Investigation of the physical effects of respiratory motion compensation in a large population of patients undergoing Tc-99m cardiac perfusion SPECT/CT stress imaging

Efficacy of a Novel Respiratory Motion Reduction Block in Reducing Motion Artifact on Myocardial Perfusion Single-Photon Emission Computed Tomography

PURPOSE

Motion artifacts caused by heart motion during myocardial perfusion single-photon emission computed tomography (SPECT) can compromise image quality and diagnostic accuracy. This study aimed to evaluate the efficacy of the novel respiratory motion reduction block (RRB) device in reducing motion artifacts by compressing the hypochondrium and improving SPECT image quality.

METHODS

In total, 91 patients who underwent myocardial perfusion SPECT with 99mTc-sestamibi were retrospectively analyzed. Patients (n = 28) who underwent SPECT without the RRB were included in the control group, and those (n = 63) who underwent SPECT with the RRB were in the RRB group. The distance of heart motion during dynamic acquisition was measured, and projection data were assessed for patient motion and motion artifacts. Patient motion was classified into various levels, and motion artifacts on SPECT images were visually examined.

RESULTS

The distances of heart motion without and with the RRB were 15.4 ± 5.3 and 7.5 ± 2.3, respectively. Compared with the control group, the RRB group had a lower frequency of heart motion based on the projection data, particularly in terms of creep and shift motion. The RRB group had a significantly lower incidence of motion artifacts on SPECT images than the control group.

CONCLUSIONS

The RRB substantially reduced specific types of motion, such as shift and creep, and had a low influence on bounce motion. However, it could effectively suppress respiratory-induced heart motion and reduce motion artifacts on myocardial perfusion SPECT, thereby emphasizing its potential for improving image quality.

Method for comparison of data driven gating algorithms in emission tomography

Objective.Multiple algorithms have been proposed for data driven gating (DDG) in single photon emission computed tomography (SPECT) and have successfully been applied to myocardial perfusion imaging (MPI). Application of DDG to acquisition types other than SPECT MPI has not been demonstrated so far, as limitations and pitfalls of current methods are unknown.Approach.We create a comprehensive set of phantoms simulating the influence of different motion artifacts, view angles, moving objects, contrast, and count levels in SPECT. We perform Monte Carlo simulation of the phantoms, allowing the characterization of DDG algorithms using quantitative metrics derived from the data and evaluate the Center of Light (COL) and Laplacian Eigenmaps methods as sample DDG algorithms.Main results.View angle, object size, count rate density, and contrast influence the accuracy of both DDG methods. Moreover, the ability to extract the respiratory motion in the phantom was shown to correlate with the contrast of the moving feature to the background, the signal to noise ratio, and the noise in the data.Significance.We showed that reporting the average correlation to an external physical reference signal per acquisition is not sufficient to characterize DDG methods. Assessing DDG methods on a view-by-view basis using the simulations and metrics from this work could enable the identification of pitfalls of current methods, and extend their application to acquisitions beyond SPECT MPI.

Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT

BACKGROUND

Low-dose (LD) myocardial perfusion (MP) SPECT suffers from high noise level, leading to compromised diagnostic accuracy. Here we investigated the denoising performance for MP-SPECT using a conditional generative adversarial network (cGAN) in projection-domain (cGAN-prj) and reconstruction-domain (cGAN-recon).

METHODS

Sixty-four noisy SPECT projections were simulated for a population of 100 XCAT phantoms with different anatomical variations and 99mTc-sestamibi distributions. Series of LD projections were obtained by scaling the full dose (FD) count rate to be 1/20 to 1/2 of the original. Twenty patients with 99mTc-sestamibi stress SPECT/CT scans were retrospectively analyzed. For each patient, LD SPECT images (7/10 to 1/10 of FD) were generated from the FD list mode data. All projections were reconstructed by the quantitative OS-EM method. A 3D cGAN was implemented to predict FD images from their corresponding LD images in the projection- and reconstruction-domain. The denoised projections were reconstructed for analysis in various quantitative indices along with cGAN-recon, Gaussian, and Butterworth-filtered images.

RESULTS

cGAN denoising improves image quality as compared to LD and conventional post-reconstruction filtering. cGAN-prj can further reduce the dose level as compared to cGAN-recon without compromising the image quality.

CONCLUSIONS

Denoising based on cGAN-prj is superior to cGAN-recon for MP-SPECT.

Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising

BACKGROUND

Myocardial perfusion (MP) SPECT is a well-established method for diagnosing cardiac disease, yet its radiation risk poses safety concern. This study aims to apply and evaluate the use of Pix2Pix generative adversarial network (Pix2Pix GAN) in denoising low dose MP SPECT images.

METHODS

One hundred male and female patients with different 99mTc-sestamibi activity distributions, organ and body sizes were simulated by a population of digital 4D Extended Cardiac Torso (XCAT) phantoms. Realistic noisy SPECT projections of full dose of 987 MBq injection and 16 min acquisition, and low dose ranged from 1/20 to 1/2 of the full dose, were generated by an analytical projector from the right anterior oblique (RAO) to the left posterior oblique (LPO) positions. Additionally, twenty patients underwent ~1,184 MBq 99mTc-sestamibi stress SPECT/CT scan were also retrospectively recruited for the study. For each patient, low dose SPECT images (7/10 to 1/10 of full dose) were generated from the full dose list mode data. Our Pix2Pix GAN model was trained with full dose and low dose reconstructed SPECT image pairs. Normalized mean square error (NMSE), structural similarity index (SSIM), coefficient of variation (CV), full-width-at-half-maximum (FWHM) and relative defect size differences (RSD) of Pix2Pix GAN processed images were evaluated along with a reference convolutional auto encoder (CAE) network and post-reconstruction filters.

RESULTS

NMSE values of 0.0233±0.004 vs. 0.0249±0.004 and 0.0313±0.007 vs. 0.0579±0.016 were obtained on 1/2 and 1/20 dose level for Pix2Pix GAN and CAE in the simulation study, while they were 0.0376±0.010 vs. 0.0433±0.010 and 0.0907±0.020 vs. 0.1186±0.025 on 7/10 and 1/10 dose level in the clinical study. Similar results were also obtained from the SSIM, CV, FWHM and RSD values. Overall, the use of Pix2Pix GAN was superior to other denoising methods in all physical indices, particular in the lower dose levels in the simulation and clinical study.

CONCLUSIONS

The Pix2Pix GAN method is effective to reduce the noise level of low dose MP SPECT. Further studies on clinical performance are warranted to demonstrate its full clinical effectiveness.

Data-driven respiratory signal estimation from temporally finely sampled projection data in conventional cardiac perfusion SPECT imaging

PURPOSE

The aim of this work was to revisit the data-driven approach of axial center-of-mass (COM) measurements to recover a surrogate respiratory signal from finely sampled (100 ms) single photon emission computed tomography (SPECT) projection data derived from list-mode acquisitions.

METHODS

For our initial evaluation, we acquired list-mode projection data from an anthropomorphic cardiac phantom mounted on a Quasar respiratory motion platform simulating 15 mm amplitude respiratory motion. We also selected 302 consecutive patients (138 males, 164 females) with list-mode acquisitions, external respiratory motion tracking, and written consent to evaluate the clinical efficacy of our data-driven approach. Linear regression, Pearson's correlation coefficient (r), and standard error of the estimates (SEE) between the respiratory signals obtained with a visual tracking system (VTS) and COM measurements were calculated for individual projection data sets and for the patient group as a whole. Both the VTS- and COM-derived respiratory signals were used to estimate and correct respiratory motion. The reconstruction for six-degree of freedom rigid-body motion estimation was done in two ways: (1) using three iterations of ordered-subsets expectation-maximization (OSEM) with four subsets (16 projection angles per subset), or 12 iterations of maximum-likelihood expectation-maximization (MLEM). Respiratory motion compensation was done employing either OSEM with 16 subsets (four projection angles per subset) and five iterations or MLEM and 80 iterations, using the two respiratory estimates, respectively. Polar map quantification was also performed, calculating the percentage count difference (%Diff) between polar maps without and with respiratory motion included. Average % Diff was calculated in 17 segments (defined according to ASNC Guidelines). Paired t-tests were used to determine significance (p-values).

