CT-based attenuation correction in I-123-ioflupane SPECT.

Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation correction (AC) without the need for additional CT images, a deep learning method was used to generate pseudo-CT images has previously been reported, but it is limited because of cross-modality transformation, resulting in misalignment and modality-specific artifacts. This study aimed to develop a deep learning-based approach using non-attenuation-corrected (NAC) images and CTAC-based images for training to yield AC images in brain-perfusion SPECT. This study also investigated whether the proposed approach is superior to conventional Chang's AC (ChangAC). In total, 236 patients who underwent brain-perfusion SPECT were randomly divided into two groups: the training group (189 patients; 80%) and the test group (47 patients; 20%). Two models were constructed using Autoencoder (AutoencoderAC) and U-Net (U-NetAC), respectively. ChangAC, AutoencoderAC, and U-NetAC approaches were compared with CTAC using qualitative analysis (visual evaluation) and quantitative analysis (normalized mean squared error [NMSE] and the percentage error in each brain region). Statistical analyses were performed using the Wilcoxon signed-rank sum test and Bland-Altman analysis. U-NetAC had the highest visual evaluation score. The NMSE results for the U-NetAC were the lowest, followed by AutoencoderAC and ChangAC (P<0.001). Bland-Altman analysis showed a fixed bias for ChangAC and AutoencoderAC and a proportional bias for ChangAC. ChangAC underestimated counts by 30-40% in all brain regions. AutoencoderAC and U-NetAC produced mean errors of <1% and maximum errors of 3%, respectively. New deep learning-based AC methods for AutoencoderAC and U-NetAC were developed. Their accuracy was higher than that obtained by ChangAC. U-NetAC exhibited higher qualitative and quantitative accuracy than AutoencoderAC. We generated highly accurate AC images directly from NAC images without the need for intermediate pseudo-CT images. To verify our models' generalizability, external validation is required.

Chang's method, the most widely used attenuation correction (AC) in brain single-photon emission computed tomography (SPECT), requires delineation of the outer contour of the head. Manual and automatic threshold-based methods are prone to errors due to variability of tracer uptake in the scalp. The present study proposes a new method for fully automated delineation of the head based on stereotactical normalization. The method was validated for SPECT with I-123-ioflupane. The new method was compared to threshold-based delineation in 62 unselected patients who had received I-123-ioflupane SPECT at one of 3 centres. The impact on diagnostic power was tested for semi-quantitative analysis and visual reading of the SPECT images (six independent readers). The two delineation methods produced highly consistent semi-quantitative results. This was confirmed by receiver operating characteristic analyses in which the putamen specific-to-background ratio achieved highest area under the curve with negligible effect of the delineation method: 0.935 versus 0.938 for stereotactical normalization and threshold-based delineation, respectively. Visual interpretation of DVR images was also not affected by the delineation method. Delineation of the head contour by stereotactical normalization appears useful for Chang AC in I-123-ioflupane SPECT. It is robust and does not require user interaction. •Chang attenuation correction in brain SPECT requires delineation of the head contour. •Manual and threshold-based methods are prone to errors. •The study proposes a fully-automated method for delineation based on stereotactical normalization. •The method is shown to work reliably in I-123-ioflupane SPECT. •It might improve the workflow of I-123-ioflupane SPECT in everyday patient care.

This prospective study evaluated the contribution of single-photon emission computed tomography (SPECT)-(low-dose)CT (SPECT-ldCT) over SPECT alone in all-comers referred for bone scintigraphy for any indication. In this prospective study, imaging was performed on 100 consecutive patients who presented for bone scintigraphy using a combined SPECT-ldCT single-gantry system (Brightview XCT, Philips Medical Systems Inc., Cleveland, OH). SPECT images were reconstructed with (AC) and without (NAC) attenuation and scatter correction. SPECT (NAC), SPECT-ldCT (NAC), and SPECT-ldCT (AC) were reviewed independently and in a blinded manner. Reader interpretation of images was compared with the final clinical diagnosis. Subjects were referred for oncologic (28%) and nononcologic (72%) indications. Attenuation correction significantly improved perceived image quality (P = 0.012), but did not significantly alter diagnostic confidence (P = 0.96). Availability of ldCT data during interpretation of the SPECT images yielded a significant increase in the level of diagnostic confidence (P < 0.001). When the ldCT data were available, 18 of 200 bone SPECT reads recommended additional diagnostic CT imaging, compared with 70 when ldCT data were unavailable at the time of reading (P < 0.001). The sensitivity, specificity, and accuracy of SPECT-ldCT (with or without attenuation and scatter correction) in the diagnosis of osseous processes were 90.9%, 85.9%, and 87.0%, respectively, and these values did not differ significantly from those obtained with SPECT alone. Bone SPECT-ldCT provides interpreting physicians a significantly greater level of diagnostic confidence and reduces additional diagnostic imaging studies, but the overall diagnostic accuracy of SPECT-ldCT was not affected when compared with SPECT alone, suggesting that SPECT-ldCT should be used on a patient-by-patient basis.

