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TMEM16A (also known as anoctamin 1, ANO1) is the molecular basis of the calcium‐activated chloride channels, with ten transmembrane segments. Recently, atomic structures of the transmembrane domains of mouse TMEM16A (mTMEM16A) were determined by single‐particle electron cryomicroscopy. This gives us a solid ground to discuss the electrophysiological properties and functions of TMEM16A. TMEM16A is reported to be dually regulated by Ca2+ and voltage. In addition, the dysfunction of TMEM16A has been found to be involved in many diseases including cystic fibrosis, various cancers, hypertension, and gastrointestinal motility disorders. TMEM16A is overexpressed in many cancers, including gastrointestinal stromal tumors, gastric cancer, head and neck squamous cell carcinoma (HNSCC), colon cancer, pancreatic ductal adenocarcinoma, and esophageal cancer. Furthermore, overexpression of TMEM16A is related to the occurrence, proliferation, and migration of tumor cells. To date, several studies have shown that many natural compounds and synthetic compounds have regulatory effects on TMEM16A. These small molecule compounds might be novel drugs for the treatment of diseases caused by TMEM16A dysfunction in the future. In addition, recent studies have shown that TMEM16A plays different roles in different diseases through different signal transduction pathways. This review discusses the topology, electrophysiological properties, modulators and functions of TMEM16A in mediates nociception, gastrointestinal dysfunction, hypertension, and cancer and focuses on multiple regulatory mechanisms regarding TMEM16A. This review discusses the topology, electrophysiological properties, modulators and functions of TMEM16A in mediates nociception, gastrointestinal dysfunction, hypertension, and cancer and focuses on multiple regulatory mechanisms regarding TMEM16A. [ABSTRACT FROM AUTHOR]

Objective: To determine the correlation of displacement of the mediastinal metastatic lymph nodes with adjacent organs in non‐small cell lung cancer using four‐dimensional computed tomography. Methods: The displacements of 51 mediastinal metastatic lymph nodes located in the left–right, anterior–posterior, and superior–inferior directions were measured using four‐dimensional computed tomography. Correlation analyses were carried out to determine the association between the displacement of lymph nodes and volumetric change of the lungs and heart. Results: A significant correlation of displacement of the left group lymph nodes in left–right and the right group in anterior–posterior directions with volumetric change of the ipsilateral lung was observed. There were significant correlations between displacement of the left group lymph nodes and volumetric change of the heart. Conclusion: Displacement of the mediastinal metastatic lymph nodes was relevant to the adjacent organs. [ABSTRACT FROM AUTHOR]

Objectives: When initially discovered in 1963, techniques did not allow definitive diagnosis of the basketball‐sized mandibular tumour discovered in a woman from the Buffalo site dated to 17th century West Virginia. Her subsequent medical examiners stated that they "can only speculate as to the true nature of the tumor." An interdisciplinary approach was pursued to assess ability of currently available techniques to resolve this conundrum. Materials and methods: Macroscopic examination, X‐ray, computed tomography (CT), micro‐CT (7‐μ resolution), and epi‐illumination microscopy (100‐μ resolution) were utilized to identify diagnostic criteria that would allow discrimination among the possible etiologies. Results: Macroscopic examination revealed a 24 by 23 by 19‐cm tumour attached to the right mandible. X‐ray examination revealed intact trabeculae of reduced size but of uniform thickness. CT examination revealed intact trabeculae of decreased size but uniform thickness. Epi‐illumination microscopy confirmed the CT findings. Trabeculae appeared to be intact, uniform in thickness, and with intact connectivity. Micro‐CT revealed significant variation in trabecular thickness with frequent areas of resorption and loss of interconnections. Discussion: Osteosarcoma was considered macroscopically, on the basis of tumour size. However, the small, uniformly sized, intact trabeculae recognized on X‐ray, CT, and with epi‐illumination microscopy presented the pattern anticipated with an osteoblastoma. Micro‐CT, however, falsified that diagnosis, in favour of an osteosarcoma diagnosis. The final consideration is of an osteoblastic osteosarcoma, apparently the first recognized in the archaeologic record. The misleading nature of routine X‐ray, CT, and even microscopy (at 100‐μ resolution) in this case stands as a caveat. [ABSTRACT FROM AUTHOR]

Purpose: Imaging phantoms are widely used for testing and optimization of imaging devices without the need to expose humans to irradiation. However, commercially available phantoms are commonly manufactured in simple, generic forms and sizes and therefore do not resemble the clinical situation for many patients. Methods: Using 3D printing techniques, we created a life‐size phantom based on a clinical CT scan of the thorax from a patient with lung cancer. It was assembled from bony structures printed in gypsum, lung structures consisting of airways, blood vessels >1 mm, and outer lung surface, three lung tumors printed in nylon, and soft tissues represented by silicone (poured into a 3D‐printed mold). Results: Kilovoltage x‐ray and CT images of the phantom closely resemble those of the real patient in terms of size, shapes, and structures. Surface comparison using 3D models obtained from the phantom and the 3D models used for printing showed mean differences <1 mm for all structures. Tensile tests of the materials used for the phantom show that the phantom is able to endure radiation doses over 24,000 Gy. Conclusions: It is feasible to create an anthropomorphic thorax phantom using 3D printing and molding techniques. The phantom closely resembles a real patient in terms of spatial accuracy and is currently being used to evaluate x‐ray‐based imaging quality and positional verification techniques for radiotherapy. [ABSTRACT FROM AUTHOR]