RESULTS

The r-value calculated when comparing the VTS and COM respiratory signals varied widely between -0.01 and 0.96 with an average of 0.70, while the SEE varied between 0.80 and 6.48 mm with an average of 2.05 mm for our patient set, while the same values for the one anthropomorphic phantom acquisition are 0.91 and 1.11 mm, respectively. A comparison between the respiratory motion estimates for VTS and COM in the S-I direction yielded an r = 0.90 (0.94), and an SEE of 1.56 mm (1.20 mm) for OSEM (MLEM), respectively. Bland-Altman plots and calculated intraclass correlation coefficients also showed excellent agreement between the VTS and COM respiratory motion estimates. Average S-I respiratory estimates for the VTS (COM) were 9.04 (9.2 mm) and 9.01 mm (9.14 mm) for the OSEM and MLEM, respectively. The paired t-test approached significance when comparing VTS and COM estimated respiratory signals with p-values of 0.069 and 0.051 for OSEM and MLEM. The respiratory estimates from the anthropomorphic cardiac phantom experiment using the VTS (COM) were 12.62 (14.10 mm) and 12.55 mm (14.29 mm) for OSEM and MLEM, respectively. Polar map quantification yielded average % Diff consistently better when employing VTS-derived respiratory estimates to correct for respiration compared to the COM-derived estimates.

CONCLUSIONS

The results indicate that our COM method has the potential to provide an automated data-driven correction of cardiac respiratory motion without the drawbacks of our VTS methodology. However, it is not generally equivalent to the VTS method in extent of correction.

A novel deep-learning-based approach for automatic reorientation of 3D cardiac SPECT images

PURPOSE

Reconstructed transaxial cardiac SPECT images need to be reoriented into standard short-axis slices for subsequent accurate processing and analysis. We proposed a novel deep-learning-based method for fully automatic reorientation of cardiac SPECT images and evaluated its performance on data from two clinical centers.

METHODS

We used a convolutional neural network to predict the 6 rigid-body transformation parameters and a spatial transformation network was then implemented to apply these parameters on the input images for image reorientation. A novel compound loss function which balanced the parametric similarity and penalized discrepancy of the prediction and training dataset was utilized in the training stage. Data from a set of 322 patients underwent data augmentation to 6440 groups of images for the network training, and a dataset of 52 patients from the same center and 23 patients from another center were used for evaluation. Similarity of the 6 parameters was analyzed between the proposed and the manual methods. Polar maps were generated from the output images and the averaged count values of the 17 segments were computed from polar maps to evaluate the quantitative accuracy of the proposed method.

RESULTS

All the testing patients achieved automatic reorientation successfully. Linear regression results showed the 6 predicted rigid parameters and the average count value of the 17 segments having good agreement with the reference manual method. No significant difference by paired t-test was noticed between the rigid parameters of our method and the manual method (p > 0.05). Average count values of the 17 segments show a smaller difference of the proposed and manual methods than those between the existing and manual methods.

CONCLUSION

The results strongly indicate the feasibility of our method in accurate automatic cardiac SPECT reorientation. This deep-learning-based reorientation method has great promise for clinical application and warrants further investigation.

Interventional respiratory motion compensation by simultaneous fluoroscopic and nuclear imaging: a phantom study

PURPOSE

A compact and mobile hybrid c-arm scanner, capable of simultaneously acquiring nuclear and fluoroscopic projections and SPECT/CBCT, was developed to aid fluoroscopy-guided interventional procedures involving the administration of radionuclides (e.g. hepatic radioembolization). However, as in conventional SPECT/CT, the acquired nuclear images may be deteriorated by patient respiratory motion. We propose to perform compensation for respiratory motion by extracting the motion signal from fluoroscopic projections so that the nuclear counts can be gated into motion bins. The purpose of this study is to quantify the performance of this motion compensation technique with phantom experiments.

METHODS

Anthropomorphic phantom configurations that are representative of distributions obtained during the pre-treatment procedure of hepatic radioembolization were placed on a stage that translated with three different motion patterns. Fluoroscopic projections and nuclear counts were simultaneously acquired under planar and SPECT/CBCT imaging. The planar projections were visually assessed. The SPECT reconstructions were visually assessed and quantitatively assessed by calculating the activity recovery of the spherical inserts in the phantom.

RESULTS

The planar nuclear projections of the translating anthropomorphic phantom were blurry when no motion compensation was applied. With motion compensation, the nuclear projections became representative of the stationary phantom nuclear projection. Similar behavior was observed for the visual quality of SPECT reconstructions. The mean error of the activity recovery in the uncompensated SPECT reconstructions was 15.8% ± 0.9% for stable motion, 11.9% ± 0.9% for small variations, and 11.0% ± 0.9% for large variations. When applying motion compensation, the mean error decreased to 1.8% ± 1.6% for stable motion, 2.2% ± 1.5% for small variations, and 5.2% ± 2.5% for large variations.

