Attenuation Map Research Articles

Eliminating CT radiation for clinical PET examination using deep learning

Clinical PET/CT examinations rely on CT modality for anatomical localization and attenuation correction of the PET data. However, the use of CT significantly increases the risk of ionizing radiation exposure for patients. We propose a deep learning framework to learn the relationship mapping between attenuation corrected (AC) PET and non-attenuation corrected (NAC) PET images to estimate PET attenuation maps and generate pseudo-CT images for medical observation. In this study, 5760, 1608 and 1351 pairs of transverse PET-CT slices were used as the training, validation, and testing sets, respectively, to implement the proposed framework. A pix2pix model was adopted to predict AC PET images from NAC PET images, which allowed the calculation of PET attenuation maps (µ-maps). The same model was then applied to generate realistic CT images from the calculated µ-maps. The quality of predicted AC PET and CT was assessed using normalized root mean square error (NRMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and Pearson correlation coefficient (PCC). Relative to true AC PET, the synthetic AC PET achieved superior quantitative performances with 2.20 ± 1.17% NRMSE, 34.03 ± 4.73 dB PSNR, 97.90 ± 1.22% SSIM and 98.45 ± 1.31% PCC. The synthetic CT and synthetic AC PET images were deemed acceptable by radiologists who rated the images, as they provided sufficient anatomical and functional information, respectively. This work demonstrates that the proposed deep learning framework is a promising method in clinical applications, such as radiotherapy and low-dose imaging.

Evaluation of applying space-variant resolution modeling to attenuation correction in PET

Attenuation correction aims to recover the underestimated tracer uptake and improve the image contrast recovery in positron emission tomography (PET). However, traditional ray-tracing-based projection of attenuation maps is inaccurate as some physical effects are not considered, such as finite crystal size, inter-crystal penetration and inter-crystal scatter. In this study, we evaluated the effects of applying resolution modeling (RM) to attenuation correction by implementing space-variant RM to complement physical effects which are usually omitted in the traditional projection model. We verified this method on a brain PET scanner developed by our group, in both Monte Carlo simulation and real-world data, in comparison with space-invariant Gaussian RM, average-depth-of-interaction, and multi-ray tracing methods. The results indicate that the space-variant RM is superior in terms of artifacts reduction and contrast recovery.

Calculation of Gallium-68 Dose Factors for [68Ga] DOTATATE Injected Patients: A Comparison with OLINDA Database

[68Ga] DOTATATE as a radiolabeled tracer is used for in vivo detection of neuroendocrine tumors in the PET/CT examinations. This study aims to calculate S-values in various organs in a voxelized-based Monte Carlo simulation approach for each patient individually. PET/CT images of 9 patients suspected of neuroendocrine cancer were acquired 60 minutes after injection of [68Ga] DOTATATE. After reshaping and registering CT images to the size of PET images, GATE/GEANT4 Monte Carlo (MC) toolkit was used with two inputs of CT images as voxelized attenuation map and PET images as a voxelized activity map for the calculation of the different organs dose. Voxelized dose maps were extracted in the target organs for different source organs. S-value volume histogram and absolute S-values based on the MIRD formalism were calculated. The highest S-values were observed for spleen, bladder, kidneys, liver, pituitary, and the lung with 6.26E-05 ± 1.47E-05, 5.17E-05 ± 3.08E-05, 3.41E-05 ± 7.68E-06, 2.08E-05 ± 4.12E-06, 1.62E-05 ± 5.74E-06 and 8.47E-06 ± 2.47E-06 mGy/MBq.S, respectively. The difference between the amounts of the calculated S-values and those presented in OLINDA software is mainly related to the anatomical difference of the patients with the standard phantom in OLINEDA software. This study showed that patient-specific dosimetry is necessary to calculate S-values.

Propagation Attenuation Maps Based on Parabolic Equation Method.

