Attenuation Correction Method Research Articles

Thoracic aortic microcalcification activity in combined positron emission tomography and magnetic resonance imaging.

Non-invasive detection of pathological changes in thoracic aortic disease remains an unmet clinical need particularly for patients with congenital heart disease. Positron emission tomography combined with magnetic resonance imaging (PET-MRI) could provide a valuable low-radiation method of aortic surveillance in high-risk groups. Quantification of aortic microcalcification activity using sodium [18F]fluoride holds promise in the assessment of thoracic aortopathies. We sought to evaluate aortic sodium [18F]fluoride uptake in PET-MRI using three methods of attenuation correction compared to positron emission tomography computed tomography (PET-CT) in patients with bicuspid aortic valve, METHODS: Thirty asymptomatic patients under surveillance for bicuspid aortic valve disease underwent sodium [18F]fluoride PET-CT and PET-MRI of the ascending thoracic aorta during a single visit. PET-MRI data were reconstructed using three iterations of attenuation correction (Dixon, radial gradient recalled echo with two [RadialVIBE-2] or four [RadialVIBE-4] tissue segmentation). Images were qualitatively and quantitatively analysed for aortic sodium [18F]fluoride uptake on PET-CT and PET-MRI. Aortic sodium [18F]fluoride uptake on PET-MRI was visually comparable with PET-CT using each reconstruction and total aortic standardised uptake values on PET-CT strongly correlated with each PET-MRI attenuation correction method (Dixon R = 0.70; RadialVIBE-2 R = 0.63; RadialVIBE-4 R = 0.64; p < 0.001 for all). Breathing related artefact between soft tissue and lung were detected using Dixon and RadialVIBE-4 but not RadialVIBE-2 reconstructions, with the presence of this artefact adjacent to the atria leading to variations in blood pool activity estimates. Consequently, quantitative agreements between radiotracer activity on PET-CT and PET-MRI were most consistent with RadialVIBE-2. Ascending aortic microcalcification analysis in PET-MRI is feasible with comparable findings to PET-CT. RadialVIBE-2 tissue attenuation correction correlates best with the reference standard of PET-CT and is less susceptible to artefact. There remain challenges in segmenting tissue types in PET-MRI reconstructions, and improved attenuation correction methods are required.

Learning CT-free attenuation-corrected total-body PET images through deep learning.

Total-body PET/CT scanners with long axial fields of view have enabled unprecedented image quality and quantitative accuracy. However, the ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Therefore, we attempted to generate CT-free attenuation-corrected (CTF-AC) total-body PET images through deep learning. Based on total-body PET data from 122 subjects (29 females and 93 males), a well-established cycle-consistent generative adversarial network (Cycle-GAN) was employed to generate CTF-AC total-body PET images directly while introducing site structures as prior information. Statistical analyses, including Pearson correlation coefficient (PCC) and t-tests, were utilized for the correlation measurements. The generated CTF-AC total-body PET images closely resembled real AC PET images, showing reduced noise and good contrast in different tissue structures. The obtained peak signal-to-noise ratio and structural similarity index measure values were 36.92 ± 5.49 dB (p < 0.01) and 0.980 ± 0.041 (p < 0.01), respectively. Furthermore, the standardized uptake value (SUV) distribution was consistent with that of real AC PET images. Our approach could directly generate CTF-AC total-body PET images, greatly reducing the radiation risk to patients from redundant anatomical examinations. Moreover, the model was validated based on a multidose-level NAC-AC PET dataset, demonstrating the potential of our method for low-dose PET attenuation correction. In future work, we will attempt to validate the proposed method with total-body PET/CT systems in more clinical practices. The ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Our CT-free PET attenuation correction method would be beneficial for a wide range of patient populations, especially for pediatric examinations and patients who need multiple scans or who require long-term follow-up. • CT is the main source of radiation in PET/CT imaging, especially for total-body PET/CT devices, and reduced radiopharmaceutical doses make the radiation burden from CT more obvious. • The CT-free PET attenuation correction method would be beneficial for patients who need multiple scans or long-term follow-up by reducing additional radiation from redundant anatomical examinations. • The proposed method could directly generate CT-free attenuation-corrected (CTF-AC) total-body PET images, which is beneficial for PET/MRI or PET-only devices lacking CT image poses.