Purpose: To quantify differences in computationally estimated computed tomography (CT) organ doses for patient-specific voxel phantoms to estimated organ doses in matched computational phantoms using different matching criteria. Materials and methods: Fifty-two patient-specific computational voxel phantoms were created through CT image segmentation. In addition, each patient-specific phantom was matched to six computational phantoms of the same gender based, respectively, on age and gender (reference phantoms), height and weight, effective diameter (both central slice and exam range average), and water equivalent diameter (both central slice and exam range average). Each patient-specific phantom and matched computational phantom were then used to simulate six different torso examinations using a previously validated Monte Carlo CT dosimetry methodology that accounts for tube current modulation. Organ doses for each patient-specific phantom were then compared with the organ dose estimates of each of the matched phantoms. Results: Relative to the corresponding patient-specific phantoms, the root mean square of the difference in organ dose was 39.1%, 20.3%, 22.7%, 21.6%, 20.5%, and 17.6%, for reference, height and weight, effective diameter (central slice and scan average), and water equivalent diameter (central slice and scan average), respectively. The average magnitude of difference in organ dose was 24%, 14%, 16.9%, 16.2%, 14%, and 11.9%, respectively. Conclusion: Overall, these data suggest that matching a patient to a computational phantom in a library is superior to matching to a reference phantom. Water equivalent diameter is the superior matching metric, but it is less feasible to implement in a clinical and retrospective setting. For these reasons, height-and-weight matching is an acceptable and reliable method for matching a patient to a member of a computational phantom library with regard to CT dosimetry. [ABSTRACT FROM AUTHOR]

Purpose: To accurately model the beam delivery time (BDT) for a synchrotron-based proton spot scanning system using experimentally determined beam parameters. Methods: A model to simulate the proton spot delivery sequences was constructed, and BDT was calculated by summing times for layer switch, spot switch, and spot delivery. Test plans were designed to isolate and quantify the relevant beam parameters in the operation cycle of the proton beam therapy delivery system. These parameters included the layer switch time, magnet preparation and verification time, average beam scanning speeds in x- and y-directions, proton spill rate, and maximum charge and maximum extraction time for each spill. The experimentally determined parameters, as well as the nominal values initially provided by the vendor, served as inputs to the model to predict BDTs for 602 clinical proton beam deliveries. The calculated BDTs (TBDT) were compared with the BDTs recorded in the treatment delivery log files (TLog): ∆t = TLog−TBDT. Results: The experimentally determined average layer switch time for all 97 energies was 1.91 s (ranging from 1.9 to 2.0 s for beam energies from 71.3 to 228.8 MeV), average magnet preparation and verification time was 1.93 ms, the average scanning speeds were 5.9 m/s in x-direction and 19.3 m/s in y-direction, the proton spill rate was 8.7 MU/s, and the maximum proton charge available for one acceleration is 2.0 ± 0.4 nC. Some of the measured parameters differed from the nominal values provided by the vendor. The calculated BDTs using experimentally determined parameters matched the recorded BDTs of 602 beam deliveries (∆t = −0.49 ± 1.44 s), which were significantly more accurate than BDTs calculated using nominal timing parameters (∆t = −7.48 ± 6.97 s). Conclusions: An accurate model for BDT prediction was achieved by using the experimentally determined proton beam therapy delivery parameters, which may be useful in modeling the interplay effect and patient throughput. The model may provide guidance on how to effectively reduce BDT and may be used to identifying deteriorating machine performance. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this feasibility study using phantoms was to propose a novel method for obtaining computer-generated realistic virtual nodules in lung computed tomography (CT). Methods: In the proposed methodology, pulmonary nodule images obtained with a CT scanner are deconvolved with the point spread function (PSF) in the scan plane and slice sensitivity profile (SSP) measured for the scanner; the resultant images are referred to as nodule-like object functions. Next, by convolving the nodule-like object function with the PSF and SSP of another (target) scanner, the virtual nodule can be generated so that it has the characteristics of the spatial resolution of the target scanner. To validate the methodology, the authors applied physical nodules of 5-, 7- and 10-mm-diameter (uniform spheres) included in a commercial CT test phantom. The nodule-like object functions were calculated from the sphere images obtained with two scanners (Scanner A and Scanner B); these functions were referred to as nodule-like object functions A and B, respectively. From these, virtual nodules were generated based on the spatial resolution of another scanner (Scanner C). By investigating the agreement of the virtual nodules generated from the nodule-like object functions A and B, the equivalence of the nodule-like object functions obtained from different scanners could be assessed. In addition, these virtual nodules were compared with the real (true) sphere images obtained with Scanner C. As a practical validation, five types of laboratory-made physical nodules with various complicated shapes and heterogeneous densities, similar to real lesions, were used. The nodule-like object functions were calculated from the images of these laboratory-made nodules obtained with Scanner A. From them, virtual nodules were generated based on the spatial resolution of Scanner C and compared with the real images of laboratory-made nodules obtained with Scanner C. Results: Good agreement of the virtual nodules generated from the nodule-like object functions A and B of the phantom spheres was found, suggesting the validity of the nodule-like object functions. The virtual nodules generated from the nodule-like object function A of the phantom spheres were similar to the real images obtained with Scanner C; the root mean square errors (RMSEs) between them were 10.8, 11.1, and 12.5 Hounsfield units (HU) for 5-, 7-, and 10-mm-diameter spheres, respectively. The equivalent results (RMSEs) using the nodule-like object function B were 15.9, 16.8, and 16.5 HU, respectively. These RMSEs were small considering the high contrast between the sphere density and background density (approximately 674 HU). The virtual nodules generated from the nodule-like object functions of the five laboratory-made nodules were similar to the real images obtained with Scanner C; the RMSEs between them ranged from 6.2 to 8.6 HU in five cases. Conclusions: The nodule-like object functions calculated from real nodule images would be effective to generate realistic virtual nodules. The proposed method would be feasible for generating virtual nodules that have the characteristics of the spatial resolution of the CT system used in each institution, allowing for site-specific nodule generation. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the feasibility of a 3D imaging system utilizing a 155Eu source and pixelated cadmium-zinc-telluride (CZT) detector for applications in adaptive radiotherapy. Specifically, to compare the reconstructed stopping power ratio (SPR) values of a head phantom obtained with the proposed imaging technique with theoretical SPR values. Method: A Geant4 Monte Carlo simulation was performed with the novel imaging system. The simulation was repeated with a typical 120 kV X-ray tube spectrum while maintaining all other parameters. Dual energy 155Eu source cone beam computed tomography (CBCT) images were reconstructed with an iterative projection algorithm known as total variation superiorization with diagonally relaxed orthogonal projections (TVS-DROP). Single energy 120 kV source CBCT images were also reconstructed with TVS-DROP. Reconstructed images were converted to SPR with stoichiometric calibration techniques based on ICRU 44 tissues. Quantitative accuracy of reconstructed attenuation coefficient images as well as SPR images were compared. Results: Images generated by gamma emissions of 155Eu showed superior contrast resolution to those generated by the 120 kV spectrum. Quantitatively, all reconstructed images correlated with reference attenuation coefficients of the head phantom within 1 standard deviation. Images generated with the 155Eu source showed a smaller standard deviation of pixel values. Use of a dual energy conversion into SPR resulted in superior SPR accuracy with the 155Eu source. Conclusion: 155Eu was found to display desirable qualities when used as a source for dual energy CBCT. Further work is required to demonstrate whether the simulation results presented here can be translated into an experimental prototype. [ABSTRACT FROM AUTHOR]

