Mitigating differential baseline shifts in locally advanced lung cancer patients using an average anatomy model.

BACKGROUND AND PURPOSE: Differential baseline shifts between primary tumor and involved lymph nodes in locally advanced lung cancer patients compromise the accuracy of radiotherapy. The purpose of this study was to evaluate the performance of an average anatomy model (AAM) derived from repeat imaging and deformable registration to reduce these geometrical uncertainties. METHODS AND MATERIALS: An in-house implementation of a B-Spline deformable image registration (DIR) algorithm was first validated using three different validation approaches: (a) a circle method to test the consistency of the DIR, (b) fiducial marker target registration error, and (c) the recovery of a known deformation vector field (DVF). Subsequently, AAM was generated by first averaging five DVFs resulting from cone beam CT (CBCT) to planning CT (pCT) DIR and second by applying the inverse of the average DVF to the pCT. The proposed method was evaluated on 15 locally advanced lung cancer patients receiving daily motion compensated CBCT and a repeat CT (rCT) for adaptive radiotherapy. Reduction of systematic baseline shifts of the primary tumor were quantified for the fractions used to build the AAM as well as over the whole treatment and compared to the performance of the rCT. RESULTS: The deformable registration accuracy was ≤ 2 mm vector length for the first two validation methods and about 3 mm for the third method. The systematic baseline shifts over the five fractions prior to the rCT used to build the AAM reduced from 5.9 mm vector length relative to the pCT to 2.3 and 4.2 mm relative to the AAM and rCT, respectively. The overall systematic errors in the left-right, cranio-caudal, and anterior-posterior directions were [3.4, 3.8, 3.3] mm, [2.3, 2.9, 2.6] mm, and [2.3, 3.1, 2.7] mm for the pCT, AAM, and rCT, respectively. CONCLUSIONS: The AAM mitigates systematic errors occurring during treatment due to differential baseline shifts between the primary tumor and involved lymph nodes similar to (or even better than) rCT. The superior performance of the AAM in terms of the systematic error derived from the initial fractions indicates that further analysis of the optimum intervention time is required. This model has the potential to be used as an efficient and accurate alternative for rCT in adaptive radiotherapy of locally advanced lung cancer patients, obviating the need for rescanning and recontouring.

Assessment of metal artifact reduction methods in pelvic CT.

PURPOSE: Metal artifact reduction (MAR) produces images with improved quality potentially leading to confident and reliable clinical diagnosis and therapy planning. In this work, the authors evaluate the performance of five MAR techniques for the assessment of computed tomography images of patients with hip prostheses. METHODS: Five MAR algorithms were evaluated using simulation and clinical studies. The algorithms included one-dimensional linear interpolation (LI) of the corrupted projection bins in the sinogram, two-dimensional interpolation (2D), a normalized metal artifact reduction (NMAR) technique, a metal deletion technique, and a maximum a posteriori completion (MAPC) approach. The algorithms were applied to ten simulated datasets as well as 30 clinical studies of patients with metallic hip implants. Qualitative evaluations were performed by two blinded experienced radiologists who ranked overall artifact severity and pelvic organ recognition for each algorithm by assigning scores from zero to five (zero indicating totally obscured organs with no structures identifiable and five indicating recognition with high confidence). RESULTS: Simulation studies revealed that 2D, NMAR, and MAPC techniques performed almost equally well in all regions. LI falls behind the other approaches in terms of reducing dark streaking artifacts as well as preserving unaffected regions (p < 0.05). Visual assessment of clinical datasets revealed the superiority of NMAR and MAPC in the evaluated pelvic organs and in terms of overall image quality. CONCLUSIONS: Overall, all methods, except LI, performed equally well in artifact-free regions. Considering both clinical and simulation studies, 2D, NMAR, and MAPC seem to outperform the other techniques.

Combined fuzzy logic and random walker algorithm for PET image tumor delineation.