Evaluation of attenuation correction method for head holder in brain perfusion single-photon emission computed tomography.

Head holder attenuation affects brain perfusion single-photon emission computed tomography (SPECT) image quality. Here, we proposed a head holder-attenuation correction (AC) method using attenuation coefficient maps calculated by Chang's method from CT images. Then, we evaluated the effectiveness of the head holder-AC method by numerical phantom and clinical cerebral perfusion SPECT studies. In the numerical phantom, the posterior counts were 10.7% lower than the anterior counts without head holder-AC method. However, by performing head holder-AC, the posterior count recovered by approximately 6.8%, approaching the true value. In the clinical study, the normalized count ratio was significantly increased by performing the head holder-AC method in the posterior-middle cerebral artery, posterior cerebral artery and cerebellum regions. There were no significant increases in other regions. The head holder-AC method can correct the counts attenuated by the head holder.

Deep learning-based attenuation correction method in 99mTc-GSA SPECT/CT hepatic imaging: a phantom study.

This study aimed to evaluate a deep learning-based attenuation correction (AC) method to generate pseudo-computed tomography (CT) images from non-AC single-photon emission computed tomography images (SPECTNC) for AC in 99mTc-galactosyl human albumin diethylenetriamine pentaacetic acid (GSA) scintigraphy and to reduce patient dosage. A cycle-consistent generative network (CycleGAN) model was used to generate pseudo-CT images. The training datasets comprised approximately 850 liver phantom images obtained from SPECTNC and real CT images. The training datasets were then input to CycleGAN, and pseudo-CT images were output. SPECT images with real-time CT attenuation correction (SPECTCTAC) and pseudo-CT attenuation correction (SPECTGAN) were acquired. The difference in liver volume between real CT and pseudo-CT images was evaluated. Total counts and uniformity were then used to evaluate the effects of AC. Additionally, the similarity coefficients of SPECTCTAC and SPECTGAN were assessed using a structural similarity (SSIM) index. The pseudo-CT images produced a lower liver volume than the real CT images. SPECTCTAC exhibited a higher total count than SPECTNC and SPECTGAN, which were approximately 60% and 7% lower, respectively. The uniformities of SPECTCTAC and SPECTGAN were better than those of SPECTNC. The mean SSIM value for SPECTCTAC and SPECTGAN was 0.97. We proposed a deep learning-based AC approach to generate pseudo-CT images from SPECTNC images in 99mTc-GSA scintigraphy. SPECTGAN with AC using pseudo-CT images was similar to SPECTCTAC, demonstrating the possibility of SPECT/CT examination with reduced exposure to radiation.

Deep-learning-based methods of attenuation correction for SPECT and PET

Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to generate attenuation maps (μ-maps) for AC of hybrid SPECT/CT and PET/CT scanners. However, CT-based AC methods frequently produce artifacts due to CT artifacts and misregistration of SPECT-CT and PET-CT scans. Segmentation-based AC methods using magnetic resonance imaging (MRI) for PET/MRI scanners are inaccurate and complicated since MRI does not contain direct information of photon attenuation. Computational AC methods for SPECT and PET estimate attenuation coefficients directly from raw emission data, but suffer from low accuracy, cross-talk artifacts, high computational complexity, and high noise level. The recently evolving deep-learning-based methods have shown promising results in AC of SPECT and PET, which can be generally divided into two categories: indirect and direct strategies. Indirect AC strategies apply neural networks to transform emission, transmission, or MR images into synthetic μ-maps or CT images which are then incorporated into AC reconstruction. Direct AC strategies skip the intermediate steps of generating μ-maps or CT images and predict AC SPECT or PET images from non-attenuation-correction (NAC) SPECT or PET images directly. These deep-learning-based AC methods show comparable and even superior performance to non-deep-learning methods. In this article, we first discussed the principles and limitations of non-deep-learning AC methods, and then reviewed the status and prospects of deep-learning-based methods for AC of SPECT and PET.