Modern wireless communication systems use various technological solutions to increase the efficiency of created radio networks. This efficiency also applies to radio resources. Currently, the utilization of a radio environment map (REM) is one of the directions allowing to improve radio resource management. The REM is increasingly used in emerging mobile ad-hoc networks (MANETs), in particular military tactical networks. In this case, the use of new technologies such as software-defined radio and network, cognitive radio, radio sensing, and building electromagnetic situational awareness made it possible to implement REM in tactical MANETs. Propagation attenuation maps (PAMs) are crucial REM elements that allow for determining the ranges of radio network nodes. In this paper, we present a novel algorithm for PAM based on a parabolic equation method (PEM). The PEM allows determining the signal attenuation along the assumed propagation direction. In this case, we consider terrain topography to obtain a more realistic analysis. Then, we average the adjacent attenuation profiles defined for the selected directions in places where attenuation has not been calculated. To this aim, linear regression is applied. Finally, we define several metrics that allow for the accuracy assessment of determining the PAM as a function of its dimensions.

A novel tool for motion-related dose inaccuracies reduction in 99mTc-MAA SPECT/CT images for SIRT planning

IntroductionIn Selective Internal Radiation Therapy (SIRT), 90Y is administered to primary/secondary hepatic lesions. An accurate pre-treatment planning using 99mTc-MAA SPECT/CT allows the assessment of its feasibility and of the activity to be injected. Unfortunately, SPECT/CT suffers from patient-specific respiratory motion which causes artifacts and absorbed dose inaccuracies. In this study, a data-driven solution was developed to correct the respiratory motion. MethodsThe tool realigns the barycenter of SPECT projection images and shifts them to obtain a fine registration with the attenuation map. The tool was validated using a modified dynamic phantom with several breathing patterns. We compared the absorbed dose distributions derived from uncorrected(Dm)/corrected(Dc) images with static ones(Ds) in terms of γ-passing rates, 210 Gy isodose volumes, dose-volume histograms and percentage differences of mean doses (i.e., ΔD¯m and ΔD¯c, respectively). The tool was applied to twelve SIRT patients and the Bland-Altman analysis was performed on mean doses. ResultsIn the phantom study, the agreement between Dc and Ds was higher (γ-passing rates generally > 90%) than Dm and Ds. The isodose volumes in Dc were closer than Dm to Ds, with differences up to 10% and 30% respectively. A reduction from a median ΔD¯m = -19.3% to ΔD¯c = -0.9%, from ΔD¯m = -42.8% to ΔD¯c = -7.0% and from ΔD¯m = 1586% to ΔD¯c = 47.2% was observed in liver-, tumor- and lungs-like structures. The Bland-Altman analysis on patients showed variations (±50 Gy) and (±4 Gy) between D¯c and D¯m of tumor and lungs, respectively. ConclusionThe proposed tool allowed the correction of 99mTc-MAA SPECT/CT images, improving the accuracy of the absorbed dose distribution.

Are Quantitative Errors Reduced with Time-of-Flight Reconstruction When Using Imperfect MR-Based Attenuation Maps for 18F-FDG PET/MR Neuroimaging?

We studied whether TOF reduces error propagation from attenuation correction to PET image reconstruction in PET/MR neuroimaging, by using imperfect attenuation maps in a clinical PET/MR system with 525 ps timing resolution. Ten subjects who had undergone 18F-FDG PET neuroimaging were included. Attenuation maps using a single value (0.100 cm−1) with and without air, and a 3-class attenuation map with soft tissue (0.096 cm−1), air and bone (0.151 cm−1) were used. CT-based attenuation correction was used as a reference. Volume-of-interest (VOI) analysis was conducted. Mean bias and standard deviation across the brain was studied. Regional correlations and concordance were evaluated. Statistical testing was conducted. Average bias and standard deviation were slightly reduced in the majority (23–26 out of 35) of the VOI with TOF. Bias was reduced near the cortex, nasal sinuses, and in the mid-brain with TOF. Bland–Altman and regression analysis showed small improvements with TOF. However, the overall effect of TOF to quantitative accuracy was small (3% at maximum) and significant only for two attenuation maps out of three at 525 ps timing resolution. In conclusion, TOF might reduce the quantitative errors due to attenuation correction in PET/MR neuroimaging, but this effect needs to be further investigated on systems with better timing resolution.

