Ring Artifacts Research Articles

A unified scatter rejection and correction method for cone beam computed tomography.

Scattered radiation is a major cause of image quality degradation in flat panel detector-based cone beam CT (CBCT). While recently introduced 2D antiscatter grids reject the majority of scatter fluence, the small percentage of scatter fluence still transmitted to the detector remains a major challenge for implementation of quantitative imaging techniques such as dual energy imaging in CBCT. Additionally, this residual scatter is also a major source of grid-induced artifacts, which impedes implementation of 2D grids in CBCT. We therefore present a new method to achieve both robust scatter rejection and residual scatter correction using a 2D antiscatter grid; in doing so, we expand the role of 2D grids from mere scatter rejection devices to scatter measurement devices. In our method, the radiopaque septa of the 2D grid emulate a micro array of beam-stops placed on the detector which introduce spatially periodic septal shadows. By selecting sufficiently thin grid septa, the primary intensity can be reduced while preserving the uniformity of scatter intensity. This enables us to correlate the modulated pixel signal intensity in septal shadows with local scatter intensity. Our method then exploits this correlation to measure and remove residual scatter intensity from projections. No assumptions are made about the object being imaged. We refer to this as Grid-based Scatter Sampling (GSS). In this work, we evaluate the principle of signal modulation with grid septa, the accuracy of scatter estimates, and the effect of the GSS method on image quality using simulations and measurements. We also implement the GSS method experimentally using a 2D grid prototype. Our results demonstrate that the GSS method increased CT number accuracy and reduced image artifacts associated with scatter. With 2D grid and residual scatter correction, HU nonuniformity was reduced from 65HU to 30HU in pelvis sized phantoms, and HU variations due to change in phantom size were reduced from 59HU to 20HU, when compared to use of only a 2D grid. With residual scatter correction via GSS method, grid-induced ring artifacts were suppressed, leading to a 41% reduction in noise. The shape of the modulation transfer function (MTF) was preserved before and after suppression of ring artifacts. Our grid-based scatter sampling method enables utilization of a 2D grid as a scatter measurement and correction device. This method significantly improves quantitative accuracy in CBCT, further reducing the image quality gap between CBCT and multi-detector CT. By correcting residual scatter with the proposed method, grid-induced line artifacts in projections and associated ring artifacts in CBCT images were also suppressed with no compromise of spatial resolution.

Empirical beam hardening and ring artifact correction for x-ray grating interferometry (EBHC-GI).

Talbot-Lau grating interferometry enables the use of polychromatic x-ray sources, extending the range of potential applications amenable to phase contrast imaging. However, these sources introduce beam hardening effects not only from the samples but also from the gratings. As a result, grating inhomogeneities due to manufacturing imperfections can cause spectral nonuniformity artifacts when used with polychromatic sources. Consequently, the different energy dependencies of absorption, phase, and visibility contrasts impose challenges that so far have limited the achievable image quality. The purpose of this work was to develop and validate a correction strategy for grating-based x-ray imaging that accounts for beam hardening generated from both the imaged object and the gratings. The proposed two-variable polynomial expansion strategy was inspired by work performed to address beam hardening from a primary modulator. To account for the multicontrast nature of grating interferometry, this approach was extended to each contrast to obtain three sets of correction coefficients, which were determined empirically from a calibration scan. The method's feasibility was demonstrated using a tabletop Talbot-Lau grating interferometer micro-computed tomography (CT) system using CT acquisitions of a water sample and a silicon sample, representing low and high atomic number materials. Spectral artifacts such as cupping and ring artifacts were quantified using mean squared error (MSE) from the beam-hardening-free target image and standard deviation within a reconstructed image of the sample. Finally, the model developed using the water sample was applied to a fixated murine lung sample to demonstrate robustness for similar materials. The water sample's absorption CT image was most impacted by spectral artifacts, but following correction to decrease ring artifacts, an 80% reduction in MSE and 57% reduction in standard deviation was observed. The silicon sample created severe artifacts in all contrasts, but following correction, MSE was reduced by 94% in absorption, 96% in phase, and 90% in visibility images. These improvements were due to the removal of ring artifacts for all contrasts and reduced cupping in absorption and phase images and reduced capping in visibility images. When the water calibration coefficients were applied to the lung sample, ring artifacts most prominent in the absorption contrast were eliminated. The described method, which was developed to remove artifacts in absorption, phase, and normalized visibility micro-CT images due to beam hardening in the system gratings and imaged object, reduced the MSE by up to 96%. The method depends on calibrations that can be performed on any system and does not require detailed knowledge of the x-ray spectrum, detector energy response, grating attenuation properties and imperfections, or the geometry and composition of the imaged object.