Pelvic PET/MR attenuation correction in the image space using deep learning.

The five-class Dixon-based PET/MR attenuation correction (AC) model, which adds bone information to the four-class model by registering major bones from a bone atlas, has been shown to be error-prone. In this study, we introduce a novel method of accounting for bone in pelvic PET/MR AC by directly predicting the errors in the PET image space caused by the lack of bone in four-class Dixon-based attenuation correction. A convolutional neural network was trained to predict the four-class AC error map relative to CT-based attenuation correction. Dixon MR images and the four-class attenuation correction µ-map were used as input to the models. CT and PET/MR examinations for 22 patients ([18F]FDG) were used for training and validation, and 17 patients were used for testing (6 [18F]PSMA-1007 and 11 [68Ga]Ga-PSMA-11). A quantitative analysis of PSMA uptake using voxel- and lesion-based error metrics was used to assess performance. In the voxel-based analysis, the proposed model reduced the median root mean squared percentage error from 12.1% and 8.6% for the four- and five-class Dixon-based AC methods, respectively, to 6.2%. The median absolute percentage error in the maximum standardized uptake value (SUVmax) in bone lesions improved from 20.0% and 7.0% for four- and five-class Dixon-based AC methods to 3.8%. The proposed method reduces the voxel-based error and SUVmax errors in bone lesions when compared to the four- and five-class Dixon-based AC models.

Generative adversarial network-based attenuation correction for 99mTc-TRODAT-1 brain SPECT.

Attenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT). Chang's method was developed for AC (Chang-AC) when CT-based AC was not available, assuming uniform attenuation coefficients inside the body contour. This study aims to evaluate Chang-AC and different deep learning (DL)-based AC approaches on 99mTc-TRODAT-1 brain SPECT using clinical patient data on two different scanners. Two hundred and sixty patients who underwent 99mTc-TRODAT-1 SPECT/CT scans from two different scanners (scanner A and scanner B) were retrospectively recruited. The ordered-subset expectation-maximization (OS-EM) method reconstructed 120 projections with dual-energy scatter correction, with or without CT-AC. We implemented a 3D conditional generative adversarial network (cGAN) for the indirect deep learning-based attenuation correction (DL-ACμ) and direct deep learning-based attenuation correction (DL-AC) methods, estimating attenuation maps (μ-maps) and attenuation-corrected SPECT images from non-attenuation-corrected (NAC) SPECT, respectively. We further applied cross-scanner training (cross-scanner indirect deep learning-based attenuation correction [cull-ACμ] and cross-scanner direct deep learning-based attenuation correction [call-AC]) and merged the datasets from two scanners for ensemble training (ensemble indirect deep learning-based attenuation correction [eDL-ACμ] and ensemble direct deep learning-based attenuation correction [eDL-AC]). The estimated μ-maps from (c/e)DL-ACμ were then used in reconstruction for AC purposes. Chang's method was also implemented for comparison. Normalized mean square error (NMSE), structural similarity index (SSIM), specific uptake ratio (SUR), and asymmetry index (%ASI) of the striatum were calculated for different AC methods. The NMSE for Chang's method, DL-ACμ, DL-AC, cDL-ACμ, cDL-AC, eDL-ACμ, and eDL-AC is 0.0406 ± 0.0445, 0.0059 ± 0.0035, 0.0099 ± 0.0066, 0.0253 ± 0.0102, 0.0369 ± 0.0124, 0.0098 ± 0.0035, and 0.0162 ± 0.0118 for scanner A and 0.0579 ± 0.0146, 0.0055 ± 0.0034, 0.0063 ± 0.0028, 0.0235 ± 0.0085, 0.0349 ± 0.0086, 0.0115 ± 0.0062, and 0.0117 ± 0.0038 for scanner B, respectively. The SUR and %ASI results for DL-ACμ are closer to CT-AC, Followed by DL-AC, eDL-ACμ, cDL-ACμ, cDL-AC, eDL-AC, Chang's method, and NAC. All DL-based AC methods are superior to Chang-AC. DL-ACμ is superior to DL-AC. Scanner-specific training is superior to cross-scanner and ensemble training. DL-based AC methods are feasible and robust for 99mTc-TRODAT-1 brain SPECT.

