Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET

Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm3 and 64 mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both simulated and acquired datasets led to similar results and conclusions as far as the performance of segmentation algorithms under evaluation is concerned.     

Improved quantitation for PET/CT image reconstruction with system modeling and anatomical priors

Accurate quantitation of positron emission tomography (PET) tracer uptake levels in tumors is important for staging and monitoring response to treatment. Quantitative accuracy in PET is particularly poor for small tumors because of system partial volume errors and smoothing operations. This work proposes a reconstruction algorithm to reduce the quantitative errors due to limited system resolution and due to necessary image noise reduction. We propose a method for finding and using the detection system response in the projection matrix of a statistical reconstruction algorithm. In addition, we use aligned anatomical information, available in PET/CT scanners, to govern the penalty term applied during each image update. These improvements are combined with Fourier rebinning in a clinically feasible algorithm for reconstructing fully three-dimensional PET data. Results from simulation and measured studies show improved quantitation of tumor values in terms of bias and variance across multiple tumor sizes and activity levels with the proposed method. At common clinical image noise levels for the detection task, the proposed method reduces the error in maximum tumor values by 11% compared to filtered back-projection and 5% compared to conventional iterative methods.     

基于RoughSet的特征集融合PET/CT肺部肿瘤CAD模型

针对肺部肿瘤PET/CT感兴趣区域(ROI)在高维特征表示下存在着特征相关和维数灾难问题,提出了一种基于粗糙集特征集融合的PET/CT肺部肿瘤CAD模型。首先提取肺部肿瘤ROI的8维形状特征、7维灰度特征、3维Tamura纹理特征、56维GLCM特征和24维频域特征,得到98维特征矢量;然后基于遗传算法的知识约简方法和基于属性重要度的启发式算法对提取的特征集合分别进行特征级融合得到特征子集G1、G2、G3,A1、A2、A3,降低特征矢量的维数;再次利用网格寻优算法优化核函数的SVM作为分类器分别进行融合前和融合后的分类识别比较,基于遗传算法的特征集融合和基于属性重要度的特征集融合的分类识别比较2组实验;最后以2 000幅肺部肿瘤的PET/CT图像为原始数据,采用基于粗糙集特征集融合的肺部肿瘤PET/CT计算机辅助诊断模型对肺部肿瘤进行辅助诊断。实验结果表明,经过粗糙集特征集融合的肺部肿瘤诊断识别方法能有效提高肺部肿瘤的诊断正确率,一定程度上降低了特征之间的相关性。     

融合图像放疗靶区定位精度的检验和初步临床结果

目的：探讨以图像融合技术为基础的肿瘤三维适形放疗靶区定位精度的检验及依据融合图像放疗靶区的确定与单纯CT影像放疗靶区确定的初步临床结果。方法：利用定制的模体分别行CT、MRI和PET成像，进行CT与MRI，CT与PET融合。检验融合后定制标记点的定位精度。对3例特殊病例分别以单纯CT图像为基础和融合图像为基础，进行三维适形放疗靶区认定，对不同医生之间和同一医生在不同时间，放疗靶区定义情况进行对照分析。结果：MRI／CT融合图像总定位精度小于2mm，PET／CT图像融合图像融合精度情况（包括同机融合和异机融合），采用不同的融合算法。定位精度有显著差异（P〈0．01，t=5．385）。单纯利用CT图像进行靶区的定义，不同医生之间，在不同的时间存在差异（P〈0．05），而采用融合技术可减少他们的争议和差异。结论：利用多模式图像融合可以提高靶区定义的准确性．有利于三维适形精确放射治疗。     

The development and testing of a digital PET phantom for the evaluation of tumor volume segmentation techniques

