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Introduction Currently, the addition of computed tomography (CT) to a gamma-camera has revolutionized nuclear medicine. Indeed the CT, because of its good spatial resolution, of the attenuation correction of the single photon emission computed tomography (SPECT) images and of a better anatomical localization of lesions, improves the sensitivity, specificity and accuracy of the examination. Despite the fact that the hybrid camera uses a low-dose non-diagnostic scanner, increases the overall delivered dose of radiation. Methods The aim of this study was to evaluate the contribution of CT to the total effective dose of 99mTc-HMDP (hydroxymethylenediphosphonate labelled with technetium 99 metastable) SPECT/CT for an adult oncologic population. This prospective study included 103 patients (75 women and 28 men) aged 28 to 79 years. Results The mean effective doses of SPECT, CT and SPECT/CT were respectively 3.8 mSv, 3.3 mSv and 7.1 mSv, respectively. The average contribution of CT scans to the total effective dose for SPECT/CT examination was 45 ± 9.7%, and ranged from 10 to 67.4%. The lowest value was for the thorax area. Conclusion This radiation dose is not negligible. But, taking into account the benefit of hybrid imaging, this additional radiation remains justifiable. Nevertheless, the "As Low as Reasonably Achievable (ALARA)'' principle must be respected to ensure that the patient is not subjected to unnecessarily high levels of radiation.

Emission-based attenuation correction (AC) meth-ods offer the possibility of overcoming quantification errors induced by conventional MR-based approaches in PET/MR imaging. However, the joint problem of determining AC and the activity of interest is strongly ill-posed in non-TOF PET. This can be improved by exploiting the extra information arising from low energy window photons, but the feasibility of this approach has only been studied with relatively simplistic analytic simulations so far. This manuscript aims to address some of the remaining challenges needed to handle realistic measurements; in particular, the detection efficiency (“normalisation”) estimation for each energy window is investigated. An energy-dependent detection efficiency model is proposed, accounting for the presence of unscattered events in the lower energy window due to detector scatter. Geometric calibration factors are estimated prior to the reconstruction for both scattered and unscattered events. Different reconstruction methods are also compared. Results show that geometric factors differ markedly between the energy windows and that our analytical model correspond in good approximation to Monte Carlo simulation; the multiple energy window reconstruction appears sensitive to input/model mismatch. Our method applies to Monte Carlo generated data but can be extended to measured data. This study is restricted to single scatter events.

To investigate minimal required sub milli-Sievert (mSv) ultra-low dose CT and corresponding tube current and voltage for reliable attenuation correction and semi- quantitation in 18F-FDG PET-CT in an effort for radiation dose reduction. Methods: We performed a PET-CT investigational study using a NEMA torso phantom containing six spheres (diameter: 10, 13, 17, 22, 28, 37 mm) filled with a fixed concentration of 60 kBq/ml and a background of 15 kBq/ml of 18F-FDG. Two sets of PET images, separated by 2 hours, were acquired for 3 minutes in a single bed position using 3-D mode with and without time-of-flight in a GE D-690 scanner. Several sets of CT images were acquired for attenuation correction with different combinations of tube voltage (80, 100, 120 kVp) and effective mAs (tube current-time product divided by pitch), using the maximum beam collimation (64 x 0.625 mm). The lowest CT acquisition technique available on this scanner is 10 mA, 0.4 s and 1.375 for the tube current, tube rotation time and pitch, respectively. The CT radiation dose was estimated based on the computed tomography dose index volume (CTDIvol) measurements performed following the standard methodology and the Imaging Performance Assessment of CT Scanners (ImPACT) calculator. Each of the CT techniques was used for attenuation correction to the same PET acquisition, using ordered-subset expectation maximum (OSEM) algorithm with 24 subsets and 2 iterations. The maximal and average radioactivity (kBq/ml) and standardized uptake values (SUV) of the spheres were measured. The minimal ultra-low dose CT for attenuation correction was determined by reproducible SUV measurements (±10%) compared to our reference CT protocol of 100 kVp and 80 mA for 0.5 s rotation. Results: The minimal ultra-low dose of CT for reproducible quantification in all spheres (

В огляді літератури розглянуто історичні аспекти та ключові питання клінічного застосування позитронно-емісійної томографії (ПЕТ)/комп’ютерної томографії як нового методу гібридної візуалізації. Ця оглядова робота аналізує проблеми, пов’язані з процесом злиття ПET з магнітно-резонансною томографією (МРТ) і розвитком даних систем протягом багатьох років. У роботі досліджуються діагностичні аспекти мультимодального зображення з корекцією ослаблення та методи реконструкції з метою отримання кількісного зображення. Розвиток ПЕТ/МРТ-візуалізації відіграє важливу роль у галузі нейрохірургії, онкології, опор-но-рухового апарату тощо, таким чином, може розглядатись як сучасний мультимодальний діагностичний метод додаткової гібридної візуалізації. Однак питання діагностичного застосування ПЕТ/МРТ-візуалізації і надалі потребує досліджень щодо інших можливих способів впровадження ПЕТ/МРТ для нових доклінічних і клінічних випробувань.

Purpose Selective internal radiation therapy (SIRT) is a treatment for various kinds of liver tumours by injecting 90 Y bearing microspheres into the liver vessels. To perform meaningful post-treatment dosimetry , quantitative imaging is performed. Methods This work uses a Monte-Carlo based reconstruction software with scatter and attenuation correction and collimator modelling that allows the quantification of 90 Y bremsstrahlung SPECT/CT data. A dataset comprising 17 patients and measurements on a Jaszczak phantom, a NEMA IEC Body phantom and an anthropomorphic liver phantom are analysed and activities and dose values are acquired. These measured values are compared with applied activities and pre-treatment calculations, allowing to assess the quality of the SPECT reconstruction. A detailed uncertainty budget is presented, including uncertainties of the dose calibrator, the count rate, non-included interactions and other factors. Results The applied method is validated by finding measurements repeatable within the given uncertainty, and it is shown the influence of various parameters on the reconstruction process is negligible. Furthermore, activities and doses measured in the phantoms show good agreement with calculated values, if they are corrected for partial volume effects . Conclusions The strict observation of metrological requirements and the creation of an uncertainty budget increase the reliability and traceability of this novel approach to 90Y dosimetry. It gives an example of successful voxel-based dosimetry based on quantitative 90Y SPECT/CT image data.