PURPOSE: The random walk (RW) technique serves as a powerful tool for PET tumor delineation, which typically involves significant noise and/or blurring. One challenging step is hard decision-making in pixel labeling. Fuzzy logic techniques have achieved increasing application in edge detection. We aimed to combine the advantages of fuzzy edge detection with the RW technique to improve PET tumor delineation. METHODS: A fuzzy inference system was designed for tumor edge detection from RW probabilities. Three clinical PET/computed tomography datasets containing 12 liver, 13 lung, and 18 abdomen tumors were analyzed, with manual expert tumor contouring as ground truth. The standard RW and proposed combined method were compared quantitatively using the dice similarity coefficient, the Hausdorff distance, and the mean standard uptake value. RESULTS: The dice similarity coefficient of the proposed method versus standard RW showed significant mean improvements of 21.0±7.2, 12.3±5.8, and 18.4%±6.1% for liver, lung, and abdominal tumors, respectively, whereas the mean improvements in the Hausdorff distance were 3.6±1.4, 1.3±0.4, 1.8±0.8 mm, and the mean improvements in SUVmean error were 15.5±6.3, 11.7±8.6, and 14.1±6.8% (all P's<0.001). For all tumor sizes, the proposed method outperformed the RW algorithm. Furthermore, tumor edge analysis demonstrated further enhancement of the performance of the algorithm, relative to the RW method, with decreasing edge gradients. CONCLUSION: The proposed technique improves PET lesion delineation at different tumor sites. It depicts greater effectiveness in tumors with smaller size and/or low edge gradients, wherein most PET segmentation algorithms encounter serious challenges. Favorable execution time and accurate performance of the algorithm make it a great tool for clinical applications.

Metal artifact reduction strategies for improved attenuation correction in hybrid PET/CT imaging.

Metallic implants are known to generate bright and dark streaking artifacts in x-ray computed tomography (CT) images, which in turn propagate to corresponding functional positron emission tomography (PET) images during the CT-based attenuation correction procedure commonly used on hybrid clinical PET/CT scanners. Therefore, visual artifacts and overestimation and/or underestimation of the tracer uptake in regions adjacent to metallic implants are likely to occur and as such, inaccurate quantification of the tracer uptake and potential erroneous clinical interpretation of PET images is expected. Accurate quantification of PET data requires metal artifact reduction (MAR) of the CT images prior to the application of the CT-based attenuation correction procedure. In this review, the origins of metallic artifacts and their impact on clinical PET/CT imaging are discussed. Moreover, a brief overview of proposed MAR methods and their advantages and drawbacks is presented. Although most of the presented MAR methods are mainly developed for diagnostic CT imaging, their potential application in PET/CT imaging is highlighted. The challenges associated with comparative evaluation of these methods in a clinical environment in the absence of a gold standard are also discussed.

Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma.

PURPOSE: Several methods have been proposed for the segmentation of ¹8F-FDG uptake in PET. In this study, we assessed the performance of four categories of ¹8F-FDG PET image segmentation techniques in pharyngolaryngeal squamous cell carcinoma using clinical studies where the surgical specimen served as the benchmark. METHODS: Nine PET image segmentation techniques were compared including: five thresholding methods; the level set technique (active contour); the stochastic expectation-maximization approach; fuzzy clustering-based segmentation (FCM); and a variant of FCM, the spatial wavelet-based algorithm (FCM-SW) which incorporates spatial information during the segmentation process, thus allowing the handling of uptake in heterogeneous lesions. These algorithms were evaluated using clinical studies in which the segmentation results were compared to the 3-D biological tumour volume (BTV) defined by histology in PET images of seven patients with T3-T4 laryngeal squamous cell carcinoma who underwent a total laryngectomy. The macroscopic tumour specimens were collected "en bloc", frozen and cut into 1.7- to 2-mm thick slices, then digitized for use as reference. RESULTS: The clinical results suggested that four of the thresholding methods and expectation-maximization overestimated the average tumour volume, while a contrast-oriented thresholding method, the level set technique and the FCM-SW algorithm underestimated it, with the FCM-SW algorithm providing relatively the highest accuracy in terms of volume determination (-5.9 ± 11.9%) and overlap index. The mean overlap index varied between 0.27 and 0.54 for the different image segmentation techniques. The FCM-SW segmentation technique showed the best compromise in terms of 3-D overlap index and statistical analysis results with values of 0.54 (0.26-0.72) for the overlap index. CONCLUSION: The BTVs delineated using the FCM-SW segmentation technique were seemingly the most accurate and approximated closely the 3-D BTVs defined using the surgical specimens. Adaptive thresholding techniques need to be calibrated for each PET scanner and acquisition/processing protocol, and should not be used without optimization.

Reduction of artefacts caused by hip implants in CT-based attenuation-corrected PET images using 2-D interpolation of a virtual sinogram on an irregular grid.