CT VS MR Attenuation Correction: A Systematic Review on PET Image Quality Assessment

This systematic review was conducted to evaluate the image quality performance when implementing computed tomography data (CTAC) or magnetic resonance data for attenuation correction (MRAC) on positron emission tomography (PET) images. The CTAC and MRAC were performed on image from PET/CT and PET/MR scanners, respectively. The systematic review was done based on Preferred Reporting Items for Systematic Reviews (PRISMA). In this study, twelve articles were included from six databases. The image performance was evaluated by overall image quality, contrast, spatial resolution, detectability, standardised uptake value (SUV) and acquisition time. Data was shown as mean ± standard deviation and compared between CTAC and MRAC images to determine which attenuation correction method provides better image quality. Results found that PET-CTAC and PET-MRAC have similar image performance in overall image quality (p=0.93), detectabilty (p=0.84), SUVmean (p=0.84) and SUVmax (p=0.81). Meanwhile, PET-CTAC acquisition time is significantly faster than PET-MRAC by approximately two fold (p &lt;0.05). There were no statistical analyses performed for image contrast, spatial resolution and contrast-noise-ratio due to the insufficient data. In conclusion, although PET/CT is faster than PET/MRI procedure, images yielded from CTAC and MRAC are equivalent to each other. Due to the variation of linear attenuation coefficient for each type of tissue, future review of image quality comparison can be done focusing on specific tissue or region such as soft tissue, bone and lungs to reflect the real impact of CTAC and MRAC on PET image.

Attenuation Correction of the X-Band Dual-Polarization Phased Array Radar Based on Observed Raindrop Size Distribution Characteristics

X-band dual-polarization phased array radar (XPAR-D) possesses high resolution and plays a significant role in detecting meso- and micro-scale convective systems. However, the precipitation attenuation it endures necessitates an effective correction method. This study selected radar data from XPAR-D at the peak of Maofeng Mountain in Guangzhou during 16–17 May 2020 from three precipitation stages after quality control. Attenuation coefficients were calculated for different precipitation types through scattering simulations of raindrop size distribution (RSD) data. Next, an attenuation correction algorithm (MZH-KDP method) was proposed for the radar reflectivity factor (ZH) according to different raindrop types and compared to the ZH-KDP method currently in use. The results indicate that the attenuation amount of XPAR-D echoes depends on the attenuation path and echo intensity. When the attenuation path is shorter and the echo intensity is weaker, the amount of attenuation and correction is smaller. Difficulties arise when there are noticeable deviations, which are challenging to resolve using attenuation correction methods. Longer attenuation paths and stronger echoes highlight the advantages of the MZH-KDP method, while the ZH-KDP method tends to overcorrect the bias. The MZH-KDP method outperforms the ZH-KDP method for different precipitation types. The superior correction capability of the MZH-KDP method provides a significant advantage in improving the performance of XPAR-D for the detection of extreme weather.