Purpose: Aim of this study was to evaluate the use of computer-aided design (CAD) models for attenuation correction (AC) of hardware components in positron emission tomography/magnetic resonance (PET/MR) imaging. Methods: The technical feasibility and quantitative impact of CAD-AC compared to computer tomography (CT)-based AC (reference) was investigated on a modular phantom consisting of 19 different material samples (plastics and metals arranged around a cylindrical emission phantom) typically used in phantoms, patient tables, and radiofrequency (RF) coils in PET/MR. The clinical applicability of the CAD-AC method was then evaluated on a 16-channel RF breast coil in a PET/MR patient study. The RF breast coil in this study was specifically designed PET compatible. Using this RF breast coil, the impact on clinical PET/MR breast imaging was systematically evaluated in breast phantom measurements and, furthermore, in n = 10 PET/MR patients with breast cancer. PET data were reconstructed three times: (1) no AC (NAC), (2) established CT-AC, and (3) CAD-AC. For both phantom measurements, a scan without attenuating hardware components (material probes or RF breast coil) was acquired serving as reference. Relative differences in PET data were calculated for all experiments. Results: In all phantom and patient measurements, significant gains in PET signal compared to NAC data were measurable with CT and CAD-AC. In initial phantom experiments, mean relative differences of –0.2% for CT-AC and 0.2% for CAD-AC were calculated compared to reference measurements without the material probes. The application to a RF breast coil depicts that CAD-AC results in significant gains compared to NAC data (10%) and a slight underestimation in PET signal of –1.3% in comparison to the no-coil reference measurement. In the patient study, a total of 15 congruent lesions in all 10 patients with a mean relative difference of 14% (CT and CAD-AC) in standardized uptake value compared to NAC data could be detected. Conclusions: To ensure best possible PET image quality and accurate PET quantification in PET/MR imaging, the AC of hardware components such as phantoms and RF coils is important. In initial phantom experiments and in clinical application to an RF breast coil, it was found that CAD-based AC results in significant gains in PET signal compared to NAC data and provides comparably good results to the established method of CT-based AC.

BACKGROUND Attenuation correction can improve the quantitative accuracy of single-photon emission computed tomography (SPECT) images. Existing SPECT-only systems normally can only provide non-attenuation corrected (NC) images which are susceptible to attenuation artifacts. In this work, we developed a post-reconstruction attenuation correction (PRAC) approach facilitated by a deep learning-based attenuation map for myocardial perfusion SPECT imaging. METHODS In the PRAC method, new projection data were estimated via forwardly projecting the scanner-generated NC image. Then an attenuation map, generated from NC image using a pretrained deep learning (DL) convolutional neural network, was incorporated into an offline reconstruction algorithm to obtain the attenuation-corrected images from the forwardly projected projections. We evaluated the PRAC method using 30 subjects with a DL network trained with 40 subjects, using the vendor-generated AC images and CT-based attenuation maps as the ground truth. RESULTS The PRAC methods using DL-generated and CT-based attenuation maps were both highly consistent with the scanner-generated AC image. The globally normalized mean absolute errors were 1.1% ± .6% and .7% ± .4% and the localized absolute percentage errors were 8.9% ± 13.4% and 7.8% ± 11.4% in the left ventricular (LV) blood pool, respectively, and - 1.3% ± 8.0% and - 3.8% ± 4.5% in the LV myocardium for PRAC methods using DL-generated and CT-based attenuation maps, respectively. The summed stress scores after PRAC using both attenuation maps were more consistent with the ground truth than those of the NC images. CONCLUSION We developed a PRAC approach facilitated by deep learning-based attenuation maps for SPECT myocardial perfusion imaging. It may be feasible for this approach to provide AC images for SPECT-only scanner data.

Since the clinical adoption of magnetic resonance (MR) in medical imaging, MR has proven to be a workhorse in diagnostic neuroradiology, with the ability to provide superb anatomic detail as well as additional functional and physiologic data, depending on the techniques utilized. Positron emission tomography/computed tomography has also shown irreplaceable diagnostic value in certain disease processes of the central nervous system by providing molecular and metabolic information through the development of numerous disease-specific PET tracers, many of which can be utilized as a diagnostic technique in and of themselves or can provide a valuable adjunct to information derived from MR. Despite these advances, many challenges still remain in neuroradiology, particularly in malignancy, neurodegenerative disease, epilepsy, and cerebrovascular disease. Through improvements in attenuation correction, motion correction, and PET detectors, combining the 2 modalities of PET and MR through simultaneous imaging has proven feasible and allows for improved spatial and temporal resolution without compromising either of the 2 individual modalities. The complementary information offered by both technologies has provided increased diagnostic accuracy in both research and many clinical applications in neuroradiology.

Objective To evaluate the impact of reconstruction parameters on the spatial resolution of the tomographic image in single photon emission computed tomography (SPECT)/computed tomography (CT), and compare spatial resolution between a new polyvalent whole-body Cadmium-Zinc-Telluride camera (CZT-SPECT/CT) and a conventional dual-head Anger camera (conventional SPECT/CT). Methods Spatial resolution was evaluated with four-line sources filled with 99mTc in tomographic images reconstructed by varying reconstruction parameters. Ordered-subset expectation maximization (OSEM) algorithm was performed with varying iterations (1-20), the number of subsets was fixed at 10. Butterworth filter, Gauss filter and no-filter were selected, respectively. Computed tomography-based attenuation correction (CTAC), scatter correction, resolution recovery and no correction (NC) were adopted for image correction. Filtered back projection (FBP) with Butterworth filter and CTAC was performed in image reconstruction. Spatial resolution was expressed by the full width at half-maximum (FWHM) value. Results The impact of reconstruction parameters on the spatial resolution was identical in both cameras: FWHM values decreased with the increase of iterations and converged uniformly when the number of iterations was over 4. FWHM values decreased with the increase of cutoff frequency of the Butterworth filter and increased with the increase of the Gauss filter. scatter correction and resolution recovery improved spatial resolution, whereas CTAC had a negligible effect on spatial resolution when reconstructed by OSEM. FWHM was generally lower with OSEM reconstruction than FBP reconstruction. On the whole, under the same reconstruction conditions, CZT-SPECT/CT had a lower FWHM value than conventional SPECT/CT. Conclusion The spatial resolution was improved with the increase of iterations. Increasing the cutoff frequency of the Butterworth filter and decreasing the Gauss filter enhanced spatial resolution. The spatial resolution was better reconstructed by OSEM associated with attenuation correction, scatter correction and resolution recovery than FBP. CZT-SPECT/CT had better spatial resolution than conventional SPECT/CT.

PURPOSE Positron emission tomography (PET) attenuation correction (AC) in positron emission tomography-magnetic resonance (PET/MR) scanners constitutes a critical and barely explored issue in spinal cord investigation, mainly due to the limitations in accounting for highly attenuating bone structures which surround the spinal canal. Our study aims at evaluating the clinical suitability of MR-driven AC (MRAC) for 18-fluorodeoxy-glucose positron emission tomography (18 F-FDG-PET) in spinal cord. METHODS Thirty-six patients, undergoing positron emission tomography-computed tomography (PET/CT) and PET/MR in the same session for oncological examination, were retrospectively analyzed. For each patient, raw PET data from PET/MR scanner were reconstructed with 4- and 5-class MRAC maps, generated by hybrid PET/MR system (PET_MRAC4 and PET_MRAC5, respectively, where PET_MRAC is PET images reconstructed using MR-based attenuation correction map), and an AC map derived from CT data after a custom co-registration pipeline (PET_rCTAC, where PET_rCTAC is PET images reconstructed using CT-based attenuation correction map), which served as reference. Mean PET standardized uptake values ( SUVm ) were extracted from the three reconstructed PET images by regions of interest (ROIs) identified on T2-weighted MRI, in the spinal cord, lumbar cerebrospinal fluid (CSF), and vertebral marrow at five levels (C2, C5, T6, T12, and L3). SUVm values from PET_MRAC4 and PET_MRAC5 were compared with each other and with the reference by means of paired t-test, and correlated using Pearson's correlation (r) to assess their consistency. Cohen's d was calculated to assess the magnitude of differences between PET images. RESULTS SUVm values from PET_MRAC4 were lower than those from PET_MRAC5 in almost all analyzed ROIs, with a mean difference ranging from 0.03 to 0.26 (statistically significant in the vertebral marrow at C2 and C5, spinal cord at T6 and T2, and CSF at L3). This was also confirmed by the effect size, with highest values at low spinal levels (d = 0.45 at T12 in spinal cord, d = 0.95 at L3 in CSF). SUVm values from PET_MRAC4 and PET_MRAC5 showed a very good correlation (0.81