PURPOSE: Metallic prosthetic replacements, such as hip or knee implants, are known to cause strong streaking artefacts in CT images. These artefacts likely induce over- or underestimation of the activity concentration near the metallic implants when applying CT-based attenuation correction of positron emission tomography (PET) images. Since this degrades the diagnostic quality of the images, metal artefact reduction (MAR) prior to attenuation correction is required. METHODS: The proposed MAR method, referred to as virtual sinogram-based technique, replaces the projection bins of the sinogram that are influenced by metallic implants by a 2-D Clough-Tocher cubic interpolation scheme performed in an irregular grid, called Delaunay triangulated grid. To assess the performance of the proposed method, a physical phantom and 30 clinical PET/CT studies including hip prostheses were used. The results were compared to the method implemented on the Siemens Biograph mCT PET/CT scanner. RESULTS: Both phantom and clinical studies revealed that the proposed method performs equally well as the Siemens MAR method in the regions corresponding to bright streaking artefacts and the artefact-free regions. However, in regions corresponding to dark streaking artefacts, the Siemens method does not seem to appropriately correct the tracer uptake while the proposed method consistently increased the uptake in the underestimated regions, thus bringing it to the expected level. This observation is corroborated by the experimental phantom study which demonstrates that the proposed method approaches the true activity concentration more closely. CONCLUSION: The proposed MAR method allows more accurate CT-based attenuation correction of PET images and prevents misinterpretation of tracer uptake, which might be biased owing to the propagation of bright and dark streaking artefacts from CT images to the PET data following the attenuation correction procedure.

Qualitative and quantitative assessment of metal artifacts arising from implantable cardiac pacing devices in oncological PET/CT studies: a phantom study.

PURPOSE: We evaluate the magnitude of metallic artifacts caused by various implantable cardiac pacing devices (without leads) on both attenuation maps (µ-maps) and positron emission tomography (PET) images using experimental phantom studies. We also assess the efficacy of a metal artifact reduction (MAR) algorithm along with the severity of artifacts in the presence of misalignment between µ-maps and PET images. METHODS: Four pacing devices including two pacemakers (pacemakers 1 and 2) and two cardiac resynchronization therapy (CRT) devices of pacemaker (CRT-P) and defibrillator (CRT-D) type were placed in three phantoms including a cylindrical Ge-68 phantom, a water-bath phantom and an anthropomorphic heart/thorax phantom. The µ-maps were derived from computed tomography (CT) images reconstructed using the standard method supplied by the manufacturer and those reconstructed using the MAR algorithm. In addition, the standard reconstructed CT images of the last two phantoms were manually misaligned by 10 mm along the patient's axis to simulate misalignment between CT and PET images. RESULTS: The least and severest artifacts produced on both µ-maps and PET images of the Ge-68 phantom were induced by CRT-P and pacemaker 1 devices, respectively. In the water-bath phantom, CRT-P induced 17.5% over- and 9.2% underestimation of tracer uptake whereas pacemaker 1 induced 69.6% over- and 65.7% underestimation. In the heart/thorax phantom representing a pacemaker-bearing patient, pacemaker 1 induced 41.8% increase and 36.6% decrease in tracer uptake and attenuation coefficients on average in regions corresponding to bright and dark streak artifacts, respectively. Statistical analysis revealed that the MAR algorithm was successful in reducing bright streak artifacts, yet unsuccessful for dark ones. In the heart/thorax phantom, the MAR algorithm reduced the overestimations to 4.4% and the underestimations to 35.5% on average. Misalignment between µ-maps and PET images increased the peak of pseudo-uptake by approximately 20%. CONCLUSIONS: This study demonstrated that, depending on their elemental composition, different implantable cardiac pacing devices result in varying magnitudes of metal artifacts and thus pseudo-uptake on PET images. The MAR algorithm was not successful in compensating for underestimations which calls for a more efficient algorithm. The results showed that misalignments between PET and CT images render metal-related pseudo-uptake more severe.

A virtual sinogram method to reduce dental metallic implant artefacts in computed tomography-based attenuation correction for PET.

