Attenuation compensation in cerebral 3D PET: effect of the attenuation map on absolute and relative quantitation (original) (raw)
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The aim of this study is to compare the effect of the two major classes for determining the attenuation map, i.e. non-uniform versus uniform using clinical studies based on qualitative assessment as well as absolute and relative quantitative volume of interest (VOI)-based analysis. We investigate the effect of six different methods for determining patient-specific attenuation map: 2 approximate calculated methods, 2 TX-based methods, our newly developed segmented magnetic resonance imaging (MRI)-guided method, and finally an inferred anatomy-based technique. Ten cerebral clinical studies were selected from the database and used for clinical evaluation of the attenuation correction techniques. Several image quality parameters were compared including absolute and relative quantification indexes and correlation between them checked. The qualitative assessment showed no significant differences between the different attenuation correction techniques as assessed by expert physicians excep...
Quantitative analysis of template-based attenuation compensation in 3D brain PET
Computerized Medical Imaging and Graphics, 2007
An atlas-guided attenuation correction method was recently proposed for 3D brain positron emission tomography (PET) imaging eliminating the need for acquisition of a patient-specific measured transmission scan. The algorithm was validated through comparison to transmission-based attenuation correction (gold standard) using voxelwise statistical parametric mapping (SPM) analysis of clinical data. In contrast to brain 'activation' studies for which SPM is primarily developed, brain PET research studies often involve absolute quantification. In the preliminary validation study published earlier, there is no validation as to how such quantification can be affected by the two methods as the assessment was carried out by an SPM group analysis alone. It is quite important to demonstrate how the proposed method performs individually, particularly for diagnostic applications or individual quantification. In this study, we assess the quantitative accuracy of this method in clinical setting using automated volume of interest (VOI)-based analysis by means of the commercially available BRASS software.
Journal of Nuclear Medicine, 2021
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 voxelwise 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.
Journal of Nuclear Medicine, 2021
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 voxelwise 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.
Medical Physics, 2012
Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [ 11 C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR/PET.
Analysis of biased PET images caused by inaccurate attenuation coefficients
Journal of Nuclear …, 2010
PET scanners with an elongated axial field of view intended to increase overall system sensitivity, such as the high-resolution research tomograph (HRRT) scanner, have been reported to produce images with decreased signals in the brain stem and cerebellum. The cause of this negative bias of the images was analyzed, and the effects of an inaccurate linear attenuation coefficient (m-value) of tissue and bones were separately examined. Methods: A new phantom was manufactured, and 18 human subjects were recruited for the study. 18 F-FDG PET images were reconstructed using attenuation coefficient maps generated by various algorithms. The algorithms included maximum a posteriori reconstruction for transmission data (MAP-TR) with default priors, MAP-TR with adjusted priors for bone (MAP-TR adj-b ), MAP-TR with adjusted priors for tissue (MAP-TR adj-t ), and noise-equivalent count TR and CT-TR. Results: With the CT-TR and MAP-TR adj-t algorithms, increased intensity in the brain stem and cerebellum was seen, and negative bias was reduced. With the MAP-TR adj-t algorithm, however, positive bias increased in the central region. Inappropriate attenuation coefficients of brain tissue increased the positive or negative bias of reconstructed images, especially for the central regions of the volume. Poor representation of the skull or bone also locally increased the bias in the near regions where bone detection had failed. Conclusion: An inaccurate m-map obtained from the MAP-TR algorithm caused the bias problem for the HRRT system. The CT-TR algorithm provided a relatively more reliable m-map that demonstrated a small degree of intensity bias. Appropriate priors for m-values of each tissue compartment and better classification to distinguish bone from tissue are necessary for accurate attenuation correction.