Spatially Variant Ultrasound Attenuation Mapping Using a Regularized Linear Least-Squares Approach

Quantitative ultrasound methods aim to estimate the acoustic properties of the underlying medium, such as the attenuation and backscatter coefficients, and have applications in various areas including tissue characterization. In practice, tissue heterogeneity makes the coefficient estimation challenging. In this work, we propose a computationally efficient algorithm to map spatial variations of the attenuation coefficient. Our proposed approach adopts a fast, linear least-squares strategy to fit the signal model to data from pulse-echo measurements. As opposed to existing approaches, we directly estimate the attenuation map, that is, the local attenuation coefficient at each axial location by solving a joint estimation problem. In particular, we impose a physical model that couples all these local estimates and combine it with a smooth regularization to obtain a smooth map. Compared to the conventional spectral log difference method and the more recent ALGEBRA approach, we demonstrate that the attenuation estimates obtained by our method are more accurate and better correlate with the ground-truth attenuation profiles over a wide range of spatial and contrast resolutions.

Online Projector Deblurring Using a Convolutional Neural Network.

Projector deblurring is an important technology for dynamic projection mapping (PM), where the distance between a projector and a projection surface changes in time. However, conventional projector deblurring techniques do not support dynamic PM because they need to project calibration patterns to estimate the amount of defocus blur each time the surface moves. We present a deep neural network that can compensate for defocus blur in dynamic PM. The primary contribution of this paper is a unique network structure that consists of an extractor and a generator. The extractor explicitly estimates a defocus blur map and a luminance attenuation map. These maps are then injected into the middle layers of the generator network that computes the compensation image. We also propose a pseudo-projection technique for synthesizing physically plausible training data, considering the geometric misregistration that potentially happens in actual PM systems. We conducted simulation and actual PM experiments and confirmed that: (1) the proposed network structure is more suitable than a simple, more general structure for projector deblurring; (2) the network trained with the proposed pseudo-projection technique can compensate projection images for defocus blur artifacts in dynamic PM; and (3) the network supports the translation speed of the surface movement within a certain range that covers normal human motions.

Cross-vender, cross-tracer, and cross-protocol deep transfer learning for attenuation map generation of cardiac SPECT

It has been proved feasible to generate attenuation maps (μ-maps) from cardiac SPECT using deep learning. However, this assumed that the training and testing datasets were acquired using the same scanner, tracer, and protocol. We investigated a robust generation of CT-derived μ-maps from cardiac SPECT acquired by different scanners, tracers, and protocols from the training data. We first pre-trained a network using 120 studies injected with 99mTc-tetrofosmin acquired from a GE 850 SPECT/CT with 360-degree gantry rotation, which was then fine-tuned and tested using 80 studies injected with 99mTc-sestamibi acquired from a Philips BrightView SPECT/CT with 180-degree gantry rotation. The error between ground-truth and predicted μ-maps by transfer learning was 5.13 ± 7.02%, as compared to 8.24 ± 5.01% by direct transition without fine-tuning and 6.45 ± 5.75% by limited-sample training. The error between ground-truth and reconstructed images with predicted μ-maps by transfer learning was 1.11 ± 1.57%, as compared to 1.72 ± 1.63% by direct transition and 1.68 ± 1.21% by limited-sample training. It is feasible to apply a network pre-trained by a large amount of data from one scanner to data acquired by another scanner using different tracers and protocols, with proper transfer learning.