Methods for accurate tumor volume segmentation of positron emission tomography (PET) images have been under investigation in recent years partly as a result of the increased use of PET in radiation treatment planning (RTP). None of the developed automated or semiautomated segmentation methods, however, has been shown reliable enough to be regarded as the standard. One reason for this is that there is no source of well characterized and reliable test data for evaluating such techniques. The authors have constructed a digital tumor phantom to address this need. The phantom was created using the Zubal phantom as input to the SimSET software used for PET simulations. Synthetic tumors were placed in the lung of the Zubal phantom to provide the targets for segmentation. The authors concentrated on the lung, since much of the interest to include PET in RTP is for nonsmall cell lung cancer. Several tests were performed on the phantom to ensure its close resemblance to clinical PET scans. The authors measured statistical quantities to compare image intensity distributions from regions-of-interest (ROIs) placed in the liver, the lungs, and tumors in phantom and clinical reconstructions. Using ROIs they also made measurements of autocorrelation functions to ensure the image texture is similar in clinical and phantom data. The authors also compared the intensity profile and appearance of real and simulated uniform activity spheres within uniform background. These measurements, along with visual comparisons of the phantom with clinical scans, indicate that the simulated phantom mimics reality quite well. Finally, they investigate and quantify the relationship between the threshold required to segment a tumor and the inhomogeneity of the tumor's image intensity distribution. The tests and various measurements performed in this study demonstrate how the phantom can offer a reliable way of testing and investigating tumor volume segmentation in PET.     

基于体模和反卷积算法的PET 呼吸运动模糊校正

呼吸运动会导致PET图像出现运动模糊,影响肿瘤诊断的准确性和放射治疗的精确性。本研究结合高频正弦振动和反卷积技术提出了一种校正PET图像运动模糊的方法。高频正弦振动用于模拟肺部肿瘤的呼吸运动。首先使用雷当变换从运动模糊图像的伪倒谱中识别模糊运动方向,然后将运动模糊图像的模糊方向旋转到垂直模糊方向,利用双谱识别模糊幅度,最后使用Richardson-Lucy迭代算法对运动模糊图像进行校正。体模实验显示,通过校正后PET图像估算出的肿瘤体积和肿瘤内平均标准摄取值（SUV）更接近真实值,与未校正的模糊运动图像相比,其校正后的肿瘤体积误差从40%下降到10%,SUV误差从28%下降到4%。结果表明所使用的方法能够有效校正呼吸运动模糊,使肿瘤诊断更加准确。     

Evaluation of Novel Genetic Algorithm Generated Schemes for Positron Emission Tomography (PET)/Magnetic Resonance Imaging (MRI) Image Fusion

The use and benefits of a multimodality approach in the context of breast cancer imaging are discussed. Fusion techniques that allow multiple images to be viewed simultaneously are discussed. Many of these fusion techniques rely on the use of color tables. A genetic algorithm that generates color tables that have desired properties such as satisfying the order principle, the rows, and columns principle, have perceivable uniformity and have maximum contrast is introduced. The generated 2D color tables can be used for displaying fused datasets. The advantage the proposed method has over other techniques is the ability to consider a much larger set of possible color tables, ensuring that the best one is found. We asked radiologists to perform a set of tasks reading fused PET/MRI breast images obtained using eight different fusion techniques. This preliminary study clearly demonstrates the need and benefit of a joint display by estimating the inaccuracies incurred when using a side-by-side display. The study suggests that the color tables generated by the genetic algorithm are good choices for fusing MR and PET images. It is interesting to note that popular techniques such as the Fire/Gray and techniques based on the HSV color space, which are prevalent in the literature and clinical practice, appear to give poorer performance.     

A methodology for generating normal and pathological brain perfusion SPECT images for evaluation of MRI/SPECT fusion methods: application in epilepsy