2010
Several factors have to be considered for implementing an accurate attenuation-correction (AC) method in a combined MR-PET scanner. In this work, some of these challenges were investigated, and an AC method based entirely on the MRI data obtained with a single dedicated sequence was developed and used for neurologic studies performed with the MR-PET human brain scanner prototype. Methods: The focus was on the problem of bone-air segmentation, selection of the linear attenuation coefficient for bone, and positioning of the radiofrequency coil. The impact of these factors on PET data quantification was studied in simulations and experimental measurements performed on the combined MR-PET scanner. A novel dual-echo ultrashort echo time (DUTE) MRI sequence was proposed for head imaging. Simultaneous MR-PET data were acquired, and the PET images reconstructed using the proposed DUTE MRI-based AC method were compared with the PET images that had been reconstructed using a CT-based AC method. Results: Our data suggest that incorrectly accounting for the bone tissue attenuation can lead to large underestimations (.20%) of the radiotracer concentration in the cortex. Assigning a linear attenuation coefficient of 0.143 or 0.151 cm 21 to bone tissue appears to give the best trade-off between bias and variability in the resulting images. Not identifying the internal air cavities introduces large overestimations (.20%) in adjacent structures. On the basis of these results, the segmented CT AC method was established as the silver standard for the segmented MRI-based AC method. For an integrated MR-PET scanner, in particular, ignoring the radiofrequency coil attenuation can cause large underestimations (i.e., #50%) in the reconstructed images. Furthermore, the coil location in the PET field of view has to be accurately known. High-quality bone-air segmentation can be performed using the DUTE data. The PET images obtained using the DUTE MRI-and CT-based AC methods compare favorably in most of the brain structures. Conclusion: A DUTE MRI-based AC method considering all these factors was implemented. Preliminary results suggest that this method could potentially be as accurate as the segmented CT method and could be used for quantitative neurologic MR-PET studies.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2016
A spatial bias in brain PET/MR exists compared to PET/CT, due to MR-based attenuation correction (MRAC). We performed an evaluation between four institutions, three PET/MR and four PET/CT systems, using an anthropomorphic brain phantom. We hypothesized, that the spatial bias would be effectively minimized with CT-based attenuation correction (CTAC). Evaluation protocol was similar to quantifying changes in neurological PET studies. Regional analysis was conducted on eight anatomical VOIs in grey matter on count-normalized, resolution matched and co-registered data. On PET/MR systems, CTAC was applied as the reference method for attenuation correction. With CTAC, visual and quantitative differences between PET/MR and PET/CT systems were minimized. Inter-system variation was +3.42 % to -3.29 % in all VOIs in PET/CTs and +2.15 % to -4.50 % in all VOIs for PET/MRs between institutions. PET/MR systems differed by +2.34 % to -2.21 %, +2.04 % to -2.08 % and -1.77 % to -5.37 % when compared...
Journal of Nuclear Medicine, 2011
PET/MRI is an emerging dual-modality imaging technology that requires new approaches to PET attenuation correction (AC). We assessed 2 algorithms for whole-body MRI-based AC (MRAC): a basic MR image segmentation algorithm and a method based on atlas registration and pattern recognition (AT&PR). Methods: Eleven patients each underwent a whole-body PET/ CT study and a separate multibed whole-body MRI study. The MR image segmentation algorithm uses a combination of image thresholds, Dixon fat-water segmentation, and component analysis to detect the lungs. MR images are segmented into 5 tissue classes (not including bone), and each class is assigned a default linear attenuation value. The AT&PR algorithm uses a database of previously aligned pairs of MRI/CT image volumes. For each patient, these pairs are registered to the patient MRI volume, and machine-learning techniques are used to predict attenuation values on a continuous scale. MRAC methods are compared via the quantitative analysis of AC PET images using volumes of interest in normal organs and on lesions. We assume the PET/CT values after CT-based AC to be the reference standard. Results: In regions of normal physiologic uptake, the average error of the mean standardized uptake value was 14.1% 6 10.2% and 7.7% 6 8.4% for the segmentation and the AT&PR methods, respectively. Lesion-based errors were 7.5% 6 7.9% for the segmentation method and 5.7% 6 4.7% for the AT&PR method. Conclusion: The MRAC method using AT&PR provided better overall PET quantification accuracy than the basic MR image segmentation approach. This better quantification was due to the significantly reduced volume of errors made regarding volumes of interest within or near bones and the slightly reduced volume of errors made regarding areas outside the lungs.