Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding.

AIMS: We aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC) histopathological subtypes through a dual-centre dataset and comprehensively evaluate the effect of ComBat harmonisation on the performance of single- and multimodality radiomic models. MATERIALS AND METHODS: A public dataset of NSCLC patients from two independent centres was used. Two image fusion methods, namely guided filtering-based fusion and image fusion based on visual saliency map and weighted least square optimisation, were used. Radiomic features were extracted from each scan, including first-order, texture and moment-invariant features. Subsequently, ComBat harmonisation was applied to the extracted features from computed tomography (CT), positron emission tomography (PET) and fused images to correct the centre effect. For feature selection, least absolute shrinkage and selection operator (Lasso) and recursive feature elimination (RFE) were investigated. For machine learning, logistic regression (LR), support vector machine (SVM) and AdaBoost were evaluated for classifying NSCLC subtypes. Training and evaluation of the models were carried out in a robust framework to offset plausible errors and performance was reported using area under the curve, balanced accuracy, sensitivity and specificity before and after harmonisation. N-way ANOVA was used to assess the effect of different factors on the performance of the models. RESULTS: Support vector machine fed with selected features by recursive feature elimination from a harmonised PET feature set achieved the highest performance (area under the curve = 0.82) in classifying NSCLC histopathological subtypes. Although the performance of the models did not significantly improve for CT images after harmonisation, the performance of PET and guided filtering-based fusion feature signatures significantly improved for almost all models. Although the selection of the image modality and feature selection methods was effective on the performance of the model (ANOVA P-values <0.001), machine learning and harmonisation did not change the performance significantly (ANOVA P-values = 0.839 and 0.292, respectively). CONCLUSION: This study confirmed the potential of radiomic analysis on PET, CT and hybrid images for histopathological classification of NSCLC subtypes.

Expanding the medical physicist curricular and professional programme to include Artificial Intelligence.

PURPOSE: To provide a guideline curriculum related to Artificial Intelligence (AI), for the education and training of European Medical Physicists (MPs). MATERIALS AND METHODS: The proposed curriculum consists of two levels: Basic (introducing MPs to the pillars of knowledge, development and applications of AI, in the context of medical imaging and radiation therapy) and Advanced. Both are common to the subspecialties (diagnostic and interventional radiology, nuclear medicine, and radiation oncology). The learning outcomes of the training are presented as knowledge, skills and competences (KSC approach). RESULTS: For the Basic section, KSCs were stratified in four subsections: (1) Medical imaging analysis and AI Basics; (2) Implementation of AI applications in clinical practice; (3) Big data and enterprise imaging, and (4) Quality, Regulatory and Ethical Issues of AI processes. For the Advanced section instead, a common block was proposed to be further elaborated by each subspecialty core curriculum. The learning outcomes were also translated into a syllabus of a more traditional format, including practical applications. CONCLUSIONS: This AI curriculum is the first attempt to create a guideline expanding the current educational framework for Medical Physicists in Europe. It should be considered as a document to top the sub-specialties' curriculums and adapted by national training and regulatory bodies. The proposed educational program can be implemented via the European School of Medical Physics Expert (ESMPE) course modules and - to some extent - also by the national competent EFOMP organizations, to reach widely the medical physicist community in Europe.

Histological and chemical damage induced by microcystin-LR and microcystin-RR on land snail Helix aspersa tissues after acute exposure.

Microcystins (MCs) are the most common cyanotoxins with more than 200 variants. Among these cyanotoxins, microcystin-LR (MC-LR) and microcystin-RR (MC-RR) are the most studied congeners due to their high toxicity and frequent occurrence in surface waters. MC-LR has been detected in more than 75% of natural cyanobacteria bloom, along with other toxic and less toxic congeners. Accumulation of several microcystins variants (MC-LR and MC-RR) has been confirmed in aquatic snails exposed naturally or in the laboratory to toxic blooms. Thus, this paper aims to compare the biochemical and histological impact of both toxic variants (microcystin-LR and microcystin-RR) and their mixed form on a bioindicator, the land snail Helix aspersa. During experiments, snails were gavaged with a single acute dose (0.5 µg/g) of purified MC-LR, MC-RR, or mixed MC-LR + MC-RR (0.25 + 0.25 µg/g). After 96 h of exposure, effects on the hepatopancreas, kidney, intestine and lungs were assessed by histological observations and analysis of oxidative stress biomarkers. The results show that a small dose of MCs variants can increase the non-enzymatic antioxidant glutathione (GSH), inhibit glutathione-s-transferase (GST) level and trigger a defense system by activating glutathione peroxidase (GPx), catalase (CAT) and superoxide dismutase (SOD). Microcystin-RR causes serious anomalies in the hepatopancreas and kidney than Microcystin-LR. The organ most affected is the kidney. The damage caused by MC-LR + MC-RR is greater than that caused by single variants.