Deep-learning-based estimation of attenuation map improves attenuation correction performance over direct attenuation estimation for myocardial perfusion SPECT

BackgroundDeep learning (DL)-based attenuation correction (AC) is promising to improve myocardial perfusion (MP) SPECT. We aimed to optimize and compare the DL-based direct and indirect AC methods, with and without SPECT and CT mismatch. MethodsOne hundred patients with different 99mTc-sestamibi activity distributions and anatomical variations were simulated by a population of XCAT phantoms. Additionally, 34 patients 99mTc-sestamibi stress/rest SPECT/CT scans were retrospectively recruited. Projections were reconstructed by OS-EM method with or without AC. Mismatch between SPECT and CT images was modeled. A 3D conditional generative adversarial network (cGAN) was optimized for two DL-based AC methods: (i) indirect approach, i.e., non-attenuation corrected (NAC) SPECT paired with the corresponding attenuation map for training. The projections were reconstructed with the DL-generated attenuation map for AC; (ii) direct approach, i.e., NAC SPECT paired with the corresponding AC SPECT for training to perform direct AC. ResultsMismatch between SPECT and CT degraded DL-based AC performance. The indirect approach is superior to direct approach for various physical and clinical indices, even with mismatch modeled. ConclusionDL-based estimation of attenuation map for AC is superior and more robust to direct generation of AC SPECT.

Artificial Intelligence in Nuclear Cardiology

Artificial intelligence (AI) encompasses a variety of computer algorithms that have a wide range of potential clinical applications in nuclear cardiology. This article will introduce core terminology and concepts for AI including classifications of AI as well as training and testing regimens. We will then highlight the potential role for AI to improve image registration and image quality. Next, we will discuss methods for AI-driven image attenuation correction. Finally, we will review advancements in machine learning and deep-learning applications for disease diagnosis and risk stratification, including efforts to improve clinical translation of this valuable technology with explainable AI models.

DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants.

Conventional magnetic resonance imaging (MRI) can for glioma assessment be supplemented by positron emission tomography (PET) imaging with radiolabeled amino acids such as O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET), which provides additional information on metabolic properties. In neuro-oncology, patients often undergo brain and skull altering treatment, which is known to challenge MRI-based attenuation correction (MR-AC) methods and thereby impact the simplified semi-quantitative measures such as tumor-to-brain ratio (TBR) used in clinical routine. The aim of the present study was to examine the applicability of our deep learning method, DeepDixon, for MR-AC in [18F]FET PET/MRI scans of a post-surgery glioma cohort with metal implants. The MR-AC maps were assessed for all 194 included post-surgery glioma patients (318 studies). The subgroup of 147 patients (222 studies, 200 MBq [18F]FET PET/MRI) with tracer uptake above 1 ml were subsequently reconstructed with DeepDixon, vendor-default atlas-based method, and a low-dose computed tomography (CT) used as reference. The biological tumor volume (BTV) was delineated on each patient by isocontouring tracer uptake above a TBR threshold of 1.6. We evaluated the MR-AC methods using the recommended clinical metrics BTV and mean and maximum TBR on a patient-by-patient basis against the reference with CT-AC. Ninety-seven percent of the studies (310/318) did not have any major artifacts using DeepDixon, which resulted in a Dice coefficient of 0.89/0.83 for tissue/bone, respectively, compared to 0.84/0.57 when using atlas. The average difference between DeepDixon and CT-AC was within 0.2% across all clinical metrics, and no statistically significant difference was found. When using DeepDixon, only 3 out of 222 studies (1%) exceeded our acceptance criteria compared to 72 of the 222 studies (32%) with the atlas method. We evaluated the performance of a state-of-the-art MR-AC method on the largest post-surgical glioma patient cohort to date. We found that DeepDixon could overcome most of the issues arising from irregular anatomy and metal artifacts present in the cohort resulting in clinical metrics within acceptable limits of the reference CT-AC in almost all cases. This is a significant improvement over the vendor-provided atlas method and of particular importance in response assessment.