Background Dual-gating reduces respiratory and cardiac motion effects but increases noise. With motion correction, motion is minimized and image quality preserved. We applied motion correction to create end-diastolic respiratory motion corrected images from dual-gated images. Methods [18F]-fluorodeoxyglucose ([18F]-FDG) PET images of 13 subjects were reconstructed with 4 methods: non-gated, dual-gated, motion corrected, and motion corrected with 4D-CT (MoCo-4D). Image quality was evaluated using standardized uptake values, contrast ratio, signal-to-noise ratio, coefficient of variation, and contrast-to-noise ratio. Motion minimization was evaluated using myocardial wall thickness. Results MoCo-4D showed improvement for contrast ratio (2.83 vs 2.76), signal-to-noise ratio (27.5 vs 20.3) and contrast-to-noise ratio (14.5 vs 11.1) compared to dual-gating. The uptake difference between MoCo-4D and non-gated images was non-significant (P > .05) for the myocardium (2.06 vs 2.15 g/mL), but significant (P < .05) for the blood pool (.80 vs .86 g/mL). Non-gated images had the lowest coefficient of variation (27.3%), with significant increase for all other methods (31.6-32.5%). MoCo-4D showed smallest myocardial wall thickness (16.6 mm) with significant decrease compared to non-gated images (20.9 mm). Conclusions End-diastolic respiratory motion correction and 4D-CT resulted in improved motion minimization and image quality over standard dual-gating.

A decade of PET/MRI clinical imaging has passed and many of the pitfalls are similar to those on earlier studies. However, techniques to overcome them have emerged and continue to develop. Although clinically significant lung nodules are demonstrable, smaller nodules may be detected using ultrashort/zero echo-time (TE) lung MRI. Fast reconstruction ultrashort TE sequences have also been used to achieve high-resolution lung MRI even with free-breathing. The introduction and improvement of time-of-flight scanners and increasing the axial length of the PET detector arrays have more than doubled the sensitivity of the PET part of the system. MRI for attenuation correction has provided many potential pitfalls, including misclassification of tissue classes based on MRI information for attenuation correction. Although the use of short echo times have helped to address these pitfalls, one of the most exciting developments has been the use of deep learning algorithms and computational neural networks to rapidly provide soft tissue, fat, bone and air information for the attenuation correction as a supplement to the attenuation correction information from fat-water imaging. Challenges with motion correction, particularly respiratory and cardiac remain but are being addressed with respiratory monitors and using PET data. In order to address truncation artefacts, the system manufacturers have developed methods to extend the MR field-of-view for the purpose of the attenuation and scatter corrections. General pitfalls like stitching of body sections for individual studies, optimum delivery of images for viewing and reporting, and resource implications for the sheer volume of data generated remain Methods to overcome these pitfalls serve as a strong foundation for the future of PET/MRI. Advances in the underlying technology with significant evolution in hard-ware and software and the exiting developments in use of deep learning algorithms and computational neural networks will drive the next decade of PET/MRI imaging.

A 62 years old woman 6 months after left total hip prosthesis referred to our institution for persistent pain and warm, stiff, and swollen joint. 18F-FDG CT/PET Images showed an intense focal uptake corresponding to the external margin of inter-trochanteric region of prosthesis and inside the stem inferiorly, but common decision was to reconstruct PET images without attenuation correction and now showed a complete and unexpected disappearance of focal and pathological FDG uptake. This case shows the potential propagation of CT artifacts into PET emission data close to metal implants and should be taken in account together to SUV values.

In the [123I]FP-CIT single-photon emission computed tomography (SPECT) examination, the specific binding ratio (SBR), calculated from the ratio of the striatal specific to extra-striatal background non-specific binding in the brain, is now commonly used as a quantitative index of parkinsonian syndrome. The purpose of this study was to examine the influence of count reduction on the SBR and to clarify the reliability of SBR values in patients with shorter scan times. A striatum phantom was used in a phantom study, with the radioactivity concentration adjusted so that the right striatum:left striatum:brain parenchyma ratio was 8:4:1. Changes in SBR values and image quality, expressed as the % coefficient of variation (%CV) and normalized mean squared error (NMSE), with decreasing acquisition counts were evaluated. In the clinical study, 106 patients (73.1 ± 9.6 years) with suspected parkinsonian syndrome underwent [123I]FP-CIT SPECT, and SBR values from normal 30 min acquisitions (fullSBR) and half-count acquisitions (halfSBR) were compared. SBR values were calculated using the Tossici-Bolt (SBRTB) and a fully automatic count-based (SBRcb) methods. In the phantom study, image quality decreased with a reduction of acquisition counts. The %CV and NMSE decreased by up to 52.5% and 81.5%, respectively. SBR values decreased slightly as acquisition counts decreased. In the clinical study, the mean values of halfSBR were lower than those of fullSBR, and they were significantly different except for SBRTB without attenuation correction. halfSBR and fullSBR values correlated well, with halfSBR values 1–8% lower than fullSBR. The accuracy of diagnosis did not decrease even after acquisition counts were reduced by half. This study demonstrated that SBR values decrease as a function of reduced acquisition counts. Since halfSBR and fullSBR showed excellent correlation, it is suggested that fullSBR can be estimated from halfSBR using a calibration formula when scan times are reduced.

Background To evaluate quantitative myocardial perfusion SPECT/CT datasets for routine clinical reporting and the assessment of myocardial tracer uptake in patients with severe TVCAD. Methods MPS scans were reconstructed as quantitative SPECT datasets using CTs from internal (SPECT/CT, Q_INT) and external (PET/CT, Q_EXT) sources for attenuation correction. TPD was calculated and compared to the TPD from non-quantitative SPECT datasets of the same patients. SUVmax, SUVpeak, and SUVmean were compared between Q_INT and Q_EXT SPECT datasets. Global SUVmax and SUVpeak were compared between patients with and without TVCAD. Results Quantitative reconstruction was feasible. TPD showed an excellent correlation between quantitative and non-quantitative SPECT datasets. SUVmax, SUVpeak, and SUVmean showed an excellent correlation between Q_INT and Q_EXT SPECT datasets, though mean SUVmean differed significantly between the two groups. Global SUVmax and SUVpeak were significantly reduced in patients with TVCAD. Conclusions Absolute quantification of myocardial tracer uptake is feasible. The method seems to be robust and principally suitable for routine clinical reporting. Quantitative SPECT might become a valuable tool for the assessment of severe coronary artery disease in a setting of balanced ischemia, where potentially life-threatening conditions might otherwise go undetected.