Two-dimensional mapping of the ultrasonic attenuation and speed of sound in brain

Brain is inhomogeneous due to its composition of different tissue types (gray and white matter), anatomical structures (e.g. thalamus and cerebellum), and cavities in the brain (ventricles). These inhomogeneities lead to spatial variations in the ultrasonic properties of the organ. The goal of this study is to characterize the spatial variation of the speed of ultrasound and frequency slope of attenuation in fixed sheep brain. 1-cm-thick slices of tissue from the coronal, sagittal and transverse cardinal planes were prepared from 12 brains. Ultrasonic measurements were performed using broadband transducers with center frequencies of 3.5, 5.0, 7.5 and 10 MHz. By mechanically scanning the transducers over the specimens, two-dimensional maps of the speed of sound (SOS) and frequency slope of attenuation (FSA) were produced. Measured values for the spatial mean and standard deviation of FSA ranged between 0.59 and 0.81 dB/cm·MHz and 0.29–0.60 dB/cm·MHz, respectively, depending on the specimen and transducer frequency. Measured values for the spatial mean and standard deviation of SOS ranged from 1532–1541 m/s and 10–14 m/s, respectively. Detailed, two-dimensional maps of FSA and SOS were produced, representing the first such characterization of the spatial variation of the ultrasonic properties of normal mammalian brain.

Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients

BackgroundQuantitative whole-body PET/MRI relies on accurate patient-specific MRI-based attenuation correction (AC) of PET, which is a non-trivial challenge, especially for the anatomically complex head and neck region. We used a deep learning model developed for dose planning in radiation oncology to derive MRI-based attenuation maps of head and neck cancer patients and evaluated its performance on PET AC.MethodsEleven head and neck cancer patients, referred for radiotherapy, underwent CT followed by PET/MRI with acquisition of Dixon MRI. Both scans were performed in radiotherapy position. PET AC was performed with three different patient-specific attenuation maps derived from: (1) Dixon MRI using a deep learning network (PETDeep). (2) Dixon MRI using the vendor-provided atlas-based method (PETAtlas). (3) CT, serving as reference (PETCT). We analyzed the effect of the MRI-based AC methods on PET quantification by assessing the average voxelwise error within the entire body, and the error as a function of distance to bone/air. The error in mean uptake within anatomical regions of interest and the tumor was also assessed.ResultsThe average (± standard deviation) PET voxel error was 0.0 ± 11.4% for PETDeep and −1.3 ± 21.8% for PETAtlas. The error in mean PET uptake in bone/air was much lower for PETDeep (−4%/12%) than for PETAtlas (−15%/84%) and PETDeep also demonstrated a more rapidly decreasing error with distance to bone/air affecting only the immediate surroundings (less than 1 cm). The regions with the largest error in mean uptake were those containing bone (mandible) and air (larynx) for both methods, and the error in tumor mean uptake was −0.6 ± 2.0% for PETDeep and −3.5 ± 4.6% for PETAtlas.ConclusionThe deep learning network for deriving MRI-based attenuation maps of head and neck cancer patients demonstrated accurate AC and exceeded the performance of the vendor-provided atlas-based method both overall, on a lesion-level, and in vicinity of challenging regions such as bone and air.

In vivo assessment of inflammatory bowel disease in rats with ultrahigh-resolution colonoscopic OCT.

A technology capable of high-resolution, label-free imaging of subtle pathology in vivo during colonoscopy is imperative for the early detection of disease and the performance of accurate biopsies. While colonoscopic OCT has been developed to visualize colonic microstructures beyond the mucosal surface, its clinical potential remains limited by sub-optimal resolution (∼6.5 µm in tissue), inadequate imaging contrast, and a lack of high-resolution OCT criteria for lesion detection. In this study, we developed an ultrahigh-resolution (UHR) colonoscopic OCT and evaluated its ability to volumetrically visualize and identify the pathological features of inflammatory bowel disease (IBD) in a rat model. Owing to its improved resolution (∼1.7 µm in tissue) and enhanced contrast, UHR colonoscopic OCT can accurately delineate fine colonic microstructures and identify the pathophysiological characteristics of IBD in vivo. By using a quantitative optical attenuation map, UHR colonoscopic OCT is able to differentiate diseased tissue (such as crypt distortion and microabscess) from normal colonic mucosa over a large field of view in vivo. Our results suggest the clinical potential of UHR colonoscopic OCT for in vivo assessment of IBD pathology.