Accounting for blood attenuation in intravascular near-infrared fluorescence-ultrasound imaging using a fluorophore-coated guidewire.

Intravascular near-infrared fluorescence (NIRF) imaging aims to improve the inspection of vascular pathology using fluorescent agents with specificity to vascular disease biomarkers. The method has been developed to operate in tandem with an anatomical modality, such as intravascular ultrasound (IVUS), and complements anatomical readings with pathophysiological contrast, enhancing the information obtained from the hybrid examination. However, attenuation of NIRF signals by blood challenges NIRF quantification. We propose a new method for attenuation correction in NIRF intravascular imaging based on a fluorophore-coated guidewire that is used as a reference for the fluorescence measurement and provides a real-time measurement of blood attenuation during the NIRF examination. We examine the performance of the method in a porcine coronary artery ex vivo and phantoms using a 3.2F NIRF-IVUS catheter. We demonstrate marked improvement over uncorrected signals of up to 4.5-fold and errors of for target signals acquired at distances up to 1mm from the catheter system employed. The method offers a potential means of improving the accuracy of intravascular NIRF imaging under in vivo conditions.

Study on Attenuation Correction for the Reflectivity of X-Band Dual-Polarization Phased-Array Weather Radar Based on a Network with S-Band Weather Radar

X-band dual-polarization phased-array weather radars (X-PARs) have been used in South China extensively. Eliminating the attenuation and system bias of X-band radar data is the key to utilizing the advantage of X-PAR networks. In this paper, the disdrometer raindrop-size distribution (DSD) measurements are used to calculate the radar polarimetric variables and analyze the characteristics of precipitation attenuation. Furthermore, based on the network of S-band dual-polarization Doppler weather radar (S-POL) and X-PARs, an attenuation-correction method for X-PAR reflectivity is proposed with S-POL constraints in view of the radar-mosaic requirements of a multi-radar network. Linear programming is used to calculate the attenuation-correction parameters of different rainfall areas, which realizes the attenuation correction for X-PAR. The results show that the attenuation-correction parameters simulated based on the disdrometer DSD vary with different precipitation classification; the attenuation-corrected reflectivity of X-PARs is consistent with S-POL and can realize a more precise observation of the evolution of the convective system. Compared with previous attenuation-correction methods with constant correction parameters, the improved method can reduce the deviation between X-PAR reflectivity and that of S-POL in heavy rainfall areas and areas of strong attenuation. The method proposed in this paper is stable and effective. After effective quality control, it is found that the X-PAR network deployed in South China observes data accurately and is consistent with S-POL; thus, it is expected to achieve high temporal–spatial resolution within a radar mosaic.

X-Band Radar Attenuation Correction Method Based on LightGBM Algorithm

X-band weather radar can provide high spatial and temporal resolution data, which is essential to precipitation observation and prediction of mesoscale and microscale weather. However, X-band weather radar is susceptible to precipitation attenuation. This paper presents an X-band attenuation correction method based on the light gradient machine (LightGBM) algorithm (the XACL method), then compares it with the ZH correction method and the ZH-KDP comprehensive correction method. The XACL method was validated using observations from two radars in July 2021, the X-band dual-polarization weather radar at the Shouxian National Climatology Observatory of China (SNCOC), and the S-band dual-polarization weather radar at Hefei. During the rainfall cases on July 2021, the results of the attenuation correction were used for precipitation estimation and verified with the rainfall data from 1204 automatic ground-based meteorological network stations in Anhui Province, China. We found that the XACL method produced a significant correction effect and reduced the anomalous correction phenomenon of the comparison methods. The results show that the average error in precipitation estimation by the XACL method was reduced by 39.88% over 1204 meteorological stations, which is better than the effect of the other two correction methods. Thus, the XACL method proved good local adaptability and provided a new X-band attenuation correction scheme.