1027 Introduction: The long-term exposure to inappropriately excessive production of cortisol from adrenal glands leads to Cushing9s syndrome (CS). The diagnosis is dependent on hypercortisolism and its ACTH dependency. ACTH levels above 20 pg/ml suggest that the hypercortisolism is ACTH dependent. It is also important to distinguish Cushing9s disease from ectopic Cushing9s syndrome followed by anatomical as well as functional localization of the tumor in the sella. Therefore, a non-invasive integrated functional plus anatomical imaging for delineating corticotropinoma is needed. Desmopressin is potent and specific for tumorous corticotrophs. However, conjugation of DOTA with native desmopressin is not possible. A modified desmopressin molecule that can conjugate with DOTA and demonstrated its binding efficiency for corticotropinoma was designed in-silico and custom synthesized for PET imaging. Methods: The radiolabelling of DOTA conjugated modified Desmopressin (mDesmo) was performed with Ga-68 chloride eluted from Ge-68/Ga-68 generator. For standardization of radiolabelling parameters, various combinations of different reaction parameters such as pH (3-5.5), amount of peptide (5-30 µg), concentration and volume of sodium acetate buffer corresponding to Ga-68 chloride eluted with 4 mL of 0.05M HCl, incubation time (5-20 min) and temperature (25°C, 37°C and 95° C) have been evaluated. The quality control (QC) tests including radionuclide purity, radiochemical, chemical purity, stability, sterility and pyrogenicity were performed. After obtaining clearance from Institutional Ethics Committee, A pilot clinical study was also performed.Patients (n=5) with different pathology were recruited and subjected to serial brain PET image acquisition initiated immediately post intravenous injection of 77.7-122.1 MBq Ga-68-mDesmo on bed. Serial PET images up to 60 min followed by whole-body PET/CT images were acquired on a dedicated hybrid PET/CT scanner. CT based attenuation correction was used for PET images. PET and CT images were reconstructed by using Iterative methods ordered subset expectation maximisation and Filter back projection respectively. A ROI was drawn around the abnormal uptake of Ga-68-mDesmo and represented as SUVmax. Results: The standardized parameters were 25 mCi of 68Ga, 20 µg of mDesmo at 95oC for 10 min, pH 4.5 and 0.025 M sodium acetate buffer. High radiolabeling yield (>99%) was obtained when reaction solution was purified using C18 cartridge. The residual ethanol content, endotoxins were with in permissible limits, found sterile on culture test and showed high human serum stability at 37°C.MIP images of Ga-68 m Desmo showed decreased blood pool and background activity at 1h. Physiological uptake was not observed in any organ of the body including brain and pituitary. Only kidneys, ureter and bladder were visualized indicating excretion of tracer through kidneys. Peak tracer concentration in pituitary adenoma was noted at 45-60 min. Conclusions: Ga-68 mDesmo is a promising tracer for non-invasive molecular imaging for delineating corticotropinoma.

Positron emission tomography and magnetic resonance imaging (PET/MRI) scanners cannot be qualified in the manner adopted for hybrid PET and computed tomography (CT) devices. The main hurdle with qualification in PET/MRI is that attenuation correction (AC) cannot be adequately measured in conventional PET phantoms due to the difficulty in converting the MRI images of the physical structures (e.g., plastic) into electron density maps. Over the last decade, a plethora of novel MR-based algorithms have been developed to more accurately derive the attenuation properties of the human head, including the skull. Although very promising, none of these techniques has yet emerged as an optimal and universally adopted strategy for AC in PET/MRI. In this work, we propose a path for PET/MRI qualification for multicenter brain imaging studies. Specifically, our solution is to separate the head attenuation correction from the other factors that affect PET data quantification and use a patient as a phantom to assess the former. The emission data collected on the integrated PET/MRI scanner to be qualified should be reconstructed using both MR- and CT-based AC methods and whole-brain qualitative and quantitative (both voxel-wise and regional) analyses should be performed. The MR-based approach will be considered satisfactory if the PET quantification bias is within the acceptance criteria specified herein. We have implemented this approach successfully across two PET/MRI scanner manufacturers at two sites.

Attenuation correction remains a challenge in pelvic PET/MRI. In addition to the segmentation/model-based approaches, deep learning methods have shown promise in synthesizing accurate pelvic attenuation maps (μ-maps). However, these methods often misclassify air pockets in the digestive tract, potentially introducing bias in the reconstructed PET images. The aims of this work were to develop deep learning–based methods to automatically segment air pockets and generate pseudo-CT images from CAIPIRINHA-accelerated MR Dixon images. Methods: A convolutional neural network (CNN) was trained to segment air pockets using 3-dimensional CAIPIRINHA-accelerated MR Dixon datasets from 35 subjects and was evaluated against semiautomated segmentations. A separate CNN was trained to synthesize pseudo-CT μ-maps from the Dixon images. Its accuracy was evaluated by comparing the deep learning–, model-, and CT-based μ-maps using data from 30 of the subjects. Finally, the impact of different μ-maps and air pocket segmentation methods on the PET quantification was investigated. Results: Air pockets segmented using the CNN agreed well with semiautomated segmentations, with a mean Dice similarity coefficient of 0.75. The volumetric similarity score between 2 segmentations was 0.85 ± 0.14. The mean absolute relative changes with respect to the CT-based μ-maps were 2.6% and 5.1% in the whole pelvis for the deep learning–based and model-based μ-maps, respectively. The average relative change between PET images reconstructed with deep learning–based and CT-based μ-maps was 2.6%. Conclusion: We developed a deep learning–based method to automatically segment air pockets from CAIPIRINHA-accelerated Dixon images, with accuracy comparable to that of semiautomatic segmentations. The μ-maps synthesized using a deep learning–based method from CAIPIRINHA-accelerated Dixon images were more accurate than those generated with the model-based approach available on integrated PET/MRI scanners.

PURPOSE 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). METHODS 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. RESULTS U-NetAC had the highest visual evaluation score. The NMSE results for the U-NetAC were the lowest, followed by AutoencoderAC and ChangAC (P