A tailored approach to abdominoperineal resection for rectal cancer: multicentre analysis of short-term outcomes and impact on oncological survival.

PURPOSE: Abdominoperineal resection of the rectum has evolved over the last century, with few modifications until 2007, when extralevator abdominoperineal resection was introduced, which improved local disease control but resulted in a significant rise in perineal complications. We adopted a modified approach in which dissection was tailored according to magnetic resonance-defined tumour involvement. The aim of this study was to assess short-term and long-term oncological outcomes following a tailored abdominoperineal resection (APR) approach. METHODS: This study was a retrospective review of prospectively maintained databases at three centres: Portsmouth NHS Trust (UK), Poole General Hospital (UK) and Champalimaud's Cancer Foundation, Portugal. The study included consecutive patients who underwent abdominoperineal resection from October 2008 until April 2018 under the supervision of the senior author. Oncological outcomes, including overall survival and disease-free survival, were used as the main outcome measures. RESULTS: A total of 584 patients underwent rectal cancer surgery during the study period. The APR ratio was 65/584 (11%). The median age was 66 years. Neoadjuvant treatment was administered to 74% of patients. Of the patients, 91% underwent surgery via a minimally invasive approach. The median hospital stay was 7 days. Patients were followed up for a median of 41 months. Only four patients had positive resection margins. The 5-year overall and disease-free survival rates were 64% and 62%, respectively. CONCLUSION: Our data suggest that tailored APR has similar short-term and long-term oncological outcomes compared with extralevator abdominoperineal resection but reduced perineal wound complications. We believe this approach could be a safe alternative but recommend a larger sample size to accurately assess its effectiveness.

Clinical predictive factors of histological chorioamnionitis: case-control study.

INTRODUCTION: Histological chorioamnionitis or "intrauterine inflammation or infection" (Triple I) it is an acute inflammation of amniotic membrane, chorionic plate and umbilical cord. SUBJECT: To assess in the event of the clinical predictive factors associated to histological chorioamnionitis. METHODS: Prospective examination of 50 placentas from aberrant pregnancies, and 50 placentas from 'normal' deliveries. The Placentas analyzed by the conventional histopathology method, and the severity of chorioamnionitis was classified histologically according to the intensity and the topography of placental inflammation.The clinical and histopathological features of the study groups were introduced into the SPSS 13 database (License University Mohammed V-Rabat). RESULTS: 36/50 placentas of aberrant pregnancies showed a histological chorioamnionitis often associated to a funisitis, and 11/50 normal placentas have shown some lesions of histological chorioamnionitis mainly grade one without funisitis.On the other hand we noted a statistically significant association between histological chorioamnionitis and premature rupture of the membranes (PROM) over than 12h (p < 0.001). CONCLUSIONS: Our study confirmed the predominance of histological chorioamnionitis lesions in clinically suspected cases of chorioamnionitis with 72% versus 22% in the controls group.Among the clinical parameters studied, only the premature rupture of the Membranes was shown a statistically significant association with the appearance of histological signs of chorioamnionitis.In conclusion, chorioamnionitis is sometimes clinically silent. Morphological placental study could be a confirmation of this pathology, which is predominantly associated to PROM over than 12 h.

Radiomics for classification of bone mineral loss: A machine learning study.