Low spatial resolution and the related partial volume effects limit the diagnostic potential of brain single photon emission computed tomography (SPECT) imaging. As a possible remedy for this problem we propose a technique for the fusion of SPECT and MR images, which requires for a given patient the SPECT data and the T1-weighted MR image. Basically, after the reconstruction and coregistration steps, the high-frequency part of the MR, which would be unrecoverable by the set SPECT acquisition system + reconstruction algorithm, is extracted and added to the SPECT image. The tuning of the weight of the MR on the resulting fused image can be performed very quickly, any iterative reconstruction algorithm can be used and, in the case that the SPECT projections are not available, the proposed technique can also be applied directly to the SPECT image, provided that the performance of the scanner is known. The procedure has the potential of increasing the diagnostic value of a SPECT image. Even in the locations of SPECT-MR mismatch it does not significantly affect quantitation over regions of interest (ROIs) whose dimensions are decidedly larger than the SPECT resolution distance. On the other hand, appreciable corrections for partial volume effects are expected in the locations where the contrast in the structural MR matches the corresponding contrast in functional activity.

We evaluated a novel normal database (NDB) generated using single photon emission computed tomography (SPECT) data obtained from healthy brains by using a SPECT/CT system, analyzed using a resolution recovery (RR) technique applied to the three-dimensional stereotactic surface projection (3D-SSP) technique. We used a three-dimensional ordered subset expectation maximization method (3D-OSEM) with applied scatter correction (SC), attenuation correction, and RR to reconstruct the data. We verified the accuracy of the novel NDB's values (Z, extent, and error scores), and compared the novel NDB to the 3D-SSP technique by using simulated misery perfusion-related patient data from a conventional NDB. In addition, Z, extent, and error scores at the precuneus, cuneus, and posterior cingulate were compared under different reconstruction conditions by using the patient data. In the simulation, Z scores decreased when using the novel NDB corrected using computed tomography-based attenuation correction (CTAC), SC, and RR. The extent scores of the posterior cingulate increased using the novel NDB, relative to the other NDBs. The error score with the novel NDB without RR decreased by 15% compared to that of the conventional NDB. Z scores generated from patient data decreased in the novel NDB with RR. The extent scores tended to decrease in the novel NDB with RR. The extent scores in the novel NDB with RR improved at the posterior cingulate, compared to the scores with the other NDBs. However, applying RR to the novel NDB conferred no advantage because the cut-off of the current Z score must be reconsidered when using the additive RR technique.

ObjectivesTo analyse the differences in the clinical features and characteristics of 123I-labelled 2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl) nortropane (123I-FP-CIT) single photon emission CT (SPECT) imaging among patients with vascular parkinsonism (VP) and Parkinson's disease...