PURPOSE Conventional 99m Tc-macroaggregated albumin (99m Tc-MAA) planar scintigraphy overestimates lung shunt fraction (LSF) compared to SPECT/CT. However, the respiratory motion artifact due to the temporal mismatch between static SPECT and helical CT (HCT) may compromise the SPECT quantitation accuracy by incorrect attenuation correction (AC) and volume-of-interest (VOI) segmentation. This study aims to evaluate AC and VOI segmentation effects systematically and to propose a CT map for LSF and tumor-to-normal liver ratio (TNR) estimation in static 99m Tc-MAA SPECT/CT. METHODS The 4D XCAT phantom was used to simulate a phantom population of 120 phantoms, modeling 10 different anatomical variations, nine TNRs (2-13.2), nine tumor sizes (2-6.7 cm diameter), eight tumor locations, three axial motion amplitudes of 1, 1.5, and 2 (cm), and four LSFs of 5%, 10%, 15%, and 20%. An analytical projector for low-energy high-resolution parallel-hole collimator was used to simulate 60 noisy projections over 360°, modeling attenuation and geometric collimator-detector response (GCDR). AC and VOI mismatch effects were investigated independently and together, using cine average CT (CACT), HCT at end-inspiration (HCT-IN), mid-respiration (HCT-MID), and end-expiration (HCT-EX) respectively as attenuation and segmentation maps. SPECT images without motion, AC, and VOI errors were also generated as reference. LSF and TNR errors were measured as compared to the ground truth. RESULTS HCT-MID has slightly better performance for AC effect compared with other CT maps in LSF and TNR estimation, while HCT-EX and HCT-MID perform better for VOI effect. For a respiratory motion amplitude of 1.5 cm and a LSF of 5%, the LSF errors are 19.56 ± 4.58%, -6.79 ± 1.74%, 77.29 ± 14.74%, and 111.25 ± 18.29% corresponding to HCT-MID, HCT-EX, HCT-IN, and CACT in static SPECT. The TNR errors are -12.38 ± 6.42%, -20.55 ± 11.25%, -20.89 ± 9.98%, and -22.89 ± 14.38% respectively. HCT-MID has the best performance for LSF estimation for LSF > 10% and TNR estimation, followed by HCT-EX, HCT-IN, and CACT. CONCLUSIONS The HCT-MID is recommended for AC and segmentation to alleviate respiratory artifacts and improve quantitation accuracy in 90 Y radioembolization treatment planning. HCT-EX would also be a recommended choice if HCT-MID is not available.

Attenuation correction (AC) and scatter correction (SC) are essential steps for accurate PET quantification in positron emission tomography (PET)/computed tomography (CT) and PET/magnetic resonance (MR) imaging. SC with a single scatter simulation (SSS) algorithm is usually performed by employing an attenuation map using CT and MR data for AC. The purpose of this study was to evaluate the effect of SC using a SSS algorithm with CT- and MR-based attenuation maps in phantom and volunteer studies for 18F-FDG brain PET. We investigated the effect of the SC on two MR-based attenuation maps, which included a four-segmentation Dixon (soft tissue, fat, lung, and air) and a five-segmentation Model, which is Dixon plus bone, and we compared those maps with a standard CT-based attenuation map. In the phantom study, the difference in (%) recovery coefficients before and after SC for a CT-based attenuation correction (CTAC) and a MR-based attenuation correction (MRAC) were 20.36 and 21.01, respectively. The difference in the scatter fractions on the sinogram was about 2% between the CTAC and the MRAC (Dixon). In the volunteer study, the scatter fractions were 24.08 for the CT, 23.09 for the Dixon, and 23.11 for the Model. The bias in the scatter ratio image, after calculating the before and after SCs from the PET images, between the CTAC and the two MRACs was less than 1% (% MAE = 0.43 for CT-Dixon and 0.39 for CT-Model). A voxel-wise analysis of the scatter ratio before and after the SC from PET images showed no significant differences between the CTAC and the two MRACs (p > 0.05). We found that despite the different attenuation maps, the difference in the effects of the SC between the two MR and the CT attenuation maps was minimal. This implies that the SC obtained using the SSS algorithm was rarely affected by the attenuation map.

Attenuation correction (AC) using CT transmission scanning enables the accurate quantitative analysis of dedicated cardiac SPECT. However, AC is challenging for SPECT-only scanners. We developed a deep learning-based approach to generate synthetic AC images from SPECT images without AC. CT-free AC was implemented using our customized Dual Squeeze-and-Excitation Residual Dense Network (DuRDN). 172 anonymized clinical hybrid SPECT/CT stress/rest myocardial perfusion studies were used in training, validation, and testing. Additional body mass index (BMI), gender, and scatter-window information were encoded as channel-wise input to further improve the network performance. Quantitative and qualitative analysis based on image voxels and 17-segment polar map showed the potential of our approach to generate consistent SPECT AC images. Our customized DuRDN showed superior performance to conventional network design such as U-Net. The averaged voxel-wise normalized mean square error (NMSE) between the predicted AC images by DuRDN and the ground-truth AC images was 2.01 ± 1.01%, as compared to 2.23 ± 1.20% by U-Net. Our customized DuRDN facilitates dedicated cardiac SPECT AC without CT scanning. DuRDN can efficiently incorporate additional patient information and may achieve better performance compared to conventional U-Net.

Background Quantification of myocardial blood flow (MBF) with PET requires accurate attenuation correction, which is performed using a separate CT. Misalignment between PET and CT scans has been reported to be a common problem. The purpose of the present study was to assess the effect of PET CT misalignment on the quantitative accuracy of cardiac 15O-water PET. Methods Ten clinical patients referred for evaluation of ischemia and assessment of MBF with 15O-water were included in the study. Eleven different misalignments between PET and CT were induced in 6 different directions with 10 and 20 mm amplitudes: caudal (+Z), cranial (− Z), lateral (±X), anterior (+Y), and anterior combined with cranial (+ Y and − Z). Blood flow was quantified from rates of washout (MBF) and uptake (transmural MBF, MBFt) for the whole left ventricle and the three coronary territories. The results from all misalignments were compared to the original scan without misalignment. Results MBF was only minorly affected by misalignments, but larger effects were seen in MBFt. On the global level, average absolute deviation across all misalignments for MBF was 1.7% ± 1.4% and for MBFt 5.4% ± 3.2 Largest deviation for MBF was − 4.8% ± 5.8% (LCX, X + 20) and for MBFt − 19.3% ± 9.6% (LCX, X + 20). In general, larger effects were seen in LAD and LCX compared to in RCA. Conclusion The quantitative accuracy of MBF from 15O-water PET, based on the washout of the tracer, is only to a minor extent affected by misalignment between PET and CT.

Attenuation correction (AC) is crucial for ensuring the quantitative accuracy of positron emission tomography (PET) imaging. However, obtaining accurate μ-maps from brain-dedicated PET scanners without AC acquisition mechanism is challenging. Therefore, to overcome these problems, we developed a deep learning-based PET AC (deep AC) framework to synthesize transmission computed tomography (TCT) images from non-AC (NAC) PET images using a convolutional neural network (CNN) with a huge dataset of various radiotracers for brain PET imaging. The proposed framework is comprised of three steps: (1) NAC PET image generation, (2) synthetic TCT generation using CNN, and (3) PET image reconstruction. We trained the CNN by combining the mixed image dataset of six radiotracers to avoid overfitting, including [18F]FDG, [18F]BCPP-EF, [11C]Racropride, [11C]PIB, [11C]DPA-713, and [11C]PBB3. We used 1261 brain NAC PET and TCT images (1091 for training and 70 for testing). We did not include [11C]Methionine subjects in the training dataset, but included them in the testing dataset. The image quality of the synthetic TCT images obtained using the CNN trained on the mixed dataset of six radiotracers was superior to those obtained using the CNN trained on the split dataset generated from each radiotracer. In the [18F]FDG study, the mean relative PET biases of the emission-segmented AC (ESAC) and deep AC were 8.46 ± 5.24 and − 5.69 ± 4.97, respectively. The deep AC PET and TCT AC PET images exhibited excellent correlation for all seven radiotracers (R2 = 0.912–0.982). These results indicate that our proposed deep AC framework can be leveraged to provide quantitatively superior PET images when using the CNN trained on the mixed dataset of PET tracers than when using the CNN trained on the split dataset which means specific for each tracer.