CONCLUSION

A compact and mobile hybrid c-arm scanner, capable of simultaneously acquiring nuclear and fluoroscopic projections, can perform compensation for respiratory motion. Such motion compensation results in sharper planar nuclear projections and increases the quantitative accuracy of the SPECT reconstructions.

Effect of data conserving respiratory motion compensation on left ventricular functional parameters assessed in gated myocardial perfusion SPECT

Background

Respiratory motion compromises image quality in myocardial perfusion (MP) single-photon emission computed tomography (SPECT) imaging and may affect analysis of left ventricular (LV) functional parameters, including phase analysis-quantified mechanical dyssynchrony parameters. In this paper, we investigate the performance of two algorithms, respiratory blur modeling (RBM) and joint motion-compensated (JMC) ordered-subsets expectation maximization (OSEM), and the effects of motion compensation on cardiac-gated MP-SPECT studies.

Methods

Image acquisitions were carried out with a dual-detector SPECT/CT system in list-mode format. A cardiac phantom was imaged as stationary and under respiratory motion. The images were reconstructed with OSEM, RBM-OSEM, and JMC-OSEM algorithms, and compared in terms of mean squared error (MSE). Subsequently, MP-SPECT data of 19 patients were binned into dual-gated (respiratory and cardiac gating) projection images. The images of the patients were analyzed with Quantitative Gated SPECT (QGS) 2012 program (Cedars-Sinai Medical Center, USA). The parameters of interest were LV volumes, ejection fraction, wall motion, wall thickening, phase analysis, and perfusion parameters.

Results

In phantom experiment, compared to the stationary OSEM reconstruction, the MSE values for OSEM, RBM-OSEM, and JMC-OSEM were 8.5406·10⁻⁵,2.7190·10⁻⁵, and 2.0795·10⁻⁵, respectively. In the analysis of LV function, use of JMC had a small but statistically significant (p < 0.05) effect on several parameters: it increased LV volumes and standard deviation of phase angle histogram, and it decreased ejection fraction, global wall motion, and lateral, septal, and apical perfusion.

Conclusions

Compared to standard OSEM algorithm, RBM-OSEM and JMC-OSEM both improve image quality under motion. Motion compensation has a minor effect on LV functional parameters.

Supplementary Information

The online version contains supplementary material available at (10.1186/s40658-021-00355-w).

Clinical evaluation of three respiratory gating schemes for different respiratory patterns on cardiac SPECT

PURPOSE

Respiratory gating reduces respiratory blur in cardiac single photon emission computed tomography (SPECT). It can be implemented as three gating schemes: (a) equal amplitude-based gating (AG); (b) phase or time-based gating (TG); or (c) equal count-based gating (CG), that is, a variant of amplitude-based method. The goal of this study is to evaluate the effectiveness of these respiratory gating methods for patients with different respiratory patterns in myocardial perfusion SPECT.

METHODS

We reviewed 1274 anonymized patient respiratory traces obtained via the Vicon motion-tracking system during their ^99m Tc-sestamibi SPECT scans and grouped them into four breathing categories: (a) regular respiration (RR); (b) periodic respiration (PR); (c) respiration with apnea (AR); and (d) unclassified respiration (UR). For each respiratory pattern, 15 patients were randomly selected and their list-mode data were rebinned using the three gating schemes. A preliminary reconstruction was performed for each gate with the heart region segmented and registered to a reference gate to estimate the respiratory motion. A final reconstruction incorporating respiratory motion correction was done to get a final image set. The estimated respiratory motion, the full-width-at-half-maxima (FWHM) measured across the image intensity profile of the left ventricle wall, as well as the normalized standard deviation measured in a uniform cuboid region of the thorax were analyzed.

RESULTS

There are 47.1%, 24.3%, 13.5%, and 15.1% RR, PR, AR, and UR patients, respectively, among the 1274 patients in this study. The differences among the three gating schemes in RR were smaller than other respiratory patterns. The AG and CG methods showed statistically larger motion estimation than TG particularly in the AR and PR patterns. Noise of AG varied more in different gates, especially for AR and UR patterns.

CONCLUSION

More than half of the patients reviewed exhibited nonregular breathing patterns. Amplitude-based gating, that is, AG and CG, is a preferred gating method for such patterns and is a robust respiratory gating implementation method given the respiratory pattern of the patients is unknown before data acquisition. Phase gating is also a feasible option for regular respiratory pattern.