Purpose: Using common datasets, to estimate and compare the diagnostic performance of image-based denoising techniques or iterative reconstruction algorithms for the task of detecting hepatic metastases. Methods: Datasets from contrast-enhanced CT scans of the liver were provided to participants in an NIH-, AAPM- and Mayo Clinic-sponsored Low Dose CT Grand Challenge. Training data included full-dose and quarter-dose scans of the ACR CT accreditation phantom and 10 patient examinations; both images and projections were provided in the training data. Projection data were supplied in a vendor-neutral standardized format (DICOM-CT-PD). Twenty quarter-dose patient datasets were provided to each participant for testing the performance of their technique. Images were provided to sites intending to perform denoising in the image domain. Fully preprocessed projection data and statistical noise maps were provided to sites intending to perform iterative reconstruction. Upon return of the denoised or iteratively reconstructed quarter-dose images, randomized, blinded evaluation of the cases was performed using a Latin Square study design by 11 senior radiology residents or fellows, who marked the locations of identified hepatic metastases. Markings were scored against reference locations of clinically or pathologically demonstrated metastases to determine a per-lesion normalized score and a per-case normalized score (a faculty abdominal radiologist established the reference location using clinical and pathological information). Scores increased for correct detections; scores decreased for missed or incorrect detections. The winner for the competition was the entry that produced the highest total score (mean of the per-lesion and per-case normalized score). Reader confidence was used to compute a Jackknife alternative free-response receiver operating characteristic (JAFROC) figure of merit, which was used for breaking ties. Results: 103 participants from 90 sites and 26 countries registered to participate. Training data were shared with 77 sites that completed the data sharing agreements. Subsequently, 41 sites downloaded the 20 test cases, which included only the 25% dose data (CTDIvol = 3.0 ± 1.8 mGy, SSDE = 3.5 ± 1.3 mGy). 22 sites submitted results for evaluation. One site provided binary images and one site provided images with severe artifacts; cases from these sites were excluded from review and the participants removed from the challenge. The mean (range) per-lesion and per-case normalized scores were −24.2% (−75.8%, 3%) and 47% (10%, 70%), respectively. Compared to reader results for commercially reconstructed quarter-dose images with no noise reduction, 11 of the 20 sites showed a numeric improvement in the mean JAFROC figure of merit. Notably two sites performed comparably to the reader results for full-dose commercial images. The study was not designed for these comparisons, so wide confidence intervals surrounded these figures of merit and the results should be used only to motivate future testing. Conclusion: Infrastructure and methodology were developed to rapidly estimate observer performance for liver metastasis detection in low-dose CT examinations of the liver after either image-based denoising or iterative reconstruction. The results demonstrated large differences in detection and classification performance between noise reduction methods, although the majority of methods provided some improvement in performance relative to the commercial quarter-dose images with no noise reduction applied. [ABSTRACT FROM AUTHOR]