Quantitative evaluation of brain MRI/SPECT fusion methods for normal and in particular pathological datasets is difficult, due to the frequent lack of relevant ground truth. We propose a methodology to generate MRI and SPECT datasets dedicated to the evaluation of MRI/SPECT fusion methods and illustrate the method when dealing with ictal SPECT. The method consists in generating normal or pathological SPECT data perfectly aligned with a high-resolution 3D T1-weighted MRI using realistic Monte Carlo simulations that closely reproduce the response of a SPECT imaging system. Anatomical input data for the SPECT simulations are obtained from this 3D T1-weighted MRI, while functional input data result from an inter-individual analysis of anatomically standardized SPECT data. The method makes it possible to control the 'brain perfusion' function by proposing a theoretical model of brain perfusion from measurements performed on real SPECT images. Our method provides an absolute gold standard for assessing MRI/SPECT registration method accuracy since, by construction, the SPECT data are perfectly registered with the MRI data. The proposed methodology has been applied to create a theoretical model of normal brain perfusion and ictal brain perfusion characteristic of mesial temporal lobe epilepsy. To approach realistic and unbiased perfusion models, real SPECT data were corrected for uniform attenuation, scatter and partial volume effect. An anatomic standardization was used to account for anatomic variability between subjects. Realistic simulations of normal and ictal SPECT deduced from these perfusion models are presented. The comparison of real and simulated SPECT images showed relative differences in regional activity concentration of less than 20% in most anatomical structures, for both normal and ictal data, suggesting realistic models of perfusion distributions for evaluation purposes. Inter-hemispheric asymmetry coefficients measured on simulated data were found within the range of asymmetry coefficients measured on corresponding real data. The features of the proposed approach are compared with those of other methods previously described to obtain datasets appropriate for the assessment of fusion methods.     

PET/CT Fusion Viewing Software for Use with Picture Archiving and Communication Systems

We developed positron emission tomography (PET)/computed tomography (CT) viewing software (PETviewer) that can display co-registered PET and CT images obtained by PET/CT and stored on picture archiving and communication systems (PACS). PETviewer has tools for presetting windows for CT display; control bars for PET window level; zoom, pan, and pseudo-color functions; and allows the user to draw a rectangular region of interest (ROI) for standardized uptake value (SUV) measurement. SUV was calculated using PET DICOM header information and the pixel intensity in PETviewer. Reconstructed datasets of PET/CT and maximum intensity projection (MIP) of the PET images were transferred and archived in PACS. Phantom experiments were performed to evaluate the validity of image fusion. PET/CT images were displayed on an independent window in PACS. Transaxial PET images were reformatted as sagittal and coronal PET images, which were displayed with the corresponding CT and PET/CT fusion images with adjustable color and transparency. Transaxial, sagittal, and coronal PET images corresponding to the location of the cursor were shown using cine display of MIP images. All images were displayed in PETviewer within 20 s on a personal computer for PACS, which was equipped with a P4, 1.3-GHz CPU, and 512 Mb of RAM. We could measure maximum and mean SUV in a ROI using PETviewer. Transaxial fused images of patients and phantoms showed excellent registration and fusion of PET and CT images in the X and Y directions. PETviewer provided very useful clinical tools for assessing PET/CT images on PACS and should assist in maximizing the benefits derived from PET/CT imaging.     

PET/CT image registration: preliminary tests for its application to clinical dosimetry in radiotherapy

The quality of dosimetry in radiotherapy treatment requires the accurate delimitation of the gross tumor volume. This can be achieved by complementing the anatomical detail provided by CT images through fusion with other imaging modalities that provide additional metabolic and physiological information. Therefore, use of multiple imaging modalities for radiotherapy treatment planning requires an accurate image registration method. This work describes tests carried out on a Discovery LS positron emission/computed tomography (PET/CT) system by General Electric Medical Systems (GEMS), for its later use to obtain images to delimit the target in radiotherapy treatment. Several phantoms have been used to verify image correlation, in combination with fiducial markers, which were used as a system of external landmarks. We analyzed the geometrical accuracy of two different fusion methods with the images obtained with these phantoms. We first studied the fusion method used by the PET/CT system by GEMS (hardware fusion) on the basis that there is satisfactory coincidence between the reconstruction centers in CT and PET systems; and secondly the fiducial fusion, a registration method, by means of least-squares fitting algorithm of a landmark points system. The study concluded with the verification of the centroid position of some phantom components in both imaging modalities. Centroids were estimated through a calculation similar to center-of-mass, weighted by the value of the CT number and the uptake intensity in PET. The mean deviations found for the hardware fusion method were: deltax/ +/-sigma = 3.3 mm +/- 1.0 mm and /deltax/ +/-sigma = 3.6 mm +/- 1.0 mm. These values were substantially improved upon applying fiducial fusion based on external landmark points: /deltax/ +/-sigma = 0.7 mm +/- 0.8 mm and /deltax/ +/-sigma = 0.3 mm 1.7 mm. We also noted that differences found for each of the fusion methods were similar for both the axial and helical CT image acquisition protocols.     