Complete Ring Artifacts Reduction Procedure for Lab-Based X-ray Nano CT Systems.

In this article, we introduce a new ring artifacts reduction procedure that combines several ideas from existing methods into one complex and robust approach with a goal to overcome their individual weaknesses and limitations. The procedure differentiates two types of ring artifacts according to their cause and character in computed tomography (CT) data. Each type is then addressed separately in the sinogram domain. The novel iterative schemes based on relative total variations (RTV) were integrated to detect the artifacts. The correction process uses the image inpainting, and the intensity deviations smoothing method. The procedure was implemented in scope of lab-based X-ray nano CT with detection systems based on charge-coupled device (CCD) and scientific complementary metal-oxide-semiconductor (sCMOS) technologies. The procedure was then further tested and optimized on the simulated data and the real CT data of selected samples with different compositions. The performance of the procedure was quantitatively evaluated in terms of the artifacts’ detection accuracy, the comparison with existing methods, and the ability to preserve spatial resolution. The results show a high efficiency of ring removal and the preservation of the original sample’s structure.

Augmented Noise Learning Framework for Enhancing Medical Image Denoising

Deep learning attempts medical image denoising either by directly learning the noise present or via first learning the image content. We observe that residual learning (RL) often suffers from signal leakage while dictionary learning (DL) is prone to Gibbs (ringing) artifacts. In this paper, we propose an unsupervised noise learning framework that enhances denoising by augmenting the limitation of RL with the strength of DL and vice versa. To this end, we propose a ten-layer deep residue network (DRN) augmented with patch-based dictionaries. The input images are presented to patch-based DL to indirectly learn the noise via sparse representation while given to the DRN to directly learn the noise. An optimum noise characterization is captured by iterating DL/DRN network against proposed loss. The denoised images are obtained by subtracting the learned noise from available data. We show that augmented DRN effectively handles high-frequency regions to avoid Gibbs artifacts due to DL while augmented DL helps to reduce the overfitting due to RL. Comparative experiments with many state-of-the-arts on MRI and CT datasets (2D/3D) including low-dose CT (LDCT) are conducted on a GPU-based supercomputer. The proposed network is trained by adding different levels of Rician noise for MRI and Poisson noise for CT images considering different nature and statistical distribution of datasets. The ablation studies are carried out that demonstrate enhanced denoising performance with minimal signal leakage and least artifacts by proposed augmented approach.

Proof of concept image artifact reduction by energy-modulated proton computed tomography (EMpCT).

To reduce imaging artifacts and improve image quality of a specific proton computed tomography (pCT) prototype scanner by combining pCT data acquired at two different incident proton energies to avoid protons stopping in sub-optimal detector sections. Image artifacts of a prototype pCT scanner are linked to protons stopping close to internal structures of the scanner's multi-stage energy detector. We aimed at avoiding such protons by acquiring pCT data at two different incident energies and combining the data in post-processing from which artifact-reduced images of the relative stopping power (RSP) were calculated. Energy-modulated pCT (EMpCT) images were assessed visually and quantitatively and compared to the original mono-energetic images in terms of RSP accuracy and noise. Data were acquired for a homogeneous water phantom. RSP images reconstructed from the mono-energetic datasets displayed local image artifacts which were ring-shaped due to the homogeneity of the phantom. The merged EMpCT dataset achieved a superior visual image quality with reduced artifacts and only minor remaining rings. The inter-quartile range (25/75) of RSP values was reduced from 0.7% with the current standard acquisition to 0.2% with EMpCT due to the reduction of ring artifacts. In this study, dose was doubled compared to a standard scan, but we discuss strategies to reduce excess dose. EMpCT allows to effectively avoid regions of the energy detector that cause image artifacts. Thereby, image quality is improved.