Purpose: Accurate deformable image registration is necessary for longitudinal studies. The error associated with commercial systems has been evaluated using computed tomography (CT). Several in-house algorithms have been evaluated for use with magnetic resonance imaging (MRI), but there is still relatively little information about MRI deformable image registration. This work presents an evaluation of two deformable image registration systems, one commercial (Velocity) and one in-house (demons-based algorithm), with MRI using two different metrics to quantify the registration error. Methods: The registration error was analyzed with synthetic MR images. These images were generated from interpatient and intrapatient variation models trained on 28 patients. Four synthetic post-treatment images were generated for each of four synthetic pretreatment images, resulting in 16 image registrations for both the T1- and T2-weighted images. The synthetic post-treatment images were registered to their corresponding synthetic pretreatment image. The registration error was calculated between the known deformation vector field and the generated deformation vector field from the image registration system. The registration error was also analyzed using a porcine phantom with ten implanted 0.35-mm diameter gold markers. The markers were visible on CT but not MRI. CT, T1-weighted MR, and T2-weighted MR images were taken in four different positions. The markers were contoured on the CT images and rigidly registered to their corresponding MR images. The MR images were deformably registered and the distance between the projected marker location and true marker location was measured as the registration error. Results: The synthetic images were evaluated only on Velocity. Root mean square errors (RMSEs) of 0.76 mm in the left-right (LR) direction, 0.76 mm in the anteroposterior (AP) direction, and 0.69 mm in the superior-inferior (SI) direction were observed for the T1-weighted MR images. RMSEs of 1.1 mm in the LR direction, 0.75 mm in the AP direction, and 0.81 mm in the SI direction were observed for the T2-weighted MR images. The porcine phantom MR images, when evaluated with Velocity, had RMSEs of 1.8, 1.5, and 2.7 mm in the LR, AP, and SI directions for the T1-weighted images and 1.3, 1.2, and 1.6 mm in the LR, AP, and SI directions for the T2-weighted images. When the porcine phantom images were evaluated with the in-house demons-based algorithm, RMSEs were 1.2, 1.5, and 2.1 mm in the LR, AP, and SI directions for the T1-weighted images and 0.81, 1.1, and 1.1 mm in the LR, AP, and SI directions for the T2-weighted images. Conclusions: The MRI registration error was low for both Velocity and the in-house demons-based algorithm according to both image evaluation methods, with all RMSEs below 3 mm. This implies that both image registration systems can be used for longitudinal studies using MRI. [ABSTRACT FROM AUTHOR]

Abstract: The enhanced dosimetric performance of knowledge‐based volumetric modulated arc therapy (VMAT) planning might be jointly contributed by the patient‐specific optimization objectives, as estimated by the RapidPlan model, and by the potentially improved Photon Optimizer (PO) algorithm than the previous Progressive Resolution Optimizer (PRO) engine. As PO is mandatory for RapidPlan estimation but optional for conventional manual planning, appreciating the two optimizers may provide practical guidelines for the algorithm selection because knowledge‐based planning may not replace the current method completely in a short run. Using a previously validated dose–volume histogram (DVH) estimation model which can produce clinically acceptable plans automatically for rectal cancer patients without interactive manual adjustment, this study reoptimized 30 historically approved plans (referred as clinical plans that were created manually with PRO) with RapidPlan solution (PO plans). Then the PRO algorithm was utilized to optimize the plans again using the same dose–volume constraints as PO plans, where the line objectives were converted as a series of point objectives automatically (PRO plans). On the basis of comparable target dose coverage, the combined applications of new objectives and PO algorithm have significantly reduced the organs‐at‐risk (OAR) exposure by 23.49–32.72% than the clinical plans. These discrepancies have been largely preserved after substituting PRO for PO, indicating the dosimetric improvements were mostly attributable to the refined objectives. Therefore, Eclipse users of earlier versions may instantly benefit from adopting the model‐generated objectives from other RapidPlan‐equipped centers, even with PRO algorithm. However, the additional contribution made by the PO relative to PRO accounted for 1.54–3.74%, suggesting PO should be selected with priority whenever available, with or without RapidPlan solution as a purchasable package. Significantly increased monitor units were associated with the model‐generated objectives but independent from the optimizers, indicating higher modulation in these plans. As a summary, PO prevails over PRO algorithm for VMAT planning with or without knowledge‐based technique. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this study was to compare three computed tomography (CT) images under different conditions—average intensity projection (AIP), free breathing (FB), mid‐ventilation (MidV)—used for radiotherapy contouring and planning in lung cancer patients. Two image sets derived from four‐dimensional CT (4DCT) acquisition (AIP and MidV) and three‐dimensional CT with FB were generated and used to plan for 29 lung cancer patients. Organs at risk (OARs) were delineated for each image. AIP images were calculated with 3D conformal radiotherapy (3DCRT) and intensity‐modulated radiation therapy (IMRT). Planning with the same target coverage was applied to the FB and MidV image sets. Plans with small and large tumors were compared regarding OAR volumes, geometrical center differences in OARs, and dosimetric indices. A gamma index analysis was also performed to compare dose distributions. There were no significant differences (P > 0.05) in OAR volumes, the geometrical center differences, maximum and mean doses of the OARs between both tumor sizes. For 3DCRT, the gamma analysis results indicated an acceptable dose distribution agreement of 95% with 2%/2 mm criteria. Although, the gamma index results show distinct contrast of dose distribution outside the planning target volume (PTV) in IMRT, but within the PTV, it was acceptable. All three images could be used for OAR delineation and dose calculation in lung cancer. AIP image sets seemed to be suitable for dose calculation while patient movement between series acquisition of FB images should be considered when defining target volumes on 4DCT images. [ABSTRACT FROM AUTHOR]