Purpose: To investigate within phantoms the minimum CT dose allowed for accurate attenuation correction of PET data and to quantify the effective dose reduction when a CT for this purpose is incorporated in the clinical setting. Methods: The NEMA image quality phantom was scanned within a large parallelepiped container. Twenty-one different CT images were acquired to correct attenuation of PET raw data. Radiation dose and image quality were evaluated. Thirty-one patients with proven multiple myeloma who underwent a dual tracer PET/CT scan were retrospec- tively reviewed. 18F-fluorodeoxyglucose PET/CT included a diagnostic whole-body low dose CT (WBLDCT: 120 kV-80mAs) and 11C-Methionine PET/CT included a whole-body ultra-low dose CT (WBULDCT) for attenuation correction (100 kV-40mAs). Effective dose and image quality were analysed. Results: Only the two lowest radiation dose conditions (80 kV-20mAs and 80 kV-10mAs) produced artifacts in CT images that degraded corrected PET images. For all the other conditions (CTDIvol ≥ 0.43 mGy), PET contrast recovery coefficients varied less than ± 1.2%. Patients received a median dose of 6.4 mSv from diagnostic CT and 2.1 mSv from the attenuation correction CT. Despite the worse image quality of this CT, 94.8% of bone lesions were identifiable. Conclusion: Phantom experiments showed that an ultra-low dose CT can be implemented in PET/CT procedures without any noticeable degradation in the attenuation corrected PET scan. The replacement of the standard CT for this ultra-low dose CT in clinical PET/CT scans involves a significant radiation dose reduction

Attenuation correction (AC) is important for the quantitative merits of positron emission tomography (PET). However, attenuation coefficients cannot be derived from magnetic resonance (MR) images directly for PET/MR systems. In this work, we aimed to derive continuous AC maps from Dixon MR images without the requirement of MR and computed tomography (CT) image registration. To achieve this, a 3-D generative adversarial network with both discriminative and cycle-consistency loss (Cycle-GAN) was developed. The modified 3-D U-net was employed as the structure of the generative networks to generate the pseudo-CT/MR images. The 3-D patch-based discriminative networks were used to distinguish the generated pseudo-CT/MR images from the true CT/MR images. To evaluate its performance, datasets from 32 patients were used in the experiment. The Dixon segmentation and atlas methods provided by the vendor and the convolutional neural network (CNN) method which utilized registered MR and CT images were employed as the reference methods. Dice coefficients of the pseudo-CT images and the regional quantification in the reconstructed PET images were compared. Results show that the Cycle-GAN framework can generate better AC compared to the Dixon segmentation and atlas methods, and shows comparable performance compared to the CNN method.

For photon attenuation correction, current positron emission tomography systems combined with magnetic resonance imaging (PET/MR) imaging systems typically use methods based on MR image segmentation with subsequent assignment of empirical attenuation coefficients in PET image reconstruction. Delineation of bone in MR images has been challenging, especially in the head and neck areas, due to the difficulty of separating bone from air. In this article, we study deep learning techniques that assist the MR-based attenuation correction (MRAC) process for PET/MR systems, with focus on the brain region. We use a generative adversarial network (GAN) with residual blocks in a conditional setting for this task. We studied the performance of the designed network on image translation and segmentation tasks, which are essential for MRAC. For both tasks, the network generates pseudo CT images that resemble real CT images with normalized pixel value difference of around 5% and structural similarity (SSIM) index of around 0.8.

Attenuation correction (AC) is essential for the generation of artifact-free and quantitatively accurate positron emission tomography (PET) images. PET AC based on computed tomography (CT) frequently results in artifacts in attenuation-corrected PET images, and these artifacts mainly originate from CT artifacts and PET-CT mismatches. The AC in PET combined with a magnetic resonance imaging (MRI) scanner (PET/MRI) is more complex than PET/CT, given that MR images do not provide direct information on high-energy photon attenuation. Deep-learning (DL)-based methods for the improvement of PET AC have received significant research attention as alternatives to conventional AC methods. Many DL studies were focused on the transformation of MR images into synthetic pseudo-CT or attenuation maps. Alternative approaches that are not dependent on the anatomical images (CT or MRI) can overcome the limitations related to current CT- and MRI-based ACs and allow for more accurate PET quantification in stand-alone PET scanners for the realization of low radiation doses. In this article, a review is presented on the limitations of the PET AC in current dual-modality PET/CT and PET/MRI scanners, in addition to the current status and progress of DL-based approaches, for the realization of improved performance of PET AC.

Background Accurate quantification of radioactivity, measured by an integrated positron emission tomography (PET) and magnetic resonance imaging (MRI) system, is still a challenge. One aspect of such a challenge is to correct for the hardware attenuation, such as the patient table and radio frequency (RF) resonators. For PET/MRI systems, computed tomography (CT) is commonly used to produce hardware attenuation correction (AC) maps, by converting Hounsfield units (HU) to a linear attenuation coefficients (LAC) map at the PET energy level 511 keV, using a bilinear model. The model does not address beam hardening, nor higher density materials, which can lead to inaccurate corrections. Purpose In this study, we introduce a transmission-based (TX-based) AC technique with a static Germanium-68 (Ge-68) transmission source to generate hardware AC maps using the PET/MRI system itself, without the need for PET or medical CT scanners. The AC TX-based maps were generated for a homogeneous cylinder, made of acrylic as a validator. The technique thereafter was applied to the patient table and posterior part of an RF-phased array used in cardiovascular PET/MRI imaging. The proposed TX-based, and the CT-based, hardware maps were used in reconstructing PET images of one cardiac patient, and the results were analysed and compared. Results The LAC derived by the TX-based method for the acrylic cylinder is estimated to be 0.10851 ± 0.00380 cm−1 compared to the 0.10698 ± 0.00321 cm−1 theoretical value reported in the literature. The PET photon counts were reduced by 8.7 ± 1.1% with the patient table, at the region used in cardiac scans, while the CT-based map, used for correction, over-estimated counts by 4.3 ± 1.3%. Reconstructed in vivo images using TX-based AC hardware maps have shown 4.1 ± 0.9% mean difference compared to those reconstructed images using CT-based AC. Conclusions The LAC of the acrylic cylinder measurements using the TX-based technique was in agreement with those in the literature confirming the validity of the technique. The over-estimation of photon counts caused by the CT-based model used for the patient table was improved by the TX-based technique. Therefore, TX-based AC of hardware using the PET/MRI system itself is possible and can produce more accurate images when compared to the CT-based hardware AC in cardiac PET images.

Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant cycle-consistency model that can filter out these domain-specific deformation. The deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods., Highlights • Novel DiCyc architecture filters out domain-specific deformations. • The deformable layers are modified for less parameters and faster convergence. • Expectation–maximization training with 2-forward-1-backward back propagation. • New cycle-consistency and image alignment losses with less conflicted gradients. • An ablation study visualizes the trade-off between image alignment and quality.