Artifacts and quantitative biases in neutron tomography introduced by systematic and random errors

Systematic and random errors are often introduced in white-beam neutron Computed Tomography (CT) due to the nature of the neutron source, neutron-matter interactions or limitations in hardware. These errors can cause bias in measured linear attenuation coefficients (LACs) and artifacts in the reconstructed images, reducing image quality and impeding quantitative analysis. Three sources of error in neutron tomography were investigated and quantified that are particularly pertinent to spallation source facilities. These include detector to sample stage misalignment, beam fluctuations and undersampling due to missing CT projections. Quantitative analyses of the grayscale biases and misalignment artifacts were performed on the reconstructed data using ImageJ. Furthermore, the efficacy of classical iterative reconstruction algorithms for the suppression of missing projection artifacts was explored. Several image qualifying metrics were used to compare Filtered Back Projection (FBP) and iterative algorithms quantitatively. We show that beam intensity fluctuations if left uncorrected provide a minimal bias even when exaggerated across a stack of projections. In addition, detector-sample stage misalignment causes a geometrical misalignment blur when not corrected for and severe ring artifacts despite image rotation correction. We suggest that iterative algorithms are more favourable in suppressing missing projection neutron artifacts, however they inaccurately reproduce the LACs for missing-wedge reconstructions compared to FBP.

Evaluation and comparison of performance of low-dose 128-slice CT scanner with different mAs values: A phantom study.

OBJECTIVE:Radiation dose in computed tomography (CT) has been the concern of physicists ever since the introduction of CT scan. The objective of this study was to evaluate the performance of low-dose 128-slice CT scanner with different mAs values.MATERIALS AND METHODS:Quantitative study was carried out at different values of mAs. Philips brilliance CT phantom with Philips ingenuity 128-slice low-dose CT scanner was chosen for this study. CT number linearity, CT number accuracy, slice thickness accuracy, high-contrast resolution, and low-contrast resolution were calculated and estimated computed tomography dose index volume (CTDIvol) for all the mAs values were recorded. Noise was calculated for all mAs values for comparison.RESULTS:Data analysis shows that image quality was acceptable for all protocols. High-contrast resolution for all protocols was 20 line pairs per centimeter. Low-contrast resolution for 50 mAs images was 4 mm and 3 mm for other mAs protocols. Images acquired using 100 mAs revealed ring artifacts. CTDIvol using 50 mAs was 33% of the CTDIvol using 150 mAs. The dose–length product at 100 mAs was reduced to 66% of the dose–length product at 150 mAs, and the same at 50 mAs was reduced to 33%.CONCLUSION:It is evident here that mAs has direct impact on the radiation dose to patient. With iDose4, mAs can be reduced to 50 mAs in multislice low-dose CT scan to reduce the radiation dose with minimal effect on image quality for slice thickness 4 mm. However, noise would dominate at tube current lower than 50 mAs for 120 kVp.

Ring artifacts correction based on the projection-field in neutron CT* *Project supported by the National Key Research and Development Program of China (Grant Nos. 2016YFA0401502 and 2017YFB0701903), the National Natural Science Foundation of China (Grant Nos. U1832219 and 12005166), the Youth Innovation Promotion Association, the Chinese Academy of Sciences (CAS) (Grant No. 2017023), the Guangdong Natural Science Foundation, China (Grant

Ring artifacts will happen mostly when the detector has inconsistent response among the detector channels, and the characteristic produced rings centered in the iso-center in the reconstructed slices inevitably affect the recognition and analysis of the corresponding sample structures in neutron computed tomography (CT). In this work, a ring correction method based on the projection-field (RCP) is proposed, it is a pre-processing method and provides the corrected projection data directly, which is also conducive to efficient data storage and other algorithmic researches. Simulation and physical experiments are performed for verifying the effect of the method, and one of the correction methods based on the image-field is used for comparison. The results demonstrate that the RCP can correct the ring artifacts well without reducing the image resolution or over-correction.

Non‐iterative blind deconvolution algorithm based on power‐law distribution

The spectral amplitude of most natural images is approximately isotropic and follows the power law. In this study, the authors propose a new non-iterative blind image deconvolution algorithm that builds an isosceles curve model to approximate the spectrum amplitude of the real image. In the authors’ proposed algorithm, the optical transfer function (OTF) is obtained by comparing the reconstructed and degraded spectra. Then they employ the integrated multidirectional comprehensive estimation to reduce the OTF estimation error. The restored image is then obtained by applying the estimated OTF and the Wiener filter. Experiments on image deconvolution tasks indicate that the proposed algorithm provides a significant performance gain by obtaining an accurate OTF, reducing ringing artefacts compared with existing algorithms, and realising real-time image restoration.