Aim To retrospectively evaluate the detectability of tumor recurrence with computed tomography (CT) and magnetic resonance imaging (MRI) after superselective conventional transcatheter arterial chemoembolization (cTACE) for hepatocellular carcinoma (HCC). Methods The detectability of tumor recurrence with CT and gadoxetate disodium-enhanced MRI obtained within 30 days (mean, 16.9 ± 10.1) were compared in 38 patients with recurrent HCC after superselective cTACE. Tumor recurrence was divided into local and distant recurrence. Local recurrence was also divided into intratumoral and peritumoral recurrence. Results Tumor recurrence (maximum diameter, 19.7 ± 10.1 mm) was demonstrated by images 12.4 ± 11.7 months after cTACE. CT could depict 16 (76.2%) of 21 intratumoral recurrences in 12 patients and 14 (53.8%) of 26 peritumoral recurrences in 11, as well as 39 (55.7%) of 70 distant recurrences in 15 (75%) of 20 patients. Arterial phase MRI could depict 20 (95.2%) of 21 intratumoral recurrences in 14 patients and all 26 (100%) peritumoral recurrences in 21, as well as 60 (85.7%) distant recurrences in all 20 (100%) patients. The detectability of tumor recurrence with MRI was significantly higher than that with CT ( P = 0.00549). On MRI, pseudolesions were observed in five (13.2%) patients and artifacts in the arterial phase in five (13.2%). Regarding the diagnostic performance, CT was superior to MRI in two (5.3%) patients and MRI was superior to CT in 19 (50%). They were almost equal in 17 (44.7%). Conclusion The detectability of tumor recurrence after superselective cTACE with gadoxetate disodium-enhanced MRI was superior to that of CT. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the effects of dose level and reconstruction method on density and texture based features computed from CT lung nodules. Methods: This study had two major components. In the first component, a uniform water phantom was scanned at three dose levels and images were reconstructed using four conventional filtered backprojection (FBP) and four iterative reconstruction (IR) methods for a total of 24 different combinations of acquisition and reconstruction conditions. In the second component, raw projection (sinogram) data were obtained for 33 lung nodules from patients scanned as a part of their clinical practice, where low dose acquisitions were simulated by adding noise to sinograms acquired at clinical dose levels (a total of four dose levels) and reconstructed using one FBP kernel and two IR kernels for a total of 12 conditions. For the water phantom, spherical regions of interest (ROIs) were created at multiple locations within the water phantom on one reference image obtained at a reference condition. For the lung nodule cases, the ROI of each nodule was contoured semiautomatically (with manual editing) from images obtained at a reference condition. All ROIs were applied to their corresponding images reconstructed at different conditions. For 17 of the nodule cases, repeat contours were performed to assess repeatability. Histogram (eight features) and gray level co-occurrence matrix (GLCM) based texture features (34 features) were computed for all ROIs. For the lung nodule cases, the reference condition was selected to be 100% of clinical dose with FBP reconstruction using the B45f kernel; feature values calculated from other conditions were compared to this reference condition. A measure was introduced, which the authors refer to as Q, to assess the stability of features across different conditions, which is defined as the ratio of reproducibility (across conditions) to repeatability (across repeat contours) of each feature. Results: The water phantom results demonstrated substantial variability among feature values calculated across conditions, with the exception of histogram mean. Features calculated from lung nodules demonstrated similar results with histogram mean as the most robust feature (Q = 1), having a mean and standard deviation Q of 0.37 and 0.22, respectively. Surprisingly, histogram standard deviation and variance features were also quite robust. Some GLCM features were also quite robust across conditions, namely, diff. variance, sum variance, sum average, variance, and mean. Except for histogram mean, all features have a Q of larger than one in at least one of the 3% dose level conditions. Conclusions: As expected, the histogram mean is the most robust feature in their study. The effects of acquisition and reconstruction conditions on GLCM features vary widely, though trending toward features involving summation of product between intensities and probabilities being more robust, barring a few exceptions. Overall, care should be taken into account for variation in density and texture features if a variety of dose and reconstruction conditions are used for the quantification of lung nodules in CT, otherwise changes in quantification results may be more reflective of changes due to acquisition and reconstruction conditions than in the nodule itself. [ABSTRACT FROM AUTHOR]

Purpose: Radiation therapy simulations solely based on MRI have advantages compared to CT-based approaches. One feature readily available from computed tomography (CT) that would need to be reproduced with MR is the ability to compute digitally reconstructed radiographs (DRRs) for comparison against on-board radiographs commonly used for patient positioning. In this study, the authors generate MR-based bone images using a single ultrashort echo time (UTE) pulse sequence and quantify their 3D and 2D image registration accuracy to CT and radiographic images for treatments in the cranium. Methods: Seven brain cancer patients were scanned at 1.5 T using a radial UTE sequence. The sequence acquired two images at two different echo times. The two images were processed using an in-house software to generate the UTE bone images. The resultant bone images were rigidly registered to simulation CT data and the registration error was determined using manually annotated landmarks as references. DRRs were created based on UTE-MRI and registered to simulated on-board images (OBIs) and actual clinical 2D oblique images from ExacTrac™. Results: UTE-MRI resulted in well visualized cranial, facial, and vertebral bones that quantitatively matched the bones in the CT images with geometric measurement errors of less than 1 mm. The registration error between DRRs generated from 3D UTE-MRI and the simulated 2D OBIs or the clinical oblique x-ray images was also less than 1 mm for all patients. Conclusions: UTE-MRI-based DRRs appear to be promising for daily patient setup of brain cancer radiotherapy with kV on-board imaging. [ABSTRACT FROM AUTHOR]

Purpose: To differentiate radiation-induced fibrosis from regional lung collapse outside of the high dose region in patients treated with stereotactic body radiation therapy (SBRT) for lung tumors. Methods: Lung deformation maps were computed from pre-treatment and post-treatment computed tomography (CT) scans using a point-to-point translation method. Fifty anatomical landmarks inside the lung (vessel or airway branches) were matched on planning and follow-up scans for the computation process. Two methods using the deformation maps were developed to differentiate regional lung collapse from fibrosis: vector field and Jacobian methods. A total of 40 planning and follow-ups CT scans were analyzed for 20 lung SBRT patients. Results: Regional lung collapse was detected in 15 patients (75%) using the vector field method, in ten patients (50%) using the Jacobian method, and in 12 patients (60%) by radiologists. In terms of sensitivity and specificity the Jacobian method performed better. Only weak correlations were observed between the dose to the proximal airways and the occurrence of regional lung collapse. Conclusions: The authors presented and evaluated two novel methods using anatomical lung deformations to investigate lung collapse and fibrosis caused by SBRT treatment. Differentiation of these distinct physiological mechanisms beyond what is usually labeled "fibrosis" is necessary for accurate modeling of lung SBRT-induced injuries. With the help of better models, it becomes possible to expand the therapeutic benefits of SBRT to a larger population of lung patients with large or centrally located tumors that were previously considered ineligible. [ABSTRACT FROM AUTHOR]