PURPOSE: The purpose of this study was to develop predictive models to classify osteoporosis, osteopenia and normal patients using radiomics and machine learning approaches. MATERIALS AND METHODS: A total of 147 patients were included in this retrospective single-center study. There were 12 men and 135 women with a mean age of 56.88±10.6 (SD) years (range: 28-87 years). For each patient, seven regions including four lumbar and three femoral including trochanteric, intertrochanteric and neck were segmented on bone mineral densitometry images and 54 texture features were extracted from the regions. The performance of four feature selection methods, including classifier attribute evaluation (CLAE), one rule attribute evaluation (ORAE), gain ratio attribute evaluation (GRAE) and principal components analysis (PRCA) along with four classification methods, including random forest (RF), random committee (RC), K-nearest neighbor (KN) and logit-boost (LB) were evaluated. Four classification categories, including osteopenia vs. normal, osteoporosis vs. normal, osteopenia vs. osteoporosis and osteoporosis+osteopenia vs. osteoporosis were examined for the defined seven regions. The classification model performances were evaluated using the area under the receiver operator characteristic curve (AUC). RESULTS: The AUC values ranged from 0.50 to 0.78. The combination of methods RF+CLAE, RF+ORAE and RC+ORAE yielded highest performance (AUC=0.78) in discriminating between osteoporosis and normal state in the trochanteric region. The combinations of RF+PRCA and LB+PRCA had the highest performance (AUC=0.76) in discriminating between osteoporosis and normal state in the neck region. CONCLUSION: The machine learning radiomic approach can be considered as a new method for bone mineral deficiency disease classification using bone mineral densitometry image features.

Robustness of post-reconstruction and direct kinetic parameter estimates under rigid head motion in dynamic brain PET imaging.

OBJECTIVE: Dynamic PET imaging is extensively used in brain imaging to estimate parametric maps. Inter-frame motion can substantially disrupt the voxel-wise time-activity curves (TACs), leading to erroneous maps during kinetic modelling. Therefore, it is important to characterize the robustness of kinetic parameters under various motion and kinetic model related factors. METHODS: Fully 4D brain simulations ([15O]H2O and [18F]FDG dynamic datasets) were performed using a variety of clinically observed motion patterns. Increasing levels of head motion were investigated as well as varying temporal frames of motion initiation. Kinetic parameter estimation was performed using both post-reconstruction kinetic analysis and direct 4D image reconstruction to assess bias from inter-frame emission blurring and emission/attenuation mismatch. RESULTS: Kinetic parameter bias heavily depends on the time point of motion initiation. Motion initiated towards the end of the scan results in the most biased parameters. For the [18F]FDG data, k4 is the more sensitive parameter to positional changes, while K1 and blood volume were proven to be relatively robust to motion. Direct 4D image reconstruction appeared more sensitive to changes in TACs due to motion, with parameter bias spatially propagating and depending on the level of motion. CONCLUSION: Kinetic parameter bias highly depends upon the time frame at which motion occurred, with late frame motion-induced TAC discontinuities resulting in the least accurate parameters. This is of importance during prolonged data acquisition as is often the case in neuro-receptor imaging studies. In the absence of a motion correction, use of TOF information within 4D image reconstruction could limit the error propagation.

Impact of time-of-flight on indirect 3D and direct 4D parametric image reconstruction in the presence of inconsistent dynamic PET data.

Kinetic parameter estimation in dynamic PET suffers from reduced accuracy and precision when parametric maps are estimated using kinetic modelling following image reconstruction of the dynamic data. Direct approaches to parameter estimation attempt to directly estimate the kinetic parameters from the measured dynamic data within a unified framework. Such image reconstruction methods have been shown to generate parametric maps of improved precision and accuracy in dynamic PET. However, due to the interleaving between the tomographic and kinetic modelling steps, any tomographic or kinetic modelling errors in certain regions or frames, tend to spatially or temporally propagate. This results in biased kinetic parameters and thus limits the benefits of such direct methods. Kinetic modelling errors originate from the inability to construct a common single kinetic model for the entire field-of-view, and such errors in erroneously modelled regions could spatially propagate. Adaptive models have been used within 4D image reconstruction to mitigate the problem, though they are complex and difficult to optimize. Tomographic errors in dynamic imaging on the other hand, can originate from involuntary patient motion between dynamic frames, as well as from emission/transmission mismatch. Motion correction schemes can be used, however, if residual errors exist or motion correction is not included in the study protocol, errors in the affected dynamic frames could potentially propagate either temporally, to other frames during the kinetic modelling step or spatially, during the tomographic step. In this work, we demonstrate a new strategy to minimize such error propagation in direct 4D image reconstruction, focusing on the tomographic step rather than the kinetic modelling step, by incorporating time-of-flight (TOF) within a direct 4D reconstruction framework. Using ever improving TOF resolutions (580 ps, 440 ps, 300 ps and 160 ps), we demonstrate that direct 4D TOF image reconstruction can substantially prevent kinetic parameter error propagation either from erroneous kinetic modelling, inter-frame motion or emission/transmission mismatch. Furthermore, we demonstrate the benefits of TOF in parameter estimation when conventional post-reconstruction (3D) methods are used and compare the potential improvements to direct 4D methods. Further improvements could possibly be achieved in the future by combining TOF direct 4D image reconstruction with adaptive kinetic models and inter-frame motion correction schemes.