For PET/CT systems, PET image reconstruction requires corresponding CT images for anatomical localization and attenuation correction. In the case of PET respiratory gating, multiple gated CT scans can offer phase-matched attenuation and motion correction, at the expense of increased radiation dose. We aim to minimize the dose of the CT scan, while preserving adequate image quality for the purpose of PET attenuation correction by introducing sparse view CT data acquisition.We investigated sparse view CT acquisition protocols resulting in ultra-low dose CT scans designed for PET attenuation correction. We analyzed the tradeoffs between the number of views and the integrated tube current per view for a given dose using CT and PET simulations of a 3D NCAT phantom with lesions inserted into liver and lung. We simulated seven CT acquisition protocols with {984, 328, 123, 41, 24, 12, 8} views per rotation at a gantry speed of 0.35 s. One standard dose and four ultra-low dose levels, namely, 0.35 mAs, 0.175 mAs, 0.0875 mAs, and 0.043 75 mAs, were investigated. Both the analytical Feldkamp, Davis and Kress (FDK) algorithm and the Model Based Iterative Reconstruction (MBIR) algorithm were used for CT image reconstruction. We also evaluated the impact of sinogram interpolation to estimate the missing projection measurements due to sparse view data acquisition. For MBIR, we used a penalized weighted least squares (PWLS) cost function with an approximate total-variation (TV) regularizing penalty function. We compared a tube pulsing mode and a continuous exposure mode for sparse view data acquisition. Global PET ensemble root-mean-squares-error (RMSE) and local ensemble lesion activity error were used as quantitative evaluation metrics for PET image quality.With sparse view sampling, it is possible to greatly reduce the CT scan dose when it is primarily used for PET attenuation correction with little or no measureable effect on the PET image. For the four ultra-low dose levels simulated, sparse view protocols with 41 and 24 views best balanced the tradeoff between electronic noise and aliasing artifacts. In terms of lesion activity error and ensemble RMSE of the PET images, these two protocols, when combined with MBIR, are able to provide results that are comparable to the baseline full dose CT scan. View interpolation significantly improves the performance of FDK reconstruction but was not necessary for MBIR. With the more technically feasible continuous exposure data acquisition, the CT images show an increase in azimuthal blur compared to tube pulsing. However, this blurring generally does not have a measureable impact on PET reconstructed images.Our simulations demonstrated that ultra-low-dose CT-based attenuation correction can be achieved at dose levels on the order of 0.044 mAs with little impact on PET image quality. Highly sparse 41- or 24- view ultra-low dose CT scans are feasible for PET attenuation correction, providing the best tradeoff between electronic noise and view aliasing artifacts. The continuous exposure acquisition mode could potentially be implemented in current commercially available scanners, thus enabling sparse view data acquisition without requiring x-ray tubes capable of operating in a pulsing mode.

BackgroundIn integrated PET/MR hybrid imaging the evaluation of PET performance characteristics according to the NEMA standard NU 2–2007 is challenging because of incomplete MR-based attenuation correction (AC) for phantom imaging. In this study, a strategy for CT-based AC of the NEMA image quality (IQ) phantom is assessed. The method is systematically evaluated in NEMA IQ phantom measurements on an integrated PET/MR system.MethodsNEMA IQ measurements were performed on the integrated 3.0 Tesla PET/MR hybrid system (Biograph mMR, Siemens Healthcare). AC of the NEMA IQ phantom was realized by an MR-based and by a CT-based method. The suggested CT-based AC uses a template μ-map of the NEMA IQ phantom and a phantom holder for exact repositioning of the phantom on the systems patient table. The PET image quality parameters contrast recovery, background variability, and signal-to-noise ratio (SNR) were determined and compared for both phantom AC methods. Reconstruction parameters of an iterative 3D OP-OSEM reconstruction were optimized for highest lesion SNR in NEMA IQ phantom imaging.ResultsUsing a CT-based NEMA IQ phantom μ-map on the PET/MR system is straightforward and allowed performing accurate NEMA IQ measurements on the hybrid system. MR-based AC was determined to be insufficient for PET quantification in the tested NEMA IQ phantom because only photon attenuation caused by the MR-visible phantom filling but not the phantom housing is considered. Using the suggested CT-based AC, the highest SNR in this phantom experiment for small lesions (<= 13 mm) was obtained with 3 iterations, 21 subsets and 4 mm Gaussian filtering.ConclusionThis study suggests CT-based AC for the NEMA IQ phantom when performing PET NEMA IQ measurements on an integrated PET/MR hybrid system. The superiority of CT-based AC for this phantom is demonstrated by comparison to measurements using MR-based AC. Furthermore, optimized PET image reconstruction parameters are provided for the highest lesion SNR in NEMA IQ phantom measurements.