Purpose: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. Methods: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting model used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors' classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. Results: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. Conclusions: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging. [ABSTRACT FROM AUTHOR]

Purpose: The work presented here demonstrates an application of diffuse optical tomography (DOT) to the problem of breast-cancer diagnosis. The potential for using spatial and temporal variability measures of the hemoglobin signal to identify useful biomarkers was studied. Methods: DOT imaging data were collected using two instrumentation platforms the authors developed, which were suitable for exploring tissue dynamics while performing a simultaneous bilateral exam. For each component of the hemoglobin signal (e.g., total, oxygenated), the image time series was reduced to eight scalar metrics that were affected by one or more dynamic properties of the breast microvasculature (e.g., average amplitude, amplitude heterogeneity, strength of spatial coordination). Receiver-operator characteristic (ROC) analyses, comparing groups of subjects with breast cancer to various control groups (i.e., all noncancer subjects, only those with diagnosed benign breast pathology, and only those with no known breast pathology), were performed to evaluate the effect of cancer on the magnitudes of the metrics and of their interbreast differences and ratios. Results: For women with known breast cancer, simultaneous bilateral DOT breast measures reveal a marked increase in the resting-state amplitude of the vasomotor response in the hemoglobin signal for the affected breast, compared to the contralateral, noncancer breast. Reconstructed 3D spatial maps of observed dynamics also show that this behavior extends well beyond the tumor border. In an effort to identify biomarkers that have the potential to support clinical aims, a group of scalar quantities extracted from the time series measures was systematically examined. This analysis showed that many of the quantities obtained by computing paired responses from the bilateral scans (e.g., interbreast differences, ratios) reveal statistically significant differences between the cancer-positive and -negative subject groups, while the corresponding measures derived from individual breast scans do not. ROC analyses yield area-under-curve values in the 77%-87% range, depending on the metric, with sensitivity and specificity values ranging from 66% to 91%. An interesting result is the initially unexpected finding that the hemodynamic-image metrics are only weakly dependent on the tumor burden, implying that the DOT technique employed is sensitive to tumor-induced changes in the vascular dynamics of the surrounding breast tissue as well. Computational modeling studies serve to identify which properties of the vasomotor response (e.g., average amplitude, amplitude heterogeneity, and phase heterogeneity) principally determine the values of the metrics and their codependences. Findings from the modeling studies also serve to clarify the influence of spatial-response heterogeneity and of system-design limitations, and they reveal the impact that a complex dependence of metric values on the modeled behaviors has on the success in distinguishing between cancer-positive and -negative subjects. Conclusions: The authors identified promising hemoglobin-based biomarkers for breast cancer from measures of the resting-state dynamics of the vascular bed. A notable feature of these biomarkers is that their spatial extent encompasses a large fraction of the breast volume, which is mainly independent of tumor size. Tumor-induced induction of nitric oxide synthesis, a well-established concomitant of many breast cancers, is offered as a plausible biological causal factor for the reported findings. [ABSTRACT FROM AUTHOR]

Purpose: Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images. Methods: The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accurately delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1-10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the "gold standard" for validation of the method's accuracy. Results: The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm. Conclusions: Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic segmentation method. [ABSTRACT FROM AUTHOR]

There is a broad differential diagnosis for interstitial shadows on chest computed tomography in rheumatoid arthritis patients, especially those previously treated with immunosuppressant drugs. We report an immunocompromised rheumatoid arthritis patient in respiratory failure with diffuse ground-glass opacities (GGOs), who was diagnosed with pulmonary lymphangitic carcinomatosis as the initial presentation of prostate cancer. He was successfully treated with chemohormonal androgen deprivation therapy, including bicalutamide, leuprorelin acetate, denosumab, and docetaxel. Metastatic pulmonary lymphangitis, rarely from the prostate, should always be considered in the differential diagnosis of GGOs, even when the patient has no known prior malignancies. [ABSTRACT FROM AUTHOR]

Purpose: To determine if and by how much the commercial 4DCT protocols under- and overestimate tumor breathing motion. Methods: 1D simulations were conducted that modeled a 16-slice CT scanner and tumors moving proportionally to breathing amplitude. External breathing surrogate traces of at least 5-min duration for 50 patients were used. Breathing trace amplitudes were converted to motion by relating the nominal tumor motion to the 90th percentile breathing amplitude, reflecting motion defined by the more recent 5DCT approach. Based on clinical low-pitch helical CT acquisition, the CT detector moved according to its velocity while the tumor moved according to the breathing trace. When the CT scanner overlapped the tumor, the overlapping slices were identified as having imaged the tumor. This process was repeated starting at successive 0.1 s time bin in the breathing trace until there was insufficient breathing trace to complete the simulation. The tumor size was subtracted from the distance between the most superior and inferior tumor positions to determine the measured tumor motion for that specific simulation. The effect of the scanning parameter variation was evaluated using two commercial 4DCT protocols with different pitch values. Because clinical 4DCT scan sessions would yield a single tumor motion displacement measurement for each patient, errors in the tumor motion measurement were considered systematic. The mean of largest 5% and smallest 5% of the measured motions was selected to identify over- and underdetermined motion amplitudes, respectively. The process was repeated for tumor motions of 1-4 cm in 1 cm increments and for tumor sizes of 1-4 cm in 1 cm increments. Results: In the examined patient cohort, simulation using pitch of 0.06 showed that 30% of the patients exhibited a 5% chance of mean breathing amplitude overestimations of 47%, while 30% showed a 5% chance of mean breathing amplitude underestimations of 36%; with a separate simulation using pitch of 0.1 showing, respectively, 37% overestimation and 61% underestimation. Conclusions: The simulation indicates that commercial low-pitch helical 4DCT processes potentially yield large tumor motion measurement errors, both over- and underestimating the tumor motion. [ABSTRACT FROM AUTHOR]