Application of adaptive kinetic modelling for bias propagation reduction in direct 4D image reconstruction.

Parametric imaging in thoracic and abdominal PET can provide additional parameters more relevant to the pathophysiology of the system under study. However, dynamic data in the body are noisy due to the limiting counting statistics leading to suboptimal kinetic parameter estimates. Direct 4D image reconstruction algorithms can potentially improve kinetic parameter precision and accuracy in dynamic PET body imaging. However, construction of a common kinetic model is not always feasible and in contrast to post-reconstruction kinetic analysis, errors in poorly modelled regions may spatially propagate to regions which are well modelled. To reduce error propagation from erroneous model fits, we implement and evaluate a new approach to direct parameter estimation by incorporating a recently proposed kinetic modelling strategy within a direct 4D image reconstruction framework. The algorithm uses a secondary more general model to allow a less constrained model fit in regions where the kinetic model does not accurately describe the underlying kinetics. A portion of the residuals then is adaptively included back into the image whilst preserving the primary model characteristics in other well modelled regions using a penalty term that trades off the models. Using fully 4D simulations based on dynamic [(15)O]H2O datasets, we demonstrate reduction in propagation-related bias for all kinetic parameters. Under noisy conditions, reductions in bias due to propagation are obtained at the cost of increased noise, which in turn results in increased bias and variance of the kinetic parameters. This trade-off reflects the challenge of separating the residuals arising from poor kinetic modelling fits from the residuals arising purely from noise. Nonetheless, the overall root mean square error is reduced in most regions and parameters. Using the adaptive 4D image reconstruction improved model fits can be obtained in poorly modelled regions, leading to reduced errors potentially propagating to regions of interest which the primary biologic model accurately describes. The proposed methodology, however, depends on the secondary model and choosing an optimal model on the residual space is critical in improving model fits.

K-edge ratio method for identification of multiple nanoparticulate contrast agents by spectral CT imaging.

OBJECTIVE: Recently introduced energy-sensitive X-ray CT makes it feasible to discriminate different nanoparticulate contrast materials. The purpose of this work is to present a K-edge ratio method for differentiating multiple simultaneous contrast agents using spectral CT. METHODS: The ratio of two images relevant to energy bins straddling the K-edge of the materials is calculated using an analytic CT simulator. In the resulting parametric map, the selected contrast agent regions can be identified using a thresholding algorithm. The K-edge ratio algorithm is applied to spectral images of simulated phantoms to identify and differentiate up to four simultaneous and targeted CT contrast agents. RESULTS: We show that different combinations of simultaneous CT contrast agents can be identified by the proposed K-edge ratio method when energy-sensitive CT is used. In the K-edge parametric maps, the pixel values for biological tissues and contrast agents reach a maximum of 0.95, whereas for the selected contrast agents, the pixel values are larger than 1.10. The number of contrast agents that can be discriminated is limited owing to photon starvation. For reliable material discrimination, minimum photon counts corresponding to 140 kVp, 100 mAs and 5-mm slice thickness must be used. CONCLUSION: The proposed K-edge ratio method is a straightforward and fast method for identification and discrimination of multiple simultaneous CT contrast agents. ADVANCES IN KNOWLEDGE: A new spectral CT-based algorithm is proposed which provides a new concept of molecular CT imaging by non-iteratively identifying multiple contrast agents when they are simultaneously targeting different organs.