Objective: Attenuation has a significant influence on data and consequently on image quality. Attenuation correction corrects the weakening of the gamma photons in various depths. Non-diagnostic, low-dosage CT is usually used for attenuation correction when images are taken with a SPECT/CT. The purpose of the study was to determine the influence of attenuation correction in SPECT/CT on image quality in NEMA body phantom analysis in different background/sphere ratios. Material and Methods: The NEMA IEC Body Phantom was filled with isotope technetium-99m (99mTc), with a different ratio between the phantom background and spheres. The images were reconstructed using filtered back projection (FBP), non-corrected iterative reconstruction (IR), and iterative reconstruction using computer tomography for attenuation correction (CT-AC). The average number of counts in the background and in all six spheres was measured. This was followed by a comparison of the contrast in images that were reconstructed using different methods. Results: The average number of counts in sphere increased as we increased the activity concentration ratio between the background and sphere. Statistical analysis showed that contrast is significantly divergent between different methods of reconstruction. Conclusion: The use of iterative reconstruction with CT-AC improves the contrast and image quality compared to iterative reconstruction and FBP.

Measurement of cerebral blood flow (CBF), cerebral blood volume (CBV), cerebral oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2) by PET with oxygen-15 labeled gases is useful for diagnosis and treatment planning in cases of chronic occlusive cerebrovascular disease. In the present study, CBF, CBV, OEF and CMRO2 were measured using the integrated design of PET/MRI scanner system. This is a first attempt to measure cerebral perfusion and oxygen metabolism using PET/MRI with oxygen-15 labeled gases. PET/MRI measurements with the steady-state method of oxygen-15 labeled gases, carbon monoxide (C15O), oxygen (15O2), and carbon dioxide (C15O2) were performed on nine healthy men. Two kinds of attenuation correction for PET were performed using MRI with Dixon sequence (DIXON) and Dixon sequence with model-based bone segmentation (DIXONbone). A real-time motion correction of PET images was also performed using simultaneously measured MR images to detect head motion. Mean and SD values of CBF, CBV, OEF, and CMRO2 in the cerebral cortices with attenuation correction by DIXON were 31 ± 4 mL/100 mL/min, 2.7 ± 0.2 mL/mL, 0.40 ± 0.07, and 2.5 ± 0.3 mL/100 mL/min without real-time motion correction, and 33 ± 4 mL/100 mL/min, 2.7 ± 0.2 mL/mL, 0.40 ± 0.07, and 2.6 ± 0.3 mL/100 mL/min with real-time motion correction, respectively. Values with of CBF, CBV, OEF, and CMRO2 with attenuation correction by DIXONbone were 35 ± 5 mL/100 mL/min, 2.8 ± 0.2 mL/mL, 0.40 ± 0.07, and 2.8 ± 0.3 mL/100 mL/min without real-time motion correction, and 38 ± 5 mL/100 mL/min, 2.8 ± 0.2 mL/mL, 0.40 ± 0.07, and 3.0 ± 0.4 mL/100 mL/min with real-time motion correction, respectively. Using PET/MRI with oxygen-15 labeled gases, CBF, CBV, OEF, and CMRO2 could be measured. Values of CBF, CBV, and CMRO2 measured with attenuation correction by DIXON were significantly lower than those measured with correction by DIXONbone. One of the reasons for this is that attenuation correction of DIXON does not take into consideration of the photon absorption by bone. OEF values, corresponding to ratios of CMRO2 to CBF, were not affected by attenuation correction methods. Values of CBF and CMRO2 with a real-time motion correction were significantly higher than those without correction. Using PET/MRI with adequate corrections, similar values of CBF, CBV, OEF, and CMRO2 as PET alone scanner system reported previously were obtained. The UMIN clinical trial number: UMIN000033382.

Background Cardiac PET has recently found novel applications in coronary atherosclerosis imaging using [18F]NaF as a radiotracer, highlighting vulnerable plaques. However, the resulting uptakes are relatively small, and cardiac motion and respiration-induced movement of the heart can impair the reconstructed images due to motion blurring and attenuation correction mismatches. This study aimed to apply an MR-based motion compensation framework to [18F]NaF data yielding high-resolution motion-compensated PET and MR images. Methods Free-breathing 3-dimensional Dixon MR data were acquired, retrospectively binned into multiple respiratory and cardiac motion states, and split into fat and water fraction using a model-based reconstruction framework. From the dynamic MR reconstructions, both a non-rigid cardiorespiratory motion model and a motion-resolved attenuation map were generated and applied to the PET data to improve image quality. The approach was tested in 10 patients and focal tracer hotspots were evaluated concerning their target-to-background ratio, contrast-to-background ratio, and their diameter. Results MR-based motion models were successfully applied to compensate for physiological motion in both PET and MR. Target-to-background ratios of identified plaques improved by 7 ± 7%, contrast-to-background ratios by 26 ± 38%, and the plaque diameter decreased by −22 ± 18%. MR-based dynamic attenuation correction strongly reduced attenuation correction artefacts and was not affected by stent-related signal voids in the underlying MR reconstructions. Conclusions The MR-based motion correction framework presented here can improve the target-to-background, contrast-to-background, and width of focal tracer hotspots in the coronary system. The dynamic attenuation correction could effectively mitigate the risk of attenuation correction artefacts in the coronaries at the lung-soft tissue boundary. In combination, this could enable a more reproducible and reliable plaque localisation.

The bone cannot be evaluated using magnetic resonance attenuation correction (MRAC) with the Dixon sequence. To solve this issue, the present study aimed to evaluate model-based AC for whole-body 2-[fluorine-18]-fluoro-2-deoxy-d-glucose (18F-FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI) by creating bone segmentation. We analyzed and evaluated the data of 31 consecutive patients. The Biograph mMR (Siemens Healthcare) was used for clinical whole-body 18F-FDG PET/MRI with the conventional MRAC method, and OSIRIX MD software was used to analyze the images. After the examination, the new model-based post-processing MRAC was applied to create μ-maps with bone segmentation, and retrospective PET reconstruction was performed using this μ-map. The bone structures of all patients created using model-based MRAC were visually evaluated. Standard uptake values (SUVs) at 11 anatomical positions in PET images, corrected using the μ-map with and without bone segmentation, were measured and compared. The model-based post-processing MRAC was run for all patients, without errors. Visual evaluation revealed that the model-based post-processing MRAC exhibited poor results for six patients. Furthermore, it exhibited an increasing trend of SUV in the brain compared to the conventional method. Locations other than the brain indicated a similar or decreasing trend. The two methods showed a good linear correlation for all patients. However, patients aged

In this paper, we suggest how to handle the issue of the heteroskedasticity of measurement errors when specifying dynamic models for the conditional expectation of realized variance. We show that either adding a GARCH correction within an asymmetric extension of the HAR class ( AHAR - GARCH ), or working within the class of asymmetric multiplicative error models ( AMEM ) greatly reduces the need for quarticity/quadratic terms to capture attenuation bias. This feature in AMEM can be strengthened by considering regime specific dynamics. Model Confidence Sets confirm this robustness both in- and out-of-sample for a panel of 28 big caps and the S&P500 index.