Partial volume effect correction in PET using regularized iterative deconvolution with variance control based on local topology

Correcting positron emission tomography (PET) images for the partial volume effect (PVE) due to the limited resolution of PET has been a long-standing challenge. Various approaches including incorporation of the system response function in the reconstruction have been previously tested. We present a post-reconstruction PVE correction based on iterative deconvolution using a 3D maximum likelihood expectation-maximization (MLEM) algorithm. To achieve convergence we used a one step late (OSL) regularization procedure based on the assumption of local monotonic behavior of the PET signal following Alenius et al. This technique was further modified to selectively control variance depending on the local topology of the PET image. No prior 'anatomic' information is needed in this approach. An estimate of the noise properties of the image is used instead. The procedure was tested for symmetric and isotropic deconvolution functions with Gaussian shape and full width at half-maximum (FWHM) ranging from 6.31 mm to infinity. The method was applied to simulated and experimental scans of the NEMA NU 2 image quality phantom with the GE Discovery LS PET/CT scanner. The phantom contained uniform activity spheres with diameters ranging from 1 cm to 3.7 cm within uniform background. The optimal sphere activity to variance ratio was obtained when the deconvolution function was replaced by a step function few voxels wide. In this case, the deconvolution method converged in approximately 3-5 iterations for most points on both the simulated and experimental images. For the 1 cm diameter sphere, the contrast recovery improved from 12% to 36% in the simulated and from 21% to 55% in the experimental data. Recovery coefficients between 80% and 120% were obtained for all larger spheres, except for the 13 mm diameter sphere in the simulated scan (68%). No increase in variance was observed except for a few voxels neighboring strong activity gradients and inside the largest spheres. Testing the method for patient images increased the visibility of small lesions in non-uniform background and preserved the overall image quality. Regularized iterative deconvolution with variance control based on the local properties of the PET image and on estimated image noise is a promising approach for partial volume effect corrections in PET.     

Design of respiration averaged CT for attenuation correction of the PET data from PET/CT

Our previous patient studies have shown that the use of respiration averaged computed tomography (ACT) for attenuation correction of the positron emission tomography (PET) data from PET/CT reduces the potential misalignment in the thorax region by matching the temporal resolution of the CT to that of the PET. In the present work, we investigated other approaches of acquiring ACT in order to reduce the CT dose and to improve the ease of clinical implementation. Four-dimensional CT (4DCT) data sets for ten patients (17 lung/esophageal tumors) were acquired in the thoracic region immediately after the routine PET/CT scan. For each patient, multiple sets of ACTs were generated based on both phase image averaging (phase approach) and fixed cine duration image averaging (cine approach). In the phase approach, the ACTs were calculated from CT images corresponding to the significant phases of the respiratory cycle: ACT(050phs) from end-inspiration (0%) and end-expiration (50%), ACT(2070phs) from mid-inspiration (20%) and mid-expiration (70%), ACT(4phs) from 0%, 20%, 50% and 70%, and ACT(10phs) from all ten phases, which was the original approach. In the cine approach, which does not require 4DCT, the ACTs were calculated based on the cine images from cine durations of 1 to 6 s at 1 s increments. PET emission data for each patient were attenuation corrected with each of the above mentioned ACTs and the tumor maximum standard uptake value (SUVmax), average SUV (SUVavg), and tumor volume measurements were compared. Percent differences were calculated between PET data corrected with various ACTs and that corrected with ACT(10phs). In the phase approach, the ACT(10phs) can be approximated by the ACT(4phs) to within a mean percent difference of 2% in SUV and tumor volume measurements. In cine approach, ACT(10phs) can be approximated to within a mean percent difference of 3% by ACTs computed from cine durations > or =3 s. Acquiring CT images only at the four significant phases for the ACT can reduce radiation dose to 1/3 of the current 4DCT dose; however, the implementation of this approach requires additional hardware that is not standard equipment on PET/CT scanners. In the cine approach, we recommend a duration of 6 +/- 1 s in order to include variations of respiratory patterns in a larger population. This approach can be easily implemented because cine acquisition mode is available on all GE PET/CT scanners. The CT dose in the cine approach can be reduced to approximately 5 mGy by using the lowest mA setting (10 mA), while still maintaining good quality CT data for PET attenuation correction. In our scanning protocol, the ACT is only acquired if respiration-induced misregistration is observed (determined before the PET scan is completed), and therefore patients do not receive unnecessary CT radiation dose.     