Contourlet-based active contour model for PET image segmentation.

PURPOSE: PET-guided radiation therapy treatment planning, clinical diagnosis, assessment of tumor growth, and therapy response rely on the accurate delineation of the tumor volume and quantification of tracer uptake. Most PET image segmentation techniques proposed thus far are suboptimal in the presence of heterogeneity of tracer uptake within the lesion. This work presents an active contour model approach based on the method of Chan and Vese ["Active contours without edges," IEEE Trans. Image Process. 10, 266-277 (2001)] designed to take into account the high level of statistical uncertainty (noise) and to handle the heterogeneity of tumor uptake typically present in PET images. METHODS: In the proposed method, the fitting terms in the Chan-Vese formulation are modified by introducing new input images, including the smoothed version of the original image using anisotropic diffusion filtering (ADF) and the contourlet transform of the image. The advantage of utilizing ADF for image smoothing is that it avoids blurring the object's edges and preserves the average activity within a region, which is important for accurate PET quantification. Moreover, incorporating the contourlet transform of the image into the fitting terms makes the energy functional more effective in directing the evolving curve toward the object boundaries due to the enhancement of the tumor-to-background ratio (TBR). The proper choice of the energy functional parameters has been formulated by making a clear consensus based on tumor heterogeneity and TBR levels. This cautious parameter selection leads to proper handling of heterogeneous lesions. The algorithm was evaluated using simulated phantom and clinical studies, where the ground truth and histology, respectively, were available for accurate quantitative analysis of the segmentation results. The proposed technique was also compared to a number of previously reported image segmentation techniques. RESULTS: The results were quantitatively analyzed using three evaluation metrics, including the spatial overlap index (SOI), the mean relative error (MRE), and the mean classification error (MCE). Although the performance of the proposed method was analogous to other methods for some datasets, overall the proposed algorithm outperforms all other techniques. In the largest clinical group comprising nine datasets, the proposed approach improved the SOI from 0.41±0.14 obtained using the best-performing algorithm to 0.54±0.12 and reduced the MRE from 54.23±103.29 to 0.19±16.63 and the MCE from 112.86±69.07 to 60.58±18.43. CONCLUSIONS: The proposed segmentation technique is superior to other representative segmentation techniques in terms of highest overlap between the segmented volume and the ground truth∕histology and minimum relative and classification errors. Therefore, the proposed active contour model can result in more accurate tumor volume delineation from PET images.

Evaluation of whole-body MR to CT deformable image registration.

Multimodality image registration plays a crucial role in various clinical and research applications. The aim of this study is to present an optimized MR to CT whole-body deformable image registration algorithm and its validation using clinical studies. A 3D intermodality registration technique based on B-spline transformation was performed using optimized parameters of the elastix package based on the Insight Toolkit (ITK) framework. Twenty-eight (17 male and 11 female) clinical studies were used in this work. The registration was evaluated using anatomical landmarks and segmented organs. In addition to 16 anatomical landmarks, three key organs (brain, lungs, and kidneys) and the entire body volume were segmented for evaluation. Several parameters--such as the Euclidean distance between anatomical landmarks, target overlap, Dice and Jaccard coefficients, false positives and false negatives, volume similarity, distance error, and Hausdorff distance--were calculated to quantify the quality of the registration algorithm. Dice coefficients for the majority of patients (> 75%) were in the 0.8-1 range for the whole body, brain, and lungs, which satisfies the criteria to achieve excellent alignment. On the other hand, for kidneys, Dice coefficients for volumes of 25% of the patients meet excellent volume agreement requirement, while the majority of patients satisfy good agreement criteria (> 0.6). For all patients, the distance error was in 0-10 mm range for all segmented organs. In summary, we optimized and evaluated the accuracy of an MR to CT deformable registration algorithm. The registered images constitute a useful 3D whole-body MR-CT atlas suitable for the development and evaluation of novel MR-guided attenuation correction procedures on hybrid PET-MR systems.