Make Full Use of Priors

Multi-view video plus depth (MVD) is the promising and widely adopted data representation for future 3D visual applications and interactive media. However, compression distortions on depth videos impede the development of such applications, and filters are crucially needed for the quality enhancement at the terminal side. Cross-view priors can intuitively be involved in filter design, but these priors are also distorted in compression and thus the contribution of them can hardly be considered in previous research. In this article, we propose a cross-view optimized filter for depth map quality enhancement by making full use of inner- and cross-view priors. We dedicate to evaluate the contributions of distorted cross-view priors in filtering the current view of depth, and then both inner- and cross-view priors can be involved in the filter design. Thus, distortions of cross-view priors are not barriers again as before. For the purpose of that, mutual information guided cross-view consistency is designed to evaluate the contributions of cross-view priors from compression distortions of MVD. After that, under the framework of global optimization, both inner- and cross-view priors are modeled and taken to minimize the designed energy function where both data accuracy and spatial smoothness are modeled. The experimental results show that the proposed model outperforms state-of-the-art methods, where 3.289 dB and 0.0407 average gains on peak signal-to-noise ratio and structural similarity metrics can be obtained, respectively. For the subjective evaluations, object details and structure information are recovered in the compressed depth video. We also verify our method via several practical applications, including virtual view synthesis for smooth interaction and point cloud for 3D modeling for accuracy evaluation. In these verifications, the ringing and malposition artifacts on object contours are properly handled for interactive video, and discontinuous object surfaces are restored for 3D modeling. All of these results suggest that compression distortions in MVD can be properly filtered by the proposed model, which provides a promising solution for future bandwidth constrained 3D and interactive visual applications.

A Modified Phase Cycling Method for Complex-Valued MRI Reconstruction.

The phase cycling method is a state-of-the-art method to reconstruct complex-valued MR image. However, when it follows practical two-dimensional (2D) subsampling Cartesian acquisition which is only enforcing random sampling in the phase-encoding direction, a number of artifacts in magnitude appear. A modified approach is proposed to remove these artifacts under practical MRI subsampling, by adding one-dimensional total variation (TV) regularization into the phase cycling method to “pre-process” the magnitude component before its update. Furthermore, an operation used in SFISTA is employed to update the magnitude and phase images for better solutions. The results of the experiments show the ability of the proposed method to eliminate the ring artifacts and improve the magnitude reconstruction.

Retinal image enhancement using adaptive histogram equalization tuned with nonsimilar grouping curvelet

AbstractFundus images are broadly used by medical ophthalmologists to detect and assess any customary abnormalities. Fundus imaging sensors capture the eye's rigid portion, which characteristically covers the core, tangential retina, optic disc, and macula. Existing state‐of‐the‐art fundus sensors have the drawback of producing low contrast and noisy information, which makes scientific and algorithmic evaluation very complicated. This article proposes an Adaptive Histogram Equalization—Tuned with Nonsimilar Grouping Curvelet (HET‐NOSCU), which works through a joint denoising enhancement approach. The proposed algorithm's main contribution includes (i) use of curvelet features to better preserve edges during denoising. (ii) Adaptive enhancement using the histogram to prevent halo ringing and specular artifacts, which yields superior results than the very recently established state‐of ‐the‐art methods, using similar performing parameters such as peak signal to noise ratio (PSNR), structural similarity index (SSIM), and correlation coefficient (CoC). We observe an improvement of 17.66%, 0.93%, and 0.24%, respectively, for the above parameters.

X-Ray Transmittance Modeling-Based Material Decomposition Using a Photon-Counting Detector CT System

Recently developed x-ray transmittance modeling-based three-step algorithm compensates for the spectral distortion in the photon-counting detector (PCD) and estimates the line-integrals of the basis materials. The x-ray transmittance modeling linearizes the nonlinear forward imaging model and derives a computationally efficient three-step algorithm that achieves almost unbiased and minimum variance estimator. In this article, we apply the algorithm to the experimental data from a research whole-body PCD-computed tomography (CT) system. We perform pixel-by-pixel calibration using water-equivalent phantoms to fit the output of the scanner to the forward model and then feed the calibrated data into the three-step algorithm. Experimental data from iodine phantom and swine abdomen scans demonstrate that the proposed algorithm compensates for the spectral distortion effectively and estimates the line-integrals of two basis materials, water and iodine, efficiently. The proposed method substantially reduces the beam hardening and ring artifacts present in the test phantom compared to those of the image-based material decomposition method and the images directly reconstructed from the raw data of the system. The algorithm also exhibits less bias and comparable noise to those of the other methods for various x-ray energies.