Attenuation Correction in Weather Radars for Snow

Weather radars play a prominent role in remote sensing of the atmosphere. Various fields, such as meteorology and hydrology, rely on accurate weather radar data as input for their models. Different hydrometeors present during a weather event influence the amount of attenuation encountered by the radar signal. Attenuation correction for dual-polarization weather radar data is necessary to improve the radar products and get accurate measurements. Most of the existing attenuation correction research is associated with rain hydrometeors. Currently, research that addresses the attenuation correction of snow in weather radars is limited. Although it is known that attenuation of radar signals when it encounters rain is much greater than that for snow, attenuation for all hydrometeors needs to be addressed for accurate radar estimates. In this research work, the attenuation of different hydrometeors is studied using signal simulations. Various factors which influence attenuation, such as the elevation angle and particle size distribution, are considered, and the results are presented. An attenuation correction algorithm that uses the hydrometeor classification and specific differential phase products from the DROPS2.0 algorithm is introduced. Signal simulations are employed to obtain the relationship between specific attenuation and specific differential phase for different hydrometeors used in the proposed algorithm. The attenuation correction method is applied to X-band and Ku-band radar data. Path integrated attenuation of about 8 dB was observed in the snow case discussed from Ku band radar data. The method proposed for attenuation shows promising results at both frequency bands.

Brain imaging of sequential acquisition using a flexible PET scanner and 3-T MRI: quantitative and qualitative assessment.

A mobile PET scanner termed flexible PET (fxPET) has been designed to fit existing MRI systems. The purpose of this study was to assess brain imaging with fxPET combined with 3-T MRI in comparison with conventional PET (cPET)/CT. In this prospective study, 29 subjects with no visible lesions except for mild leukoaraiosis on whole brain imaging underwent 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) cPET/CT followed by fxPET and MRI. The registration differences between fxPET and MRI and between cPET and CT were compared by measuring spatial coordinates. Three-dimensional magnetization-prepared rapid acquisition gradient-echo T1-weighted imaging (T1WI) was acquired. We applied two methods of attenuation correction to the fxPET images: MR-based attenuation correction, which yielded fxPETMRAC; and CT-based attenuation correction, which yielded fxPETCTAC. The three PET datasets were co-registered to the T1WI. Following subcortical segmentation and cortical parcellation, volumes of interest were placed in each PET image to assess physiological accumulation in the brain. SUVmean was obtained and compared between the three datasets. We also visually evaluated image distortion and clarity of fxPETMRAC. Mean misregistration of fxPET/MRI was < 3mm for each margin. Mean registration differences were significantly larger for fxPET/MRI than for cPET/CT except for the superior margin. There were high correlations between the three PET datasets regarding SUVmean. On visual evaluation of image quality, the grade of distortion was comparable between fxPETMRAC and cPET, and the grade of clarity was acceptable but inferior for fxPETMRAC compared with cPET. fxPET could successfully determine physiological [18F]FDG uptake; however, improved image clarity is desirable. In this study, fxPET/MRI at 3-T was feasible for brain imaging.

A Headrest Made of Extruded Polystyrene Reduces the Influence of Attenuation Correction on Human Brain SPECT Images.

Previous reports suggest that a headrest made of carbon significantly influences cerebral blood flow in the anterior and posterior regions by image reconstruction and attenuation correction (AC). The present study aimed to develop a headrest that reduces the influence of the AC process on human brain SPECT. Methods: To validate the performance of a headrest made of extruded polystyrene (XPS), 10 healthy controls and 43 patients with cerebrovascular disease underwent 99mTc-ethyl cysteinate dimer SPECT using a carbon headrest and an XPS headrest. We evaluated the anterior-to-posterior and middle-to-posterior ratio of the brain regions in filtered backprojection (FBP) Chang AC, ordered-subset expectation maximization (OSEM) Chang AC, and OSEM CT-based AC. Results: The anterior-to-posterior ratio was significantly higher with the carbon headrest than with the XPS headrest in FBP Chang AC and OSEM Chang AC (P < 0.001). There was no significant difference between the materials in OSEM CT-based AC. The middle-to-posterior ratio did not differ to a statistically significant extent in any correction process. Conclusion: Acquisition of brain SPECT images with an XPS headrest and processing by the FBP or OSEM Chang AC method enables the influence of the headrest to be reduced, especially in anterior and posterior brain regions.