[Human genetics of tuberculosis]. / Génétique humaine de la tuberculose.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major public health problem worldwide, resulting in 8.7 million new cases and 1.4 million deaths each year. One third of the world's population is exposed to M. tuberculosis and, after exposure, most, but not all, individuals become infected. Among infected subjects, only a minority (∼10%) will eventually develop clinical disease, which is typically either a primary, often extra-pulmonary, TB in children, or a reactivation, pulmonary TB in adults. Considerable genetic epidemiological evidence has accumulated to support a major role for human genetic factors in the development of TB. Numerous association studies with various candidate genes have been conducted in pulmonary TB, with very few consistent results. Recent genome-wide association studies revealed only a modest role for two inter-genic polymorphisms. However, a first major locus for pulmonary TB was mapped to chromosome 8q12-q13 in a Moroccan population after a genome-wide linkage screen. Using a similar strategy, two other major loci controlling TB infection were recently identified. While the precise identification of these major genes is ongoing, the other fascinating observation of these last years was the demonstration that TB can also reflect a Mendelian predisposition. Following the findings obtained in the syndrome of Mendelian susceptibility to mycobacterial diseases, several children with complete IL-12Rß1 deficiency, were found to have severe TB as their sole phenotype. Overall, these recent findings provide the proof of concept that the human genetics of TB involves a continuous spectrum from Mendelian to complex predisposition with intermediate major gene involvement. The understanding of the molecular genetic basis of TB will have fundamental immunological and medical implications, in particular for the development of new vaccines and treatments.

MRI-guided attenuation correction in whole-body PET/MR: assessment of the effect of bone attenuation.

OBJECTIVE: Hybrid PET/MRI presents many advantages in comparison with its counterpart PET/CT in terms of improved soft-tissue contrast, decrease in radiation exposure, and truly simultaneous and multi-parametric imaging capabilities. However, the lack of well-established methodology for MR-based attenuation correction is hampering further development and wider acceptance of this technology. We assess the impact of ignoring bone attenuation and using different tissue classes for generation of the attenuation map on the accuracy of attenuation correction of PET data. METHODS: This work was performed using simulation studies based on the XCAT phantom and clinical input data. For the latter, PET and CT images of patients were used as input for the analytic simulation model using realistic activity distributions where CT-based attenuation correction was utilized as reference for comparison. For both phantom and clinical studies, the reference attenuation map was classified into various numbers of tissue classes to produce three (air, soft tissue and lung), four (air, lungs, soft tissue and cortical bones) and five (air, lungs, soft tissue, cortical bones and spongeous bones) class attenuation maps. RESULTS: The phantom studies demonstrated that ignoring bone increases the relative error by up to 6.8% in the body and up to 31.0% for bony regions. Likewise, the simulated clinical studies showed that the mean relative error reached 15% for lesions located in the body and 30.7% for lesions located in bones, when neglecting bones. These results demonstrate an underestimation of about 30% of tracer uptake when neglecting bone, which in turn imposes substantial loss of quantitative accuracy for PET images produced by hybrid PET/MRI systems. CONCLUSION: Considering bones in the attenuation map will considerably improve the accuracy of MR-guided attenuation correction in hybrid PET/MR to enable quantitative PET imaging on hybrid PET/MR technologies.

A novel energy mapping approach for CT-based attenuation correction in PET.