Tumor delineation using PET in head and neck cancers: threshold contouring and lesion volumes

Tumor boundary delineation using positron emission tomography (PET) is a promising tool for radiation therapy applications. In this study we quantify the uncertainties in tumor boundary delineation as a function of the reconstruction method, smoothing, and lesion size in head and neck cancer patients using FDG-PET images and evaluate the dosimetric impact on radiotherapy plans. FDG-PET images were acquired for eight patients with a GE Advance PET scanner. In addition, a 20 cm diameter cylindrical phantom with six FDG-filled spheres with volumes of 1.2 to 26.5 cm3 was imaged. PET emission scans were reconstructed with the OSEM and FBP algorithms with different smoothing parameters. PET-based tumor regions were delineated using an automatic contouring function set at progressively higher threshold contour levels and the resulting volumes were calculated. CT-based tumor volumes were also contoured by a physician on coregistered PET/CT patient images. The intensity value of the threshold contour level that returns 100% of the actual volume, I(V100), was measured. We generated intensity-modulated radiotherapy (IMRT) plans for an example head and neck patient, treating 66 Gy to CT-based gross disease and 54 Gy to nodal regions at risk, followed by a boost to the FDG-PET-based tumor. The volumes of PET-based tumors are a sensitive function of threshold contour level for all patients and phantom datasets. A 5% change in threshold contour level can translate into a 200% increase in volume. Phantom data indicate that I(V100) can be set as a fraction, f, of the maximum measured uptake. Fractional threshold values in the cylindrical water phantom range from 0.23 to 0.51. Both the fractional threshold and the threshold-volume curve are dependent on lesion size, with lesions smaller than approximately 5 cm3 displaying a more pronounced sensitivity and larger fractional threshold values. The threshold-volume curves and fractional threshold values also depend on the reconstruction algorithm and smoothing filter with more smoothing requiring a higher fractional threshold contour level. The threshold contour level affects the tumor size, and therefore the ultimate boost dose that is achievable with IMRT. In an example head and neck IMRT plan, the D95 of the planning target volume decreased from 7770 to 7230 cGy for 42% vs. 55% contour threshold levels. PET-based tumor volumes are strongly affected by the choice of threshold level. This can have a significant dosimetric impact. The appropriate threshold level depends on lesion size and image reconstruction parameters. These effects should be carefully considered when using PET contour and/or volume information for radiotherapy applications.     

基于PET/CT形变配准技术对放疗前后靶区变化的评估

PET图像提供的新陈代谢信息可用于判断放疗后肿瘤的复发区域,对于制订精确的放疗计划具有重要的临床意义。研究采用多分辨率形变配准的方法提取放疗前后CT图像的形变域,并将其作用于放疗前PET图像,与放疗后的PET图像相比较,通过设定图像中SUV 的阈值,判断勾画轮廓之间的重叠率,以获得图像中的高摄取区域,回顾性指导精确放疗。研究针对22例肺癌病例,实验结果显示放疗后残留的高代谢区域和放疗前GTV重叠较好:当阈值设定为SUV_max的70%、80%和90%时,对应的重叠率分别为(95.2±0.6)%、(96.6±3.4)%和100%;当阈值设定为SUV2.5和SUV5.0时,对应的重叠率为(86.0±6.6)%和(97.0±3.0)%。对氟代脱氧葡萄糖(FDG)高摄取区域的高重叠率表明病变区域在放疗前后的位置相对稳定,放疗后的残余肿瘤基本上位于放疗前靶区对FDG的摄取区域。初步实验结果证明,研究可用于判断靶区区域对放疗的反应,回顾性指导在放疗计划中,针对放疗后残余的靶区加大照射剂量,保护危及器官和组织,精确放疗。     