Framelet tensor sparsity with block matching for spectral CT reconstruction.

Spectral computed tomography (CT) based on the photon-counting detection system has the capability to produce energy-discriminative attenuation maps of objects with a single scan. However, the insufficiency of photons collected into the narrow energy bins results in high quantum noise levels causing low image quality. This work aims to improve spectral CT image quality by developing a novel regularization based on framelet tensor prior. First, similar patches are extracted from highly correlated interchannel images in spectral and spatial domains, and stacked to form a third-order tensor after vectorization along the energy channels. Second, the framelet tensor nuclear norm (FTNN) is introduced and applied to construct the regularization to exploit the sparsity embedded in nonlocal similarity of spectral images, and thus the reconstruction problem is modeled as a constrained optimization. Third, an iterative algorithm is proposed by utilizing the alternating direction method of multipliers framework in which efficient solvers are developed for each subproblem. Both numerical simulations and real data verifications were performed to evaluate and validate the proposed FTNN-based method. Compared to the analytic, TV-based, and the state-of-the-art tensor-based methods, the proposed method achieves higher numerical accuracy on both reconstructed CT images and decomposed material maps in the mouse data indicating the capability in noise suppression and detail preservation of the proposed method. A framelet tensor sparsity-based iterative algorithm is proposed for spectral reconstruction. The qualitative and quantitative comparisons show a promising improvement of image quality, indicating its promising potential in spectral CT imaging.

A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE)

As part of the Virgo Cluster Survey Tracing Ionised Gas Emission, a blind narrow-band Hα+[NII] imaging survey of the Virgo cluster carried out with MegaCam at the CFHT, we discovered eight massive (1010 ≲ Mstar ≲ 1011 M⊙) lenticular galaxies with prominent ionised gas emission features in their inner (a few kiloparsec) regions. These features are either ionised gas filaments similar to those observed in cooling flows (two galaxies), or they are thin discs with sizes 0.7 ≲ R(Hα)≲2.0 kpc (six galaxies), thus significantly smaller than those of the stellar disc (R(Hα)≃7 − 22%Riso(r)). The morphological properties of these discs are similar to those of the dust seen in absorption in high-resolution HST images. Using a unique set of multifrequency data, including new or archival ASTROSAT/UVIT, GALEX, HST, CFHT,Spitzer, andHerschelimaging data, combined with IFU (MUSE, ALMA) and long-slit (SOAR) spectroscopy, we show that while the gas that is located within these inner discs is photoionised by young stars, which signals ongoing star formation, the gas in the filamentary structures is shock ionised. The star formation surface brightness of these discs is similar to that observed in late-type galaxies. Because of their reduced size, however, these lenticular galaxies are located below the main sequence of unperturbed or cluster star-forming systems. By comparing the dust masses measured from absorption maps in optical images, from the Balmer decrement, or estimated by fitting the UV-to-far-IR spectral energy distribution of the target galaxies, we confirm that the dust masses derived from optical attenuation maps are heavily underestimated because of geometrical effects due to the relative distribution of the absorbing dust and the emitting stars. We also show that these galaxies have gas-to-dust ratios ofG/D≃ 8030320, and that the star formation within these discs follows the Schmidt relation, but with an efficiency that is reduced by a factor of ∼2.5. Using our unique set of multifrequency data, we discuss the possible origin of the ionised gas in these objects, which suggests multiple and complex formation scenarios for massive lenticular galaxies in clusters.