Purpose: To present a novel positioning strategy which optimizes radiation delivery by utilizing radiobiological response knowledge and evaluate its use during prostate external beam radiotherapy. Methods: Five patients with low or intermediate risk prostate cancer were evaluated retrospectively in this IRB-approved study. For each patient, a VMAT plan with one 358° arc was generated on the planning CT (PCT) to deliver 78 Gy in 39 fractions. Five representative pretreatment cone beam CTs (CBCT) were selected for each patient. The CBCT images were registered to PCT by a human observer, which consisted of an initial automated registration with three degrees-of-freedom, followed by manual adjustment for agreement at the prostate/rectal wall interface. To determine the optimal treatment position for each CBCT, a search was performed centering on the observer-matched position (OM-position) utilizing a score function based on radiobiological and dosimetric indices (EUDprostate, D99prostate, NTCPrectum, and NTCPbladder) for the prostate, rectum, and bladder. We termed the optimal treatment position the radiobiologically optimized couch shift position (ROCS-position). Results: The dosimetric indices, averaged over the five patients' treatment plans, were (mean ± SD) 79.5 ± 0.3 Gy (EUDprostate), 78.2 ± 0.4 Gy (D99prostate), 11.1% ± 2.7% (NTCPrectum), and 46.9% ± 7.6% (NTCPbladder). The corresponding values from CBCT at the OM-positions were 79.5 ± 0.6 Gy (EUDprostate), 77.8 ± 0.7 Gy (D99prostate), 12.1% ± 5.6% (NTCPrectum), and 51.6% ± 15.2% (NTCPbladder), respectively. In comparison, from CBCT at the ROCS-positions, the dosimetric indices were 79.5 ± 0.6 Gy (EUDprostate), 77.3 ± 0.6 Gy (D99prostate), 8.0% ± 3.3% (NTCPrectum), and 46.9% ± 15.7% (NTCPbladder). Excessive NTCPrectum was observed on Patient 5 (19.5% ± 6.6%) corresponding to localization at OM-position, compared to the planned value of 11.7%. This was mitigated with radiobiologically optimized localization, resulting in a reduced NTCPrectum value of 11.3% ± 3.5%. Overall, the treatment position optimization resulted in similar target dose coverage with reduced risk to rectum. Conclusions: These encouraging results illustrate the potential advantage of applying radiobiologically optimized correction for online image-guided radiotherapy of prostate patients. [ABSTRACT FROM AUTHOR]

Multimodality imaging systems such as positron emission tomography-computed tomography (PETCT) and MRI-PET are widely available, but a simultaneous CT-MRI instrument has not been developed. Synergies between independent modalities, e.g., CT, MRI, and PET/SPECT can be realized with image registration, but such postprocessing suffers from registration errors that can be avoided with synchronized data acquisition. The clinical potential of simultaneous CT-MRI is significant, especially in cardiovascular and oncologic applications where studies of the vulnerable plaque, response to cancer therapy, and kinetic and dynamic mechanisms of targeted agents are limited by current imaging technologies. The rationale, feasibility, and realization of simultaneous CT-MRI are described in this perspective paper. The enabling technologies include interior tomography, unique gantry designs, open magnet and RF sequences, and source and detector adaptation. Based on the experience with PET-CT, PET-MRI, and MRI-LINAC instrumentation where hardware innovation and performance optimization were instrumental to construct commercial systems, the authors provide top-level concepts for simultaneous CT-MRI to meet clinical requirements and new challenges. Simultaneous CT-MRI fills a major gap of modality coupling and represents a key step toward the so-called "omnitomography" defined as the integration of all relevant imaging modalities for systems biology and precision medicine. [ABSTRACT FROM AUTHOR]

Purpose: Despite multiple methodologies already proposed to correct respiratory motion in the whole PET imaging field of view (FOV), such approaches have not found wide acceptance in clinical routine. An alternative can be the local respiratory motion correction (LRMC) of data corresponding to a given volume of interest (VOI: organ or tumor). Advantages of LRMC include the use of a simple motion model, faster execution times, and organ specific motion correction. The purpose of this study was to evaluate the performance of LMRC using various motion models for oncology (lung lesion) applications. Methods: Both simulated (NURBS based 4D cardiac-torso phantom) and clinical studies (six patients) were used in the evaluation of the proposed LRMC approach. PET data were acquired in list-mode and synchronized with respiration. The implemented approach consists first in defining a VOI on the reconstructed motion average image. Gated PET images of the VOI are subsequently reconstructed using only lines of response passing through the selected VOI and are used in combination with a center of gravity or an affine/elastic registration algorithm to derive the transformation maps corresponding to the respiration effects. Those are finally integrated in the reconstruction process to produce a motion free image over the lesion regions. Results: Although the center of gravity or affine algorithm achieved similar performance for individual lesion motion correction, the elastic model, applied either locally or to the whole FOV, led to an overall superior performance. The spatial tumor location was altered by 89% and 81% for the elastic model applied locally or to the whole FOV, respectively (compared to 44% and 39% for the center of gravity and affine models, respectively). This resulted in similar associated overall tumor volume changes of 84% and 80%, respectively (compared to 75% and 71% for the center of gravity and affine models, respectively). The application of the nonrigid deformation model in LRMC led to over an order of magnitude gain in computational efficiency of the correction relative to the application of the deformable model to the whole FOV. Conclusions: The results of this study support the use of LMRC as a flexible and efficient correction approach for respiratory motion effects for single lesions in the thoracic area. [ABSTRACT FROM AUTHOR]