PURPOSE: Dual-energy CT (DECT) is arguably the most accurate energy mapping technique in CT-based attenuation correction (CTAC) implemented on hybrid PET/CT systems. However, this approach is not attractive for clinical use owing to increased patient dose. The authors propose a novel energy mapping approach referred to as virtual DECT (VDECT) taking advantage of the DECT formulation but using CT data acquired at a single energy (kV(P)). For this purpose, the CT image acquired at one energy is used to generate the CT image at a second energy using calculated kV(P) conversion curves derived from phantom studies. METHODS: The attenuation map (µ-map) at 511 keV was generated for the XCAT phantom and clinical studies using the bilinear, DECT, and VDECT techniques. The generated µ-maps at 511 keV are compared to the reference derived from the XCAT phantom serving as ground truth. PET data generated from a predefined activity map for the XCAT phantom were then corrected for attenuation using µ-maps generated using the different energy mapping approaches. In addition, the generated µ-maps using the above described methods for a cylindrical polyethylene phantom containing different concentrations of K(2)HPO(4) in water were compared to actual attenuation coefficients. Likewise, CT images of five clinical whole-body studies were used to generate µ-maps using the various energy-mapping approaches were compared with µ-maps acquired at 511 keV using (68)Ge/(68)Ga rod sources for the clinical studies. RESULTS: The results of phantom studies demonstrate that the proposed method is more accurate than the bilinear technique. All three µ-maps yielded almost similar results for soft and lung tissues whereas for bone tissues, the DECT and the VDECT methods produced a much smaller mean relative difference (3.0% and 2.8%, respectively) than the bilinear approach (11.8%). Likewise, the comparison of PET images corrected for attenuation using the various methods showed that the proposed method provides better accuracy (6.5%) than the bilinear method (13.4%). Clinical studies further demonstrated that, compared to the bilinear method, the VDECT approach has better agreement for bony structures with the DECT technique (1.5% versus 8.9%) and transmission scanning (8.8% versus 17.7%). CONCLUSIONS: It was concluded that the proposed method outperforms the bilinear method especially in bony structures. Further evaluation using a large clinical PET/CT database is underway to evaluate the potential of the technique in a clinical setting.

Impact of miscentering on patient dose and image noise in x-ray CT imaging: phantom and clinical studies.

The operation of the bowtie filter in x-ray CT is correct if the object being scanned is properly centered in the scanner's field-of-view. Otherwise, the dose delivered to the patient and image noise will deviate from optimal setting. We investigate the effect of miscentering on image noise and surface dose on three commercial CT scanners. Six cylindrical phantoms with different size and material were scanned on each scanner. The phantoms were positioned at 0, 2, 4 and 6 cm below the isocenter of the scanner's field-of-view. Regression models of surface dose and noise were produced as a function of miscentering magnitude and phantom's size. 480 patients were assessed using the calculated regression models to estimate the influence of patient miscentering on image noise and patient surface dose in seven imaging centers. For the 64-slice CT scanner, the maximum increase of surface dose using the CTDI-32 phantom was 13.5%, 33.3% and 51.1% for miscenterings of 2, 4 and 6 cm, respectively. The analysis of patients' scout scans showed miscentering of 2.2 cm in average below the isocenter. An average increase of 23% and 7% was observed for patient dose and image noise, respectively. The maximum variation in patient miscentering derived from the comparison of imaging centers using the same scanner was 1.6 cm. Patient miscentering may substantially increase surface dose and image noise. Therefore, technologists are strongly encouraged to pay greater attention to patient centering.

SU-D-217A-03: Nuclear Medicine Uniformity Assessment Using 2D Noise Power Spectrum.

PURPOSE: Nuclear medicine quality control programs require daily evaluation for the presence of potential non-uniformities by commonly utilizing a traditional pixel value-based assessment (Integral CFOVUniformity). While this method effectively captures regional non- uniformities in the image, it does not adequately reflect subtle periodic structures that are visually apparent and clinically unacceptable, therefore requiring the need for additional visual inspection of the image. The goal of this project was to develop a new uniformity assessment metric by targetingstructural patterns and more closely correlating with visual inspection. METHODS: The new quantitative uniformity assessment metric is based on the 2D Noise Power Spectrum (NPS). A full 2D NPS was performed on each image. The NPS was thresholded to remove quantum noise and further filtered by the visual response function. A score, the Structure Noise Index (SNI), was then applied to each based on the average magnitude of the structured noise in the processed image. To verify the validity of the new metric, 50 daily uniformity images with varying degrees of visual structured and non-structured non-uniformity were scored by 5 expert nuclear medicine physicists. The correlation between the visual score and SNI were assessed. The Integral CFOV was also compared against the visual score. RESULTS: Our new SNI assessment metric compared to the Integral CFOV showed in increase in sensitivity from 67% to 100% in correctly identifying structured non-uniformities. The overall positive predictive value also increased from 55% to 72%. CONCLUSIONS: Our new uniformity metric correlates much more closely with visual assessment of structured non- uniform NM images than the traditional pixel-based method. Using this new metric in conjunction with the traditional pixel value-based assessment will allow a more accurate quantitative assessment of nuclear medicineuniformity.