Evaluation of different respiratory gating schemes for cardiac SPECT

BACKGROUND

Respiratory gating reduces motion blurring in cardiac SPECT. Here we aim to evaluate the performance of three respiratory gating strategies using a population of digital phantoms with known truth and clinical data.

METHODS

We analytically simulated 60 projections for 10 XCAT phantoms with 99mTc-sestamibi distributions using three gating schemes: equal amplitude gating (AG), equal count gating (CG), and equal time gating (TG). Clinical list-mode data for 10 patients who underwent 99mTc-sestamibi scans were also processed using the 3 gating schemes. Reconstructed images in each gate were registered to a reference gate, averaged and reoriented to generate the polar plots. For simulations, image noise, relative difference (RD) of averaged count for each of the 17 segment, and relative defect size difference (RSD) were analyzed. For clinical data, image intensity profile and FWHM were measured across the left ventricle wall.

RESULTS

For simulations, AG and CG methods showed significantly lower RD and RSD compared to TG, while noise variation was more non-uniform through different gates for AG. In the clinical study, AG and CG had smaller FWHM than TG.

CONCLUSIONS

AG and CG methods show better performance for motion reduction and are recommended for clinical respiratory gating SPECT implementation.

Time-modified OSEM algorithm for more robust assessment of left ventricular dyssynchrony with phase analysis in ECG-gated myocardial perfusion SPECT

Background

In ordered subsets expectation maximization (OSEM) reconstruction of electrocardiography (ECG)-gated myocardial perfusion single-photon emission computed tomography (SPECT), it is often assumed that the image acquisition time is constant for each projection angle and ECG bin. Due to heart rate variability (HRV), this assumption may lead to errors in quantification of left ventricular mechanical dyssynchrony with phase analysis. We hypothesize that a time-modified OSEM (TOSEM) algorithm provides more robust results.

Methods

List-mode data of 44 patients were acquired with a dual-detector SPECT/CT system and binned to eight ECG bins. First, activity ratio (AR)—the ratio of total activity in the last OSEM-reconstructed ECG bin and first five ECG bins—was computed, as well as standard deviation SD_R-R of the accepted R–R intervals; their association was evaluated with Pearson correlation analysis. Subsequently, patients whose AR was higher than 90% were selected, and their list-mode data were rebinned by omitting a part of the acquired counts to yield AR values of 90%, 80%, 70%, 60% and 50%. These data sets were reconstructed with OSEM and TOSEM algorithms, and phase analysis was performed. Reliability of both algorithms was assessed by computing concordance correlation coefficients (CCCs) between the 90% data and data corresponding to lower AR values. Finally, phase analysis results assessed from OSEM- and TOSEM-reconstructed images were compared.

Results

A strong negative correlation (r = -0.749) was found between SD_R-R and AR. As AR decreased, phase analysis parameters obtained from OSEM images decreased significantly. On the contrary, reduction of AR had no significant effect on phase analysis parameters obtained from TOSEM images (CCC > 0.88). The magnitude of difference between OSEM and TOSEM results increased as AR decreased.

Conclusions

TOSEM algorithm minimizes the HRV-related error and can be used to provide more robust phase analysis results.

Improving perfusion defect detection with respiratory motion correction in cardiac SPECT at standard and reduced doses

BACKGROUND

In cardiac SPECT perfusion imaging, respiratory motion can cause non-uniform blurring in the reconstructed myocardium. We investigate the potential benefit of respiratory correction with respiratory-binned acquisitions, both at standard dose and at reduced dose, for defect detection and for left ventricular (LV) wall resolution.

METHODS

We applied two reconstruction methods for respiratory motion correction: post-reconstruction motion correction (PMC) and motion-compensated reconstruction (MCR), and compared with reconstruction without motion correction (Non-MC). We quantified the presence of perfusion defects in reconstructed images by using the total perfusion deficit (TPD) scores and conducted receiver-operating-characteristic (ROC) studies using TPD. We quantified the LV spatial resolution by using the FWHM of its cross-sectional intensity profile.

RESULTS

The values in the area-under-the-ROC-curve (AUC) achieved by MCR, PMC, and Non-MC at standard dose were 0.835, 0.830, and 0.798, respectively. Similar AUC improvements were also obtained by MCR and PMC over Non-MC at 50%, 25%, and 12.5% of full dose. Improvements in LV resolution were also observed with motion correction.