Attenuation correction for PET/MRI to measure tracer activity surrounding total knee arthroplasty

BackgroundPositron emission tomography (PET) in combination with magnetic resonance imaging (MRI) could allow inflammatory complications near total knee arthroplasty (TKA) to be studied early in their development. However, attenuation of the PET signal by the metal TKA implants imparts substantial error into measurements of tracer activity, and conventional MR-based attenuation correction (AC) methods have large signal voids in the vicinity of metal implants.PurposeTo evaluate a segmentation-based AC approach to measure tracer uptake from PET/MRI scans near TKA implants.MethodsA TKA implant (Triathlon, Stryker, Mahwah, USA) was implanted into a cadaver. Four vials were filled with [18F]fluorodeoxyglucose with known activity concentration (4.68 MBq total, 0.76 MBq/mL) and inserted into the knee. Images of the knee were acquired using a 3T PET/MRI system (Biograph mMR, Siemens Healthcare, Erlangen, Germany). Models of the implant components were registered to the MR data using rigid-body transformations and the other tissue classes were manually segmented. These segments were used to create the segmentation-based map and complete the AC. Percentage error of the resulting measured activities was calculated by comparing the measured and known amounts of activity in each vial.ResultsThe original AC resulted in a percentage error of 64.1% from the known total activity. Errors in the individual vial activities ranged from 40.2 to 82.7%. Using the new segmentation-based AC, the percentage error of the total activity decreased to 3.55%. Errors in the individual vials were less than 15%.ConclusionsThe segmentation-based AC technique dramatically reduced the error in activity measurements that result from PET signal attenuation by the metal TKA implant. This approach may be useful to enhance the reliability of PET/MRI measurements for numerous applications.

Attenuation correction for phantom tests: an alternative to maximum-likelihood attenuation correction factor-based correction for clinical studies in time-of-flight PET.

This study evaluates the phantom attenuation correction (PAC) method as an alternative to maximum-likelihood attenuation correction factor (ML-ACF) correction in time-of-flight (TOF) brain positron emission tomography (PET) studies. In the PAC algorithm, a template emission image [Formula: see text] and a template attenuation coefficient image [Formula: see text] are prepared as a data set based on phantom geometry. Position-aligned attenuation coefficient image [Formula: see text] is derived by aligning [Formula: see text] using parameters that match the template emission image [Formula: see text] to measured emission image [Formula: see text]. Then, attenuation coefficient image [Formula: see text] combined with a headrest image is used for scatter and attenuation correction in the image reconstruction. To evaluate the PAC algorithm as an alternative to ML-ACF, Hoffman 3D brain and cylindrical phantoms were measured to obtain the image quality indexes of contrast and uniformity. These phantoms were also wrapped with a radioactive sheet to obtain attenuation coefficient images using ML-ACF. Emission images were reconstructed with attenuation correction by PAC and ML-ACF, and the results were compared using contrast and uniformity as well as visual assessment. CT attenuation correction (CT-AC) was also applied as a reference. The contrast obtained by ML-ACF was slightly overestimated due to its unique experimental condition for applying ML-ACF in Hoffman 3D brain phantom but the uniformity was almost equivalent among ML-ACF, CT-AC, and PAC. PAC showed reasonable result without overestimation compared to ML-ACF and CT-AC. PAC is an attenuation correction method that can ensure the performance in phantom test, and is considered to be a reasonable alternative to clinically used ML-ACF-based attenuation correction.