable noise levels, whereas the corresponding values were 2 of 60 for 30-120SIPs and 120 projections. Image quality did not differ significantly between 30-120SIPs and 120 projections. The kidney activity concentration was similar between the different projection sets, excepting a minor reduction of 2.5% for ASCC-OSEM 30-120SIPs. Conclusion: Adopting SIPs for sparsely acquired projections considerably recovers image quality and could allow a reduced SPECT acquisition time in clinical dosimetry protocols.

Establishment of a clinical SPECT/CT protocol for imaging of 161Tb

Background

It has been proposed, and preclinically demonstrated, that ¹⁶¹Tb is a better alternative to ¹⁷⁷Lu for the treatment of small prostate cancer lesions due to its high emission of low-energy electrons. ¹⁶¹Tb also emits photons suitable for single-photon emission computed tomography (SPECT) imaging. This study aims to establish a SPECT protocol for ¹⁶¹Tb imaging in the clinic.

Materials and methods

Optimal settings using various γ-camera collimators and energy windows were explored by imaging a Jaszczak phantom, including hollow-sphere inserts, filled with ¹⁶¹Tb. The collimators examined were extended low-energy general purpose (ELEGP), medium-energy general purpose (MEGP), and low-energy high resolution (LEHR), respectively. In addition, three ordered subset expectation maximization (OSEM) algorithms were investigated: attenuation-corrected OSEM (A-OSEM); attenuation and dual- or triple-energy window scatter-corrected OSEM (AS-OSEM); and attenuation, scatter, and collimator-detector response-corrected OSEM (ASC-OSEM), where the latter utilized Monte Carlo-based reconstruction. Uniformity corrections, using intrinsic and extrinsic correction maps, were also investigated. Image quality was assessed by estimated recovery coefficients (RC), noise, and signal-to-noise ratio (SNR). Sensitivity was determined using a circular flat phantom.

Results

The best RC and SNR were obtained at an energy window between 67.1 and 82.1 keV. Ring artifacts, caused by non-uniformity, were removed with extrinsic uniformity correction for the energy window between 67.1 and 82.1 keV, but not with intrinsic correction. Analyzing the lower energy window between 48.9 and 62.9 keV, the ring artifacts remained after uniformity corrections. The recovery was similar for the different collimators when using a specific OSEM reconstruction. Recovery and SNR were highest for ASC-OSEM, followed by AS-OSEM and A-OSEM. When using the optimized parameter setting, the resolution of ¹⁶¹Tb was higher than for ¹⁷⁷Lu (8.4 ± 0.7 vs. 10.4 ± 0.6 mm, respectively). The sensitivities for ¹⁶¹Tb and ¹⁷⁷Lu were 7.41 and 8.46 cps/MBq, respectively.

Conclusion

SPECT with high resolution is feasible with ¹⁶¹Tb; however, extrinsic uniformity correction is recommended to avoid ring artifacts. The LEHR collimator was the best choice of the three tested to obtain a high-resolution image. Due to the complex emission spectrum of low-energy photons, window-based scatter correction had a minor impact on the image quality compared to using attenuation correction only. On the other hand, performing attenuation, scatter, and collimator-detector correction clearly improved image quality. Based on these data, SPECT-based dosimetry for ¹⁶¹Tb-labeled radiopharmaceuticals is feasible.

Feasibility and limitations of quantitative SPECT for 223Ra

The aim of this paper is to investigate the feasibility and limitations of activity-concentration estimation for ²²³Ra using SPECT. Phantom measurements are performed using spheres (volumes 5.5 mL to 26.4 mL, concentrations 1.6 kBq mL^-1 to 4.5 kBq mL^-1). Furthermore, SPECT projections are simulated using the SIMIND Monte Carlo program for two geometries, one similar to the physical phantom and the other being an anthropomorphic phantom with added lesions (volumes 34 mL to 100 mL, concentrations 0.5 kBq mL^-1 to 4 kBq mL^-1). Medium-energy and high-energy collimators, 60 projections with 55 s per projection and a 20% energy window at 82 keV are employed. For the Monte Carlo simulated images, Poisson-distributed noise is added in ten noise realizations. Reconstruction is performed (OS-EM, 40 iterations, 6 subsets) employing compensation for attenuation, scatter, and collimator-detector response. The estimated concentrations in the anthropomorphic phantom are also corrected using recovery coefficients. Errors for the largest sphere in the physical phantom range from -25% to -34% for the medium-energy collimator and larger deviations for smaller spheres. Corresponding results for the high-energy collimator are -15% to -31%. The corresponding Monte Carlo simulations show standard deviations of a few percentage points. For the anthropomorphic phantom, before application of recovery coefficients the bias ranges from -16% to -46% (medium-energy collimator) and -10% to -28% (high-energy collimator), with standard deviations of 2% to 14% and 1% to 16%. After the application of recovery coefficients, the biases range from -3% to -35% (medium energy collimator) and from 0% to -18%. The errors decrease with increasing concentrations. Activity-concentration estimation of ²²³Ra with SPECT is feasible, but problems with repeatability need to be further studied.

Accelerated SPECT image reconstruction with FBP and an image enhancement convolutional neural network

BACKGROUND

Monte Carlo-based iterative reconstruction to correct for photon scatter and collimator effects has been proven to be superior over analytical correction schemes in single-photon emission computed tomography (SPECT/CT), but it is currently not commonly used in daily clinical practice due to the long associated reconstruction times. We propose to use a convolutional neural network (CNN) to upgrade fast filtered back projection (FBP) image quality so that reconstructions comparable in quality to the Monte Carlo-based reconstruction can be obtained within seconds.

RESULTS

A total of 128 technetium-99m macroaggregated albumin pre-treatment SPECT/CT scans used to guide hepatic radioembolization were available. Four reconstruction methods were compared: FBP, clinical reconstruction, Monte Carlo-based reconstruction, and the neural network approach. The CNN generated reconstructions in 5 sec, whereas clinical reconstruction took 5 min and the Monte Carlo-based reconstruction took 19 min. The mean squared error of the neural network approach in the validation set was between that of the Monte Carlo-based and clinical reconstruction, and the lung shunting fraction difference was lower than 2 percent point. A phantom experiment showed that quantitative measures required in radioembolization were accurately retrieved from the CNN-generated reconstructions.

CONCLUSIONS

FBP with an image enhancement neural network provides SPECT reconstructions with quality close to that obtained with Monte Carlo-based reconstruction within seconds.