Toward three-dimensional patient-specific internal dosimetry using GATE Monte Carlo technique

Patient-specific internal dosimetry with a high accuracy is one of the most significant issues in the field of nuclear medicine. In recent investigations, there has been a dramatic alter in the different methods for computing the correct organ doses from the injected radioactivity. Monte Carlo simulations have yielded highly accurate results for voxel-based dosimetry techniques. This paper aims to present a precise methodology based on full Monte Carlo simulations, and time-activity curve estimation using three individual scans over 90 min. In this study, a dataset of patients who were injected with F-Fluor-2-D-Glucose, F18-FDG (10 normal cases, 60 ± 8.2 kg, injected activity, 370 ± 10 MBq), was acquired in 3 different time sequences (20, 60, 90 min post-injection). First, the GATE Monte Carlo results were validated using experimental measurements. Subsequently, the Monte Carlo code was considered for S-value calculations. The CT and PET images were used to obtain attenuation maps and activity distribution inside the patients' phantom, respectively. The CT and PET images were registered using an image processing software to achieve the same matrix and pixel sizes. Regions of interest, such as the kidneys, spleen, bladder, lung, pancreas, liver, stomach, and heart wall, were identified on CT images. The segmented ROIs of the CT images were reconstructed and the voxelized phantom and voxelized source of each patient at specific time intervals were generated. The time activity curves (TACs) were obtained based on PET images. The results of the F18-FDG dose distribution in patient-specific phantoms indicate that the bladder, lung, heart, and liver absorbed more activity than other organs, as expected from the PET images. Close agreement was observed between the results obtained in this study and those of previous investigations that used biokinetic models. The dose per activity values was reported within 0.02–0.06 mGy/MBq for most organs with simulation uncertainty less than 7%. There is ample room for further progress in the clinical use of the Monte Carlo method and the improvement of the phantom-based model.

Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT.

Deep-learning-based attenuation correction (AC) for SPECT includes both indirect and direct approaches. Indirect approaches generate attenuation maps (μ-maps) from emission images, while direct approaches predict AC images directly from non-attenuation-corrected (NAC) images without μ-maps. For dedicated cardiac SPECT scanners with CZT detectors, indirect approaches are challenging due to the limited field-of-view (FOV). In this work, we aim to 1) first develop novel indirect approaches to improve the AC performance for dedicated SPECT; and 2) compare the AC performance between direct and indirect approaches for both general purpose and dedicated SPECT. For dedicated SPECT, we developed strategies to predict truncated μ-maps from NAC images reconstructed with a small matrix, or full μ-maps from NAC images reconstructed with a large matrix using 270 anonymized clinical studies scanned on a GE Discovery NM/CT 570c SPECT/CT. For general purpose SPECT, we implemented direct and indirect approaches using 400 anonymized clinical studies scanned on a GE NM/CT 850c SPECT/CT. NAC images in both photopeak and scatter windows were input to predict μ-maps or AC images. For dedicated SPECT, the averaged normalized mean square error (NMSE) using our proposed strategies with full μ-maps was 1.20 ± 0.72% as compared to 2.21 ± 1.17% using the previous direct approaches. The polar map absolute percent error (APE) using our approaches was 3.24 ± 2.79% (R2 = 0.9499) as compared to 4.77 ± 3.96% (R2 = 0.9213) using direct approaches. For general purpose SPECT, the averaged NMSE of the predicted AC images using the direct approaches was 2.57 ± 1.06% as compared to 1.37 ± 1.16% using the indirect approaches. We developed strategies of generating μ-maps for dedicated cardiac SPECT with small FOV. For both general purpose and dedicated SPECT, indirect approaches showed superior performance of AC than direct approaches.