Post-processing method for the removal of mixed ring artifacts in CT images.

Ring artifacts seriously deteriorate the quality of CT images. Intensity-dependence of detector responses will result in intensity-dependent ring artifacts and time-dependence of CT hardware systems will result in time-dependent ring artifacts. However, only the intensity-dependent ring artifacts are taken into consideration in most post-processing methods. Therefore, the purpose of this study is to propose a general post-processing method, which has a significant removal effect on the intensity-dependent ring artifacts and the time-dependent ring artifacts. First in the proposed method, transform raw CT images into polar coordinate images, and the ring artifacts will manifest as stripe artifacts. Secondly, obtain structure images by smoothing the polar coordinate images and acquire texture images containing some details and stripe artifacts by subtracting the structure images from the polar coordinate images. Third, extract the stripe artifacts from the texture images using mean extraction and texture classification, and obtain the extracted ring artifacts by transforming the extracted stripe artifacts from polar coordinates into Cartesian coordinates. Finally, obtain corrected CT images by subtracting the extracted ring artifacts from the raw CT images, and iterate the corrected CT images in above steps until the ring artifacts extracted in the last iteration are weak enough. Simulation and real data show that the proposed method can remove the intensity-dependent ring artifacts and the time-dependent ring artifacts effectively while preserving image details and spatial resolution. In particular, real data prove that the method is suitable for new CT systems such as the photon counting CT.

Ring-Artifact Correction With Total-Variation Regularization for Material Images in Photon-Counting CT

We propose a ring-artifact correction method with a compressed sensing for material images obtained with a photon-counting computed tomography (CT) system. The ring-artifacts are caused by nonuniformity of detector properties. Conventional ring-artifact correction methods tend to degrade the quality of images. In contrast, compressed sensing methods can correct ring-artifacts with less degradation of the image quality owing to a priori knowledge that ring-artifacts appeared as stripes in sinograms. In this study, we extend the compressed sensing methods for material sinograms obtained with a photon-counting CT system. This is because material sinograms tend to be simpler and sparser, for which a compressed-sensing method can be more effective. We introduced a cost function with a total variation-regularization term and positivity constraint, and optimized it with a prime-dual splitting method. The feasibility of this method was confirmed by simulations and an experiment. In both the simulations and experiment, the proposed method better corrected the ring artifacts than those on attenuation domain and without a priori knowledge. The comparison with previous methods in literature also showed the same results. These results suggest that our method is effective for correcting ring-artifacts in material images.

Iterative Ring Artifact Removal Method for Helical Computed Tomography Scans.

In this article, a statistical-based iterative ring removal (IRR) algorithm that effectively removes ring artifacts generated by defective detector cells is proposed. The physical state of computed tomography (CT) detector elements can change dynamically owing to their temperature dependence and the varying irradiation caused by focal spot movements. This variation in the properties of cells may cause false pixel values in sinograms, resulting in rings or segments of rings in reconstructed images. In this article, the proposed algorithm is studied on clinical CT. Two patients were scanned using a clinical CT scanner (AnyScan SPECT/CT, Mediso). Artificial rings and band rings were generated on the real sinogram data to examine the algorithm in different cases. The method was performed also on real ring artifacts. The IRR can correct both single and band-like ring artifacts with one or more defective pixels. The proposed algorithm can detect the period when pixels contain false signals and only those periods are corrected. The IRR reduces ring artifacts, even in cases where low-contrast rings occur in the reconstructed image. This statistical correction method efficiently detects and corrects false pixel values in the projection data without causing new artifacts in the reconstructed image. The algorithm is less sensitive to its parameters.

Effects of TOF Resolution Models on Edge Artifacts in PET Reconstruction from Limited-Angle Data.