Purpose This study aimed to verify whether cold artifacts caused by the gap state between attenuation correction computed tomography (ACCT) and positron emission tomography (PET) data (so-called hot-in-air (HIA) state) in body trunk PET/computer tomography (CT) examinations can be improved by the Absolute-single scatter simulation (SSS), which is a scatter correction method in a phantom experiment using the high-accumulation syringe of out-of-body phantom. Method PET imaging profile curves in the HIA state were evaluated using a high-accumulation syringe that simulated a urinary tract pouch encapsulated with 18F-FDG solution. The hot syringe-to-background ratio (HBR) of the syringe was changed to 5, 7, and 10. Moreover, PET image quality evaluation of the HIA state was performed with a syringe placed on the top of a NEMA IEC body phantom. Six spheres (10-37 mm in diameter) were placed inside the phantom and filled with 18F-FDG solution with a sphere-to-background ratio of 4. The evaluation items of image quality were N10 mm, QH, 10 mm / N10 mm, and recovery coefficient (RC). Result The image quality tended to deteriorate as the HBR of the syringe increased in the relative-SSS, while the effect was small in the Absolute-SSS and the lowest at HBR 10. The RC10 mm of HBR 5 was 0.33 for the Relative-SSS, which was below the criterion for the Relative-SSS, but was 0.5 for the Absolute-SSS, which met the criterion. Conclusion Absolute-SSS significantly improved cold artifacts caused by HIA states on body trunk PET/CT examinations, suggesting that it is highly useful both visually and quantitatively.

PURPOSE The purpose of this study was to evaluate the best phantom for calculating the becquerel calibration factor (BCF) and correction method to obtain the improvement of standardized uptake value (SUV) accuracy in both single photon emission computed tomography (SPECT) and SPECT/CT. METHOD A SPECT/CT scanner was used in this study. BCFs were calculated using four phantoms with different cross sections including National Electrical Manufacturers Association International Electrotechnical Commission body phantom (NEMA IEC body phantom) filled with 99mTcO4-, and five correction methods were used for reconstruction. SUVs were calculated by the NEMA IEC body phantom and pediatric phantom in house with these BCFs. We then measured SUVmean in the background region of the NEMA IEC body phantom, SUVmax and SUVpeak of the 37-mm-diameter sphere. RESULTS In the SPECT scanner, SUVmean and SUVmax measured 1.04 and 4.02, respectively, in the case of BCF calculation and SUV measurement using NEMA IEC body phantoms without corrections. In the SPECT/CT scanner, SUVmean with CT attenuation correction (AC) was in agreement with the theoretical values using each phantom. SUVmax showed the same trend. CONCLUSION In the SPECT scanner, it is possible to obtain a highly accurate SUV by using a phantom that matches the size of the subject for BCF calculation and without correction. In the SPECT/CT scanner, highly accurate SUVs can be obtained by using CT-based attenuation correction, and these values do not depend on the size of the BCF calculation phantom.

Computed tomography (CT) provides information for diagnosis, PET attenuation correction (AC), and radiation treatment planning (RTP). Disadvantages of CT include poor soft tissue contrast and exposure to ionizing radiation. While MRI can overcome these disadvantages, it lacks the photon absorption information needed for PET AC and RTP. Thus, an intelligent transformation from MR to CT, i.e., the MR-based synthetic CT generation, is of great interest as it would support PET/MR AC and MR-only RTP. Using an MR pulse sequence that combines ultra-short echo time (UTE) and modified Dixon (mDixon), we propose a novel method for synthetic CT generation jointly leveraging prior knowledge as well as partial supervision (SCT-PK-PS for short) on large-field-of-view images that span abdomen and pelvis. Two key machine learning techniques, i.e., the knowledge-leveraged transfer fuzzy c-means (KL-TFCM) and the Laplacian support vector machine (LapSVM), are used in SCT-PK-PS. The significance of our effort is threefold: 1) Using the prior knowledge-referenced KL-TFCM clustering, SCT-PK-PS is able to group the feature data of MR images into five initial clusters of fat, soft tissue, air, bone, and bone marrow. Via these initial partitions, clusters needing to be refined are observed and for each of them a few additionally labeled examples are given as the partial supervision for the subsequent semi-supervised classification using LapSVM; 2) Partial supervision is usually insufficient for conventional algorithms to learn the insightful classifier. Instead, exploiting not only the given supervision but also the manifold structure embedded primarily in numerous unlabeled data, LapSVM is capable of training multiple desired tissue-recognizers; 3) Benefiting from the joint use of KL-TFCM and LapSVM, and assisted by the edge detector filter based feature extraction, the proposed SCT-PK-PS method features good recognition accuracy of tissue types, which ultimately facilitates the good transformation from MR images to CT images of the abdomen-pelvis. Applying the method on twenty subjects' feature data of UTE-mDixon MR images, the average score of the mean absolute prediction deviation (MAPD) of all subjects is 140.72 ± 30.60 HU which is statistically significantly better than the 241.36 ± 21.79 HU obtained using the all-water method, the 262.77 ± 42.22 HU obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method, and the 197.05 ± 76.53 HU obtained via the conventional SVM method. These results demonstrate the effectiveness of our method for the intelligent transformation from MR to CT on the body section of abdomen-pelvis.

Positron emission tomography (PET) is a nuclear medicine examination involving the injection of radiotracers that provide a functional assessment of biochemical, physiologic, and pathophysiologic processes. PET radiotracers contain radioactive elements that undergo positron decay and subsequent annihilation with nearby electrons to produce two 511-keV photons traveling in opposite directions at 180 degrees from each other. Modern PET cameras implementing coincidence detection with the help of computer software use this opposite photon trajectory to reconstruct a spatial distribution. Computed tomography (CT) complements the examination to provide anatomic correlation and attenuation correction. Hybrid or fusion PET-CT imaging has become a crucial imaging tool utilized for detection, staging, and treatment follow-up of various benign and malignant conditions. F18-fludeoxyglucose (F18-FDG) is the most commonly used PET radiotracer. A glucose analog, F18-FDG capitalizes on the known propensity for malignant cells to overutilize glucose by favoring glycolytic metabolism.1 Otto Warburg discovered this principle in the 1930s, and it is known as the Warburg effect.2 The predisposition for malignant tumors to demonstrate hypermetabolism of glucose provides the basis for F18-FDG PET cancer imaging. While highly sensitive and versatile for imaging a variety of solid and hematologic tumors, F18-FDG has certain limitations, including lack of target specificity because of the ubiquitous utilization of glucose in the body and poor sensitivity for tumors with more indolent behavior and lower utilization of glucose. These F18-FDG limitations have prompted development of several new target-specific radiotracers with more specific mechanisms of localization.1 One such PET radiotracer is gallium (Ga)-68 DOTATATE that is commonly used in the imaging of neuroendocrine tumors. Our case demonstrates how a negative F18-FDG PET-CT examination does not necessarily equate to benignity and how not all malignant lesions are hypermetabolic. We illustrate the importance of understanding the mechanism of localization for PET radiotracers in the evaluation and characterization of unknown lesions.