OBJECTIVE: Attenuation correction of PET data requires accurate determination of the attenuation map (mumap), which represents the spatial distribution of linear attenuation coefficients of different tissues at 511 keV. The presence of high-density metallic dental filling material in head and neck X-ray computed tomography (CT) scanning is known to generate streak artefacts in the resulting CT images and thus in the corresponding mumaps generated using CT-based attenuation correction. Consequently, an under/overestimation of activity concentration occurs in corresponding regions of the corrected PET images. The purpose of this study is to develop a simple yet practical approach for reduction of metallic dental implant artefacts in the generated mumaps. METHODS: Currently available sinogram-based metal artefact reduction (MAR) algorithms operate directly on the raw sinograms. These usually consist of huge files stored in proprietary format not easily disclosed by the manufacturers and thus are not straightforward to read and manipulate. The proposed method uses the concept of virtual sinograms produced by forward projection of CT images in Dicom format for MAR. The projection data affected by metallic objects are detected in the sinogram space through segmentation of metallic objects in the CT image followed by forward projection of the metal-only image. Thereafter, the affected sinogram bins are replaced by interpolated values from adjacent projections using the spline interpolation technique. The algorithm was assessed using a polyethylene phantom containing materials simulating different tissues and a dedicated jaw phantom scanned before and after the insertion of metallic objects, where the corrected and noncorrected mumaps were compared with the artefact-free mumap. In addition, the Jaszczak and standard germanium phantoms including four metallic inserts were scanned on a PET/CT scanner to evaluate the impact of the MAR procedure on PET data through the comparison of uncorrected and corrected PET images to the actual activity concentrations in the phantoms. The proposed algorithm was also applied to head and neck CT images of 10 patients with metallic dental implants. RESULTS: The MAR method proved to be practical in a clinical setting and reduced substantially the visible metal induced artefacts. The mean relative error in regions close to metallic objects is reduced by approximately 90%. The statistical analysis of the Jaszczak and solid Ge-68 phantoms PET images did not reveal statistically significant differences between the corrected and artefact-free images (P>0.05). Moreover, the evaluation of clinical studies did not reveal statistically significant differences between the attenuation coefficients of the corrected mumaps and the expected theoretical values. CONCLUSION: The proposed MAR method provides a simple and convenient approach allowing correction for the presence of metal artefacts caused by dental implants without the need to manipulate the complex raw CT data. Further evaluation using a larger clinical PET/CT database is under way to evaluate the potential of the technique in a clinical setting.

Reduction of dental filling metallic artifacts in CT-based attenuation correction of PET data using weighted virtual sinograms optimized by a genetic algorithm.

PURPOSE: The presence of metallic dental fillings is prevalent in head and neck PET/CT imaging and generates bright and dark streaking artifacts in reconstructed CT images. The resulting artifacts would propagate to the corresponding PET images following CT-based attenuation correction (CTAC). This would cause over- and/or underestimation of tracer uptake in corresponding regions thus leading to inaccurate quantification of tracer uptake. The purpose of this study is to improve our recently proposed metal artifact reduction (MAR) approach and to assess its performance in a clinical setting. METHODS: The proposed MAR algorithm is performed in the virtual sinogram space to overcome the challenges associated with manipulating raw CT data. The corresponding bins of the virtual sinogram affected by metallic objects are obtained by forward projection of segmented metallic objects in the original CT image. These bins are then substituted by weighted values of three estimates: the affected bins in the original sinogram, the bins in the corrected sinogram using spline interpolation, and the sinogram bins in the neighboring column of the sinogram matrix. The optimized weighting factors (alpha, beta, and gamma) were estimated using a genetic algorithm (GA). The optimized combination of weighting coefficients was obtained using the GA applied to 24 clinical CT data sets. The proposed MAR method was then applied to 12 clinical head and neck PET/CT data sets containing dental artifacts. Analysis of the results was performed using Bland and Altman plots and a method allowing analysis in the absence of gold standard called regression without truth (RWT). The proposed method was also compared to an image-based MAR method. RESULTS: Optimization of the weighting coefficients using the GA resulted in an optimum combination of parameters of alpha=0.26, beta=0.67, and gamma=0.07. According to Bland and Altman plots generated for both CT and PET images of the clinical data, the proposed MAR algorithm is efficient for reduction of streak artifacts in CT images and such reduce the over- and/or underestimation o tracer uptake. The RWT method also confirmed the effectiveness of the proposed MAR method. The obtained figures of merit revealed that attenuation corrected PET data corrected using CTAC after applying the MAR algorithm are more similar to the assumed gold standard. Comparison with the knowledge-based method revealed that the proposed method mainly corrects the artifactual regions without modifying the unaffected regions. The knowledge-based method globally modifies the images including those that do not include metallic artifacts. CONCLUSIONS: The proposed MAR algorithm improves the quality and quantitative accuracy of clinical head and neck PET/CT images and could be easily integrated in clinical setting.