Evaluation of a 2D UNet-Based Attenuation Correction Methodology for PET/MR Brain Studies

Deep learning (DL) strategies applied to magnetic resonance (MR) images in positron emission tomography (PET)/MR can provide synthetic attenuation correction (AC) maps, and consequently PET images, more accurate than segmentation or atlas-registration strategies. As first objective, we aim to investigate the best MR image to be used and the best point of the AC pipeline to insert the synthetic map in. Sixteen patients underwent a 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) and a PET/MR brain study in the same day. PET/CT images were reconstructed with attenuation maps obtained: (1) from CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with a 2D UNet trained on MR image/attenuation map pairs. As for MR, T1-weighted and Zero Time Echo (ZTE) images were considered; as for attenuation maps, CTs and 511 keV low-resolution attenuation maps were assessed. As second objective, we assessed the ability of DL strategies to provide proper AC maps in presence of cranial anatomy alterations due to surgery. Three 11C-methionine (METH) PET/MR studies were considered. PET images were reconstructed with attenuation maps obtained: (1) from diagnostic coregistered CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with 2D UNets trained on the sixteen FDG anatomically normal patients. Only UNets taking ZTE images in input were considered. FDG and METH PET images were quantitatively evaluated. As for anatomically normal FDG patients, UNet AC models generally provide an uptake estimate with lower bias than atlas-based or segmentation-based methods. The intersubject average bias on images corrected with UNet AC maps is always smaller than 1.5%, except for AC maps generated on too coarse grids. The intersubject bias variability is the lowest (always lower than 2%) for UNet AC maps coming from ZTE images, larger for other methods. UNet models working on MR ZTE images and generating synthetic CT or 511 keV low-resolution attenuation maps therefore provide the best results in terms of both accuracy and variability. As for METH anatomically altered patients, DL properly reconstructs anatomical alterations. Quantitative results on PET images confirm those found on anatomically normal FDG patients.

Utility of Dual-Energy CT in Abdominal Interventions

AbstractDual-energy computed tomography (DECT) is an emerging CT technique based on data acquisition at two different settings. Various postprocessing techniques generate different sets of images, each with unique advantages. With DECT, it is possible to obtain virtual unenhanced images from monochromatic reconstructions and attenuation maps of different elements, thereby improving the detection and characterization of a variety of lesions. Presently, DECT is widely used to evaluate pulmonary embolism, characterize abdominal masses, determine the composition of urinary calculi, and detect tophi in gout. CT angiography is an essential prerequisite for endovascular intervention. DECT allows a better quality of angiographic images with a lesser dose of contrast. Various postprocessing techniques in DECT also help in a better evaluation of response to locoregional therapy. Virtual noncontrast images and iodine map differentiate residual or recurrent tumors from intrinsically hyperdense materials. Superior metallic artifact reduction allows better evaluation of vascular injuries adjacent to bony fractured fragments or previously deployed embolization coils. In addition to metal artifacts reduction, virtual monochromatic spectral imaging could further mitigate metal artifacts during CT-guided biopsy, providing an improved depiction of lesions and safe and versatile access for long puncture pathways. This article reviews and illustrates the different applications of DECT in various abdominal interventions. Familiarity with the capabilities of DECT may help interventional radiologists to improve their practice and ameliorate patient care.

The impact of MR-guided attenuation correction (compared to CT-based AC) on the diagnosis of anosmia based on 99m-Tc Ethyl-Cysteinate-Dimer SPECT images

99m-Tc Ethyl-Cysteinate-Dimer SPECT and MR imaging play a significant role in diagnosing anosmia. In this study, two-tissue class and three-tissue class attenuation maps (2C-MR and 3CMR) obtained from MR images were compared with CT-based attenuation correction (CTAC). Afterward, the presence of hypo-perfusion in brain lobes was evaluated in SPECT images. The 2C-MRAC map was generated through segmentation of T1-W MR images into air and soft-tissue, while in the 3C-MRAC map, the cortical bone was also considered. For investigating MRAC approaches, the difference between activity concentration (ACC) values was estimated in 144 volumes of interest. Ten normal and fourteen anosmic patients were compared by calculating the average normalized count and standard uptake value ratio parameters in the brain lobes. The comparison between attenuation correction strategies represented that MRAC images resulted in underestimation of the ACC values which was more substantial in the cortical area rather than in central regions (maximum 9% vs. 6% for 2C-MR and maximum 5.5% vs. 3.5% for 3C-MR). Nevertheless, there was a strong correlation between the MRAC and CTAC methods with a correlation coefficient of 0.7 for both 2C-MR and 3C-MR. The statistical analysis between normal and affected groups indicated the hypoperfusion in the cortex of Lh_frontal, Rh and Lh_temporal lobes with p-values < 0.05. Using MRAC resulted in underestimation of activity concentration which was partly eliminated by considering the cortical bone in the 3C-MR attenuation map. Hypo-perfusion was perceived in Frontal and Temporal lobes in SPECT-MRAC images of the anosmic group.