Hybrid PET/MR imaging of tumors using an oleanolic acid-conjugated nanoparticle

Research into multifunctional nanoparticles is focused on creating an agent for use in an all-in-one multimodal imaging system that includes diagnostic imaging, drug delivery, and therapeutic monitoring. We designed a new dual-modality tumor-targeting agent with a new tumor-targeting molecule, oleanolic acid (OA), which is derived from a natural compound and coupled with a macrocyclic chelating agent such as 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA), iron oxide nanoparticles (IONP), and radiolabeling components such as ⁶⁸Ga for dual-modality positron emission tomography (PET)/magnetic resonance imaging (MRI). We attempted to obtain fusion PET/MR images with the ⁶⁸Ga–NOTA–OA–IONP hybrid tumor-targeting imaging agent using colon cancer (HT-29) xenograft mice models. The HT-29 cancer cells showed high uptake of ⁶⁸Ga–NOTA–OA–IONP, which also had an inhibitory effect on the cells. Moreover, we obtained PET and MRI tumor images as well as fusion PET/MRI images of the tumors using ⁶⁸Ga–NOTA–OA–IONP. Therefore, the dual-modality cancer-targeting radiolabeled nanoparticle reported here is a potent imaging agent that is suitable for PET, MRI, and PET/MRI-based diagnosis of tumors; it also has the advantage of not only detecting tumor functionality, but also simultaneously aiding in tumor resolution.     

Model-based image reconstruction for four-dimensional PET

Positron emission tonography (PET) is useful in diagnosis and radiation treatment planning for a variety of cancers. For patients with cancers in thoracic or upper abdominal region, the respiratory motion produces large distortions in the tumor shape and size, affecting the accuracy in both diagnosis and treatment. Four-dimensional (4D) (gated) PET aims to reduce the motion artifacts and to provide accurate measurement of the tumor volume and the tracer concentration. A major issue in 4D PET is the lack of statistics. Since the collected photons are divided into several frames in the 4D PET scan, the quality of each reconstructed frame degrades as the number of frames increases. The increased noise in each frame heavily degrades the quantitative accuracy of the PET imaging. In this work, we propose a method to enhance the performance of 4D PET by developing a new technique of 4D PET reconstruction with incorporation of an organ motion model derived from 4D-CT images. The method is based on the well-known maximum-likelihood expectation-maximization (ML-EM) algorithm. During the processes of forward- and backward-projection in the ML-EM iterations, all projection data acquired at different phases are combined together to update the emission map with the aid of deformable model, the statistics is therefore greatly improved. The proposed algorithm was first evaluated with computer simulations using a mathematical dynamic phantom. Experiment with a moving physical phantom was then carried out to demonstrate the accuracy of the proposed method and the increase of signal-to-noise ratio over three-dimensional PET. Finally, the 4D PET reconstruction was applied to a patient case.     

Pixel-Feature Hybrid Fusion for PET/CT Images

Color blending is a popular display method for functional and anatomic image fusion. The underlay image is typically displayed in grayscale, and the overlay image is displayed in pseudo colors. This pixel-level fusion provides too much information for reviewers to analyze quickly and effectively and clutters the display. To improve the fusion image reviewing speed and reduce the information clutter, a pixel-feature hybrid fusion method is proposed and tested for PET/CT images. Segments of the colormap are selectively masked to have a few discrete colors, and pixels displayed in the masked colors are made transparent. The colormap thus creates a false contouring effect on overlay images and allows the underlay to show through to give contours an anatomic context. The PET standardized uptake value (SUV) is used to control where colormap segments are masked. Examples show that SUV features can be extracted and blended with CT image instantaneously for viewing and diagnosis, and the non-feature part of the PET image is transparent. The proposed pixel-feature hybrid fusion highlights PET SUV features on CT images and reduces display clutters. It is easy to implement and can be used as complementarily to existing pixel-level fusion methods.     