Attenuation correction (AC), as an essential component of quantitative SPECT imaging, is highly challenging in the absence of CT or transmission scanning in dedicated cardiac SPECT devices. This study investigated the potential of direct attenuation correction (AC) of <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">99m</sup> Tc-sestamibi SPECT myocardial perfusion imaging (MPI) in the image domain using residual (ResNet) and UNet deep convolutional neural networks. MPI-SPECT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">99m</sup> Tc-ses-tamibi images of 99 subjects were retrospectively used, considering non-attenuation corrected SPECT images as input and CT-based attenuation corrected (CT-AC) SPECT images as reference. Chang’s AC technique was also performed as a baseline for evaluating the performance of deep learning-based models. The quantitative accuracy of the attenuation corrected SPECT images was validated using the proposed methods considering the corresponding CT-AC SPECT images of 19 external datasets as reference. Image-derived metrics, including the voxel-wise mean absolute error (MAE), relative error (RE), structural similarity index measure (SSIM), as well as clinically relevant indices, such as total perfusion deficit (TPD), were calculated. The quantitative results obtained from the external validation dataset of the deep learning models exhibited good agreement with the reference CT-based attenuation corrected MPI-SPECT images. The deep learning models showed superior performance over Chang’s AC method. The ResNet and UNet models resulted in MAE of 20.24±17.63 and 13.65±11.23 and SSIM of 0.99±0.04 and 0.98±0.05, respectively. In comparison, Chang’s approach led to MAE and SSIM of 80.27±47.56 and 0.93±0.09, respectively. Similarly, the clinical evaluation showed a mean TPD of 12.78±9.22% and 12.57±8.93% for ResNet and UNet models, respectively, compared to TPD of 12.84±8.63% for reference SPECT CT-AC images. In contrast, Chang’s approach led to a mean TPD of 16.68±11.24%. The evaluation of deep learning approaches implemented in this work showed the promising potential of deep learning-based techniques in producing attenuation corrected MPI-SPECT images in the image domain without anatomical imaging.

Purpose To assess the patient-dependent accuracy of atlas-based attenuation correction (ATAC) for brain positron emission tomography (PET) in an integrated time-of-flight (TOF) PET/magnetic resonance (MR) imaging system. Materials and Methods Thirty recruited patients provided informed consent in this institutional review board-approved study. All patients underwent whole-body fluorodeoxyglucose PET/computed tomography (CT) followed by TOF PET/MR imaging. With use of TOF PET data, PET images were reconstructed with four different attenuation correction (AC) methods: PET with patient CT-based AC (CTAC), PET with ATAC (air and bone from an atlas), PET with ATACpatientBone (air and tissue from the atlas with patient bone), and PET with ATACboneless (air and tissue from the atlas without bone). For quantitative evaluation, PET mean activity concentration values were measured in 14 1-mL volumes of interest (VOIs) distributed throughout the brain and statistical significance was tested with a paired t test. Results The mean overall difference (±standard deviation) of PET with ATAC compared with PET with CTAC was -0.69 kBq/mL ± 0.60 (-4.0% ± 3.2) (P < .001). The results were patient dependent (range, -9.3% to 0.57%) and VOI dependent (range, -5.9 to -2.2). In addition, when bone was not included for AC, the overall difference of PET with ATACboneless (-9.4% ± 3.7) was significantly worse than that of PET with ATAC (-4.0% ± 3.2) (P < .001). Finally, when patient bone was used for AC instead of atlas bone, the overall difference of PET with ATACpatientBone (-1.5% ± 1.5) improved over that of PET with ATAC (-4.0% ± 3.2) (P < .001). Conclusion ATAC in PET/MR imaging achieves similar quantification accuracy to that from CTAC by means of atlas-based bone compensation. However, patient-specific anatomic differences from the atlas causes bone attenuation differences and misclassified sinuses, which result in patient-dependent performance variation of ATAC. © RSNA, 2017 Online supplemental material is available for this article.

For research and clinical PET brain studies performed on PET/CT systems, the CT image is often of little benefit beyond attenuation correction. The goal of this work is both to investigate the quantitative accuracy of CT-based attenuation correction (CTAC) for PET brain studies and to determine the lowest-dose protocol that performs adequately. Measurements were performed on a GE Discovery ST PET/CT scanner using the GE kVp-dependent CTAC algorithm. The effects of pitch on CT images were investigated by acquiring CT images of a Defrise disk phantom. The effects of pitch and x-ray tube voltage and current were investigated using a human skull phantom encased in plastic filled with F-18 radioactivity. This phantom was imaged and CT-based attenuation correction factors (CTACF) were created for several permutations of pitch (0.562:1, 0.938:1, 1.375:1, 1.75:1), voltage (80, 100, 120, 140 kVp) and current (10, 100mA). Emission images were acquired and reconstructed using the 3-D reprojection algorithm with the various CTACFs. The measured mean activity concentration is independent of pitch, kVp, and mA and accurate on an absolute scale to ~5%. Anatomically sized regional differences in the brain region indicate that tube voltages less than 100 kVp may not perform adequately (10% of values have a discrepancy greater than 5%). Results indicate that the higher pitch, lower current, and tube voltages down to 100 kVp perform equivalently to higher-dose configurations.