3-D DISTRIBUTION OF BONE PARAMETERS ACROSS THE DISTAL FEMUR FROM WEIGHT BEARING CT FOLLOWS PATTERNS ACCORDING TO JOINT SPACE NARROWING PHENOTYPE: A MULTICENTER OSTEOARTHRITIS STUDY INVESTIGATION

Weight bearing CT (WBCT) technology has been increasingly used in imaging of knee osteoarthritis in recent years because of its ability to assess the weight bearing joint space. However, relatively little is known regarding the extent to which this cone beam technology can provide useful information on peri-articular bone beyond the subchondral regions. To explore relationships between WBCT-derived 3-D cortical bone thickness and trabecular bone attenuation at the distal femur with concurrent joint space narrowing phenotypes. WBCT imaging was obtained ancillary to the Multicenter Osteoarthritis Study at the 144-month visit. After semi-automatic segmentation, femoral cortical thickness (fCT) and trabecular attenuation (fTA) maps were created for each of 663 available distal femurs using cortical bone mapping. A template was registered to each individual distal femur and a 3-D statistical shape model created, followed by transfer of fCT and fTA distribution maps onto the template. A statistical parametric mapping (SPM) general linear model adjusted for age, sex, BMI, and the first 5 shape modes (controlling for effects of systematic misregistration) was used to test the dependence of fCT and fTA in turn on the experimental variables of concurrent radiographic medial and lateral OARSI joint space narrowing (JSN) grade. One knee from each participant was selected for inclusion, taking the side with worse compartmental baseline JSN grade or randomly if equal. 16 knees were excluded due to unachievable registration, with a final study set of a single knee from 386 individuals. 219 were female, mean ± SD age was 63.6 ± 9.6 yrs, mass 82.3 ± 17.7 kg, height 169 ± 9 cm, and BMI 28.5 ± 5.0 kg/m 2 . SPM significance ROIs according to baseline medial (left, figs 1 & 2) and lateral (right, figs 3 & 4) JSN phenotypes are shown for fCT (top row, figs 1 & 3) and fTA (bottom row, figs 2 & 4). SPM revealed significantly greater fCT by up to ∼0.1mm and fTA by up to ∼40 attenuation units (AU) for each increment in baseline JSN grade along the outer margin of the respective compartments (unmasked blue zones in each figure). Significantly greater fCT by up to ∼0.1mm for each increment in medial JSN grade also spread across the anterior aspect of the medial femoral condyle suggesting a wider regional relationship. 3-D distribution of fCT and fTA follow distinct patterns according to OARSI JSN phenotype, relating to recognised distributions of marginal osteophytosis and subchondral sclerosis. These results indicate that cone beam WBCT can quantify cardinal bony structural features of osteoarthritis and that their distribution is related to specific osteoarthritis OARSI JSN phenotypes. National Institutes of Health, University of Kansas (R01AR071648), University of Iowa. (U01AG18832), University of California-San Francisco (U01AG19069), Boston University (U01AG018820). NS is a consultant for Integra BioLife, Trice Medical and Pacira Biosciences. The authors would like to thank participants and staff of the MOST study. CORRESPONDENCE ADDRESS: tom@turmezei.com