Matching PET and CT scans of the head and neck area: development of method and validation

Positron emission tomography (PET) provides important information on tumor biology, but lacks detailed anatomical information. Our aim in the present study was to develop and validate an automatic registration method for matching PET and CT scans of the head and neck. Three difficulties in achieving this goal are (1) nonrigid motions of the neck can hamper the use of automatic ridged body transformations; (2) emission scans contain too little anatomical information to apply standard image fusion methods; and (3) no objective way exists to quantify the quality of the match results. These problems are solved as follows: accurate and reproducible positioning of the patient was achieved by using a radiotherapy treatment mask. The proposed method makes use of the transmission rather than the emission scan. To obtain sufficient (anatomical) information for matching, two bed positions for the transmission scan were included in the protocol. A mutual information-based algorithm was used as a registration technique. PET and CT data were obtained in seven patients. Each patient had two CT scans and one PET scan. The datasets were used to estimate the consistency by matching PET to CT1, CT1 to CT2, and CT2 to PET using the full circle consistency test. It was found that using our method, consistency could be obtained of 4 mm and 1.3 degrees on average. The PET voxels used for registration were 5.15 mm, so the errors compared quite favorably with the voxel size. Cropping the images (removing the scanner bed from images) did not improve the consistency of the algorithm. The transmission scan, however, could potentially be reduced to a single position using this approach. In conclusion, the represented algorithm and validation technique has several features that are attractive from both theoretical and practical point of view, it is a user-independent, automatic validation technique for matching CT and PET scans of the head and neck, which gives the opportunity to compare different image enhancements.     

Respiratory motion correction for PET oncology applications using affine transformation of list mode data

Respiratory motion is a source of artefacts and reduced image quality in PET. Proposed methodology for correction of respiratory effects involves the use of gated frames, which are however of low signal-to-noise ratio. Therefore a method accounting for respiratory motion effects without affecting the statistical quality of the reconstructed images is necessary. We have implemented an affine transformation of list mode data for the correction of respiratory motion over the thorax. The study was performed using datasets of the NCAT phantom at different points throughout the respiratory cycle. List mode data based PET simulated frames were produced by combining the NCAT datasets with a Monte Carlo simulation. Transformation parameters accounting for respiratory motion were estimated according to an affine registration and were subsequently applied on the original list mode data. The corrected and uncorrected list mode datasets were subsequently reconstructed using the one-pass list mode EM (OPL-EM) algorithm. Comparison of corrected and uncorrected respiratory motion average frames suggests that an affine transformation in the list mode data prior to reconstruction can produce significant improvements in accounting for respiratory motion artefacts in the lungs and heart. However, the application of a common set of transformation parameters across the imaging field of view does not significantly correct the respiratory effects on organs such as the stomach, liver or spleen.     

PET image reconstruction with anatomical edge guided level set prior

Acquiring both anatomical and functional images during one scan, PET/CT systems improve the ability to detect and localize abnormal uptakes. In addition, CT images provide anatomical boundary information that can be used to regularize positron emission tomography (PET) images. Here we propose a new approach to maximum a posteriori reconstruction of PET images with a level set prior guided by anatomical edges. The image prior models both the smoothness of PET images and the similarity between functional boundaries in PET and anatomical boundaries in CT. Level set functions (LSFs) are used to represent smooth and closed functional boundaries. The proposed method does not assume an exact match between PET and CT boundaries. Instead, it encourages similarity between the two boundaries, while allowing different region definition in PET images to accommodate possible signal and position mismatch between functional and anatomical images. While the functional boundaries are guaranteed to be closed by the LSFs, the proposed method does not require closed anatomical boundaries and can utilize incomplete edges obtained from an automatic edge detection algorithm. We conducted computer simulations to evaluate the performance of the proposed method. Two digital phantoms were constructed based on the Digimouse data and a human CT image, respectively. Anatomical edges were extracted automatically from the CT images. Tumors were simulated in the PET phantoms with different mismatched anatomical boundaries. Compared with existing methods, the new method achieved better bias-variance performance. The proposed method was also applied to real mouse data and achieved higher contrast than other methods.