CONCLUSIONS

Respiratory-binned acquisitions can improve perfusion-defect detection accuracy over traditional reconstruction both at standard dose and at reduced dose. Motion correction may contribute to achieving further dose reduction while maintaining the diagnostic accuracy of traditional acquisitions.

Respiratory motion compensation in interventional liver SPECT using simultaneous fluoroscopic and nuclear imaging

PURPOSE

Quantitative accuracy of the single photon emission computed tomography (SPECT) reconstruction of the pretreatment procedure of liver radioembolization is crucial for dosimetry; visual quality is important for detecting doses deposited outside the planned treatment volume. Quantitative accuracy is limited by respiratory motion. Conventional gating eliminates motion by count rejection but increases noise, which degrades the visual reconstruction quality. Motion compensation using all counts can be performed if the motion signal and motion vector field over time are known. The measurement of the motion signal of a patient currently requires a device (such as a respiratory belt) attached to the patient, which complicates the acquisition. The motion vector field is generally extracted from a previously acquired four-dimensional scan and can differ from the motion in the scan performed during the intervention. The simultaneous acquisition of fluoroscopic and nuclear projections can be used to obtain both the motion vector field and the projections of the corresponding (moving) activity distribution. This eliminates the need for devices attached to the patient and provides an accurate motion vector field for SPECT reconstruction. Our approach to motion compensation would primarily be beneficial for interventional SPECT because the time-critical setting requires fast scans and no inconvenience of an external apparatus. The purpose of this work is to evaluate the performance of the motion compensation approach for interventional liver SPECT by means of simulations.

METHODS

Nuclear and fluoroscopic projections of a realistic digital human phantom with respiratory motion were generated using fast Monte Carlo simulators. Fluoroscopic projections were sampled at 1-5 Hz. Nuclear data were acquired continuously in list mode. The motion signal was extracted from the fluoroscopic projections by calculating the center-of-mass, which was then used to assign each photon to a corresponding motion bin. The fluoroscopic projections were reconstructed per bin and coregistered, resulting in a motion vector field that was used in the SPECT reconstruction. The influence of breathing patterns, fluoroscopic imaging dose, sampling rate, number of bins, and scanning time was studied. In addition, the motion compensation method was compared with conventional gating to evaluate the detectability of spheres with varying uptake ratios.

RESULTS

The liver motion signal was accurately extracted from the fluoroscopic projections, provided the motion was stable in amplitude and the sampling rate was greater than 2 Hz. The minimum total fluoroscopic dose for the proposed method to function in a 5-min scan was 10 µGy. Although conventional gating improved the quantitative reconstruction accuracy, substantial background noise was observed in the short scans because of the limited counts available. The proposed method similarly improved the quantitative accuracy, but generated reconstructions with higher visual quality. The proposed method provided better visualization of low-contrast features than when using gating.

CONCLUSION

The proposed motion compensation method has the potential to improve SPECT reconstruction quality. The method eliminates the need for external devices to measure the motion signal and generates an accurate motion vector field for reconstruction. A minimal increase in the fluoroscopic dose is required to substantially improve the results, paving the way for clinical use.

SPECT/CT: an update on technological developments and clinical applications

Functional nuclear medicine imaging with single-photon emission CT (SPECT) in combination with anatomical CT has been commercially available since the beginning of this century. The combination of the two modalities has improved both the sensitivity and specificity of many clinical applications and CT in conjunction with SPECT that allows for spatial overlay of the SPECT data on good anatomy images. Introduction of diagnostic CT units as part of the SPECT/CT system has also potentially allowed for a more cost-efficient use of the equipment. Most of the SPECT systems available are based on the well-known Anger camera principle with NaI(Tl) as a scintillation material, parallel-hole collimators and multiple photomultiplier tubes, which, from the centroid of the scintillation light, determine the position of an event. Recently, solid-state detectors using cadmium-zinc-telluride became available and clinical SPECT cameras employing multiple pinhole collimators have been developed and introduced in the market. However, even if new systems become available with better hardware, the SPECT reconstruction will still be affected by photon attenuation and scatter and collimator response. Compensation for these effects is needed even for qualitative studies to avoid artefacts leading to false positives. This review highlights the recent progress for both new SPECT cameras systems as well as for various data-processing and compensation methods.