LVEF measurements from cardiac PET/CT images obtained using CT based attenuation correction

Added value of attenuation-corrected Tc-99m tetrofosmin SPECT for the detection of myocardial viability: Comparison with FDG SPECT

We describe a new MR-based attenuation correction (MRAC) method for neurological studies performed using integrated PET/MR scanners. The method, combining the advantages of image segmentation and atlas-based approaches to generate a high-resolution template, is based on the widely available SPM8 software and provides robust and accurate linear attenuation coefficients (LACs) for head while requiring minimal user interaction. Atlas generation: 3T MR and CT images from 15 glioblastoma subjects were used to generate the high-resolution atlas. MR images were segmented into 6 tissue classes: GM, WM, CSF, soft tissue, bone and air)[1]. Tissue classes were then coregistered using an iterative diffeomorphic image registration algorithm [2] to form the template. Atlas validation: The template was validated on 16 subjects. SyN [3] and IRTK [4], considered state-of-the-art for non-rigid image registration[5], were used for comparison. Final attenuation maps were created from the warped CT atlas following [6]. PET images were then reconstructed using the proposed methods as well as the manufacturer’s built-in method (dual-echo Dixon-VIBE sequence) [7] and compared to the gold standard CT-based attenuation correction (CTAC). The qualitative and quantitative analysis of the attenuation maps revealed that the SPM8-based method produces very robust results (Figure ​(Figure1).1). In terms of the PET data quantification, we observed improvements of > 70% compared to the VIBE-based method (Table ​(Table11 and Figure ​Figure2).2). When compared to SyN-based image registration, the SPM8 approach showed improved global results on the brain area (Figures ​(Figures11 and ​and22). Figure 1 Comparison of LACs from a validation subject for our proposed method (A), the SyN method (B) and the manufacturer’s built-in Dixon method (C) to the gold standard CTAC (D). Image differences with respect to the gold standard CTAC of our method ... Table 1 Summary of voxel- and ROI-based results between our method (atlas) and the current manufacturer’s method (Dixon) Figure 2 PET images from a validation subject reconstructed with our proposed method (A), with the SyN method (B) and with the manufacturer’s built-in Dixon method (C), compared with the gold standard CTAC (D). Relative changes (in % with respect to gold ... We presented a new MRAC technique for brain images acquired on simultaneous PET/MR scanners. The new approach relies on segmentation- and atlas-based features to provide robust and more accurate LACs than using state-of-art non-rigid image registration while avoiding sophisticated user input or interaction.

Somatostatin receptor scintigraphy (SRS) is well-established in neuroendocrine tumour (NET) imaging. This study evaluated the impact of attenuation correction (AC) on SRS SPECT data in patients examined by SPECT-CT. Planar scintigraphy and SPECT-CT of 17 patients (10 men, seven women; age, 40-74 years; mean, 62 years) suffering from NET were included. For the visual assessment of AC, the intensity and contrast of foci classified as pathological were rated in both the non-attenuation corrected (NAC) and the attenuation corrected (AC) SPECT images using a 5-point score. The change in signal intensity after AC was semiquantified two-fold for each focus in both SPECT(AC) and SPECT(NAC): firstly by using tumour-to-background (TB) ratios (defined as T(max)/B(mean)) for the determination of a TB(AC)/TB(NAC) ratio. Secondly, by a T(max,AC)/T(max,NAC) ratio. Both ratios were correlated to the focus depth. A total of 46 pathological foci were found. Focus contrast and intensity significantly increased in 14/46 foci (30%) after AC (mean, 3.7-4.0) in the visual analysis (P<0.001). While TB ratios increased only in 24/46 foci after AC and no correlation between the T(BAC)/T(BNAC) ratio and focus depth (r=0.027; P=0.856) was found, T(max) was higher after AC in all foci and the T(max,AC)/T(max,NAC) ratio showed the expected correlation to focus depth (r=0.650; P<0.01), indicating the superiority of the Tmax approach for the demonstration of the effects of attenuation correction on focal uptake. Attenuation correction of SRS SPECT data by SPECT-CT results in visually more clearly contrasted foci. Moreover, as focus intensity increases, especially in the more centrally localised foci, CT-based AC has a potential to further improve the sensitivity of SRS SPECT.