Limited-angle data, such as data obtained from a dual-panel Breast-PET scanner, result in substantial image blur in directions coinciding with the missing cone of the image spectrum. On systems with time-of-flight (TOF) capabilities, this blur is reduced as given by the TOF uncertainty, with the image spectrum being correspondingly expanded into the missing spectral cone. Modeling of the TOF uncertainty in the reconstruction is expected to deconvolve this residual TOF blurring. We have however observed that, as a tradeoff, this TOF de-blurring process also introduces ringing artifacts at the edges, analogous to the edge effects observed with line-of-response (LOR) resolution modeling, which attempts to deconvolve the blur due to detector resolution effects. However, in the former case, the ringing artifacts are much wider due to the spatial extent of the TOF uncertainty as compared to the width of typical LOR resolution blur. We illustrate and investigate the effects of using matched, as well as under-modeled and over-modeled, TOF kernels on edge artifacts in reconstruction from limited-angle data, and compare them with TOF reconstructions of complete data. Although for the conventional data with full angular coverage the reconstruction is fairly insensitive to the exact size of the TOF kernel and TOF modeling does not produce ringing artifacts, it is not the case for the limited-angle data. We show that it is important to use some form of regularization of the TOF uncertainty deconvolution process within reconstruction of the limited-angle data, such as decreasing the TOF kernel size.

Multi-energy CT imaging for large patients using dual-source photon-counting detector CT

Multi-energy CT imaging of large patients with conventional dual-energy (DE)-CT using an energy-integrating-detector (EID) is challenging due to photon starvation-induced image artifacts, especially in lower tube potential (80–100 kV) images. Here, we performed phantom experiments to investigate the performance of DECT for morbidly obese patients, using an iodine and water material decomposition task as an example, on an emulated dual-source (DS)-photon-counting-detector (PCD)-CT, and compared its performance with a clinical DS-EID-CT. An abdominal CT phantom with iodine inserts of different concentrations was wrapped with tissue-equivalent gel layers to emulate a large patient (50 cm lateral size). The phantom was scanned on a research whole-body single-source (SS)-PCD-CT (140 kV tube potential), a DS-PCD-CT (100/Sn140 kV; Sn140 indicates 140 kV with Sn filter), and a clinical DS-EID-CT (100/Sn140 kV) with the same radiation dose. Phantom scans were repeated five times on each system. The DS-PCD-CT acquisition was emulated by scanning twice on the SS-PCD-CT using different tube potentials. The multi-energy CT images acquired on each system were then reconstructed, and iodine- and water-specific images were generated using material decomposition. The root-mean-square-error (RMSE) between true and measured iodine concentrations were calculated for each system and compared. The images acquired on the DS-EID-CT showed severe artifacts, including ringing, reduced uniformity, and photon starvation artifacts, especially for low-energy images. These were largely reduced in DS-PCD-CT images. The CT number difference that was measured using regions-of-interest across field-of-view were reduced from 20.3 ± 0.9 (DS-EID-CT) to 2.5 ± 0.4 HU on DS-PCD-CT, showing improved image uniformity using DS-PCD-CT. Iodine RMSE was reduced from 3.42 ± 0.03 mg ml−1 (SS-PCD-CT) and 2.90 ± 0.03 mg ml−1 (DS-EID-CT) to 2.39 ± 0.05 mg ml−1 using DS-PCD-CT. DS-PCD-CT out-performed a clinical DS-EID-CT for iodine and water-based material decomposition on phantom emulating obese patients by reducing image artifacts and improving iodine quantification (RMSE reduced by 20%). With DS-PCD-CT, multi-energy CT can be performed on large patients that cannot be accommodated with current DECT.

Ring-artifacts removal for photon-counting CT.

Ring artifacts usually appear in photon counting computed tomography (PCCT) images, which may compromise image quality and cause non-uniformity bias. This study proposed a fast ring artifacts removal method by exploring the correlation from projections with different views for PCCT. This method has three advantages. First, our method only employ mean projection of current scan to correct projections without additional scans. Second, our method can correct the inconsistency of all detector pixels simultaneously without locating the inconsistent response pixels. Third, it can preserve reconstructed image details well without extra computational cost. Both numerical and preclinical experiments demonstrate the proposed method can suppress the ring artifacts very well than the competitors.

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

BackgroundIt 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 methodsOptimal 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.ResultsThe 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.ConclusionSPECT 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.