Purpose: Accurate tumor delineation in positron emission tomography (PET) images is crucial in oncology. Although recent methods achieved good results, there is still room for improvement regarding tumors with complex shapes, low signal-to-noise ratio, and high levels of uptake heterogeneity. Methods: The authors developed and evaluated an original clustering-based method called spatial positron emission quantification of tumor--Automatic Lp-norm estimation (SPEQTACLE), based on the fuzzy C-means (FCM) algorithm with a generalization exploiting a Hilbertian norm to more accurately account for the fuzzy and non-Gaussian distributions of PET images. An automatic and reproducible estimation scheme of the norm on an image-by-image basis was developed. Robustness was assessed by studying the consistency of results obtained on multiple acquisitions of the NEMA phantom on three different scanners with varying acquisition parameters. Accuracy was evaluated using classification errors (CEs) on simulated and clinical images. SPEQTACLE was compared to another FCM implementation, fuzzy local information C-means (FLICM) and fuzzy locally adaptive Bayesian (FLAB). Results: SPEQTACLE demonstrated a level of robustness similar to FLAB (variability of 14%±9% vs 14%±7%, p = 0.15) and higher than FLICM (45%±18%, p < 0.0001), and improved accuracy with lower CE (14%±11%) over both FLICM (29%±29%) and FLAB (22%±20%) on simulated images. Improvement was significant for the more challenging cases with CE of 17%±11% for SPEQTACLE vs 28%±22% for FLAB (p = 0.009) and 40%±35% for FLICM (p < 0.0001). For the clinical cases, SPEQTACLE outperformed FLAB and FLICM (15%±6% vs 37%±14% and 30%±17%, p < 0.004). Conclusions: SPEQTACLE benefitted from the fully automatic estimation of the norm on a case-by-case basis. This promising approach will be extended to multimodal images and multiclass estimation in future developments. [ABSTRACT FROM AUTHOR]

Purpose: High breast density, as measured by mammography, is associated with increased breast cancer risk, but standard methods of assessment have limitations including 2D representation of breast tissue, distortion due to breast compression, and use of ionizing radiation. Ultrasound tomography (UST) is a novel imaging method that averts these limitations and uses sound speed measures rather than x-ray imaging to estimate breast density. The authors evaluated the reproducibility of measures of speed of sound and changes in this parameter using UST. Methods: One experienced and five newly trained raters measured sound speed in serial UST scans for 22 women (two scans per person) to assess inter-rater reliability. Intrarater reliability was assessed for four raters. A random effects model was used to calculate the percent variation in sound speed and change in sound speed attributable to subject, scan, rater, and repeat reads. The authors estimated the intraclass correlation coefficients (ICCs) for these measures based on data from the authors' experienced rater. Results: Median (range) time between baseline and follow-up UST scans was five (1-13) months. Contributions of factors to sound speed variance were differences between subjects (86.0%), baseline versus follow-up scans (7.5%), inter-rater evaluations (1.1%), and intrarater reproducibility (~0%). When evaluating change in sound speed between scans, 2.7% and ~0% of variation were attributed to inter- and intrarater variation, respectively. For the experienced rater's repeat reads, agreement for sound speed was excellent (ICC = 93.4%) and for change in sound speed substantial (ICC = 70.4%), indicating very good reproducibility of these measures. Conclusions: UST provided highly reproducible sound speed measurements, which reflect breast density, suggesting that UST has utility in sensitively assessing change in density. [ABSTRACT FROM AUTHOR]

Purpose: In image-guided radio frequency ablation for liver cancer treatment, pre- and post-interventional CT images are typically used to verify the treatment success of the therapy. In current clinical practice, the tumor zone in the diagnostic, preinterventional images is mentally or manually mapped to the ablation zone in the post-interventional images to decide success of the treatment. However, liver deformation and differences in image quality as well as in texture of the ablation zone and the tumor area make the mental or manual registration a challenging task. Purpose of this paper is to develop an automatic framework to register the pre-interventional image to the post-interventional image. Methods: The authors propose a registration approach enabling a nonrigid deformation of the tumor to the ablation zone, while keeping locally rigid deformation of the tumor area. The method was evaluated on CT images of 38 patient datasets from Erasmus MC. The evaluation is based on Dice coefficients of the liver segmentation on both the pre-interventional and post-interventional images, and mean distances between the liver segmentations. Additionally, residual distances after registration between corresponding landmarks and local mean surface distance in the images were computed. Results: The results show that rigid registration gives a Dice coefficient of 87.9%, a mean distance of the liver surfaces of 5.53 mm, and a landmark error of 5.38 mm, while non-rigid registration with local rigid deformation has a Dice coefficient of 92.2%, a mean distance between the liver segmentation boundaries near the tumor area of 3.83 mm, and a landmark error of 2.91 mm, where a part of this error can be attributed to the slice spacing in the authors' CT images. Conclusions: This method is thus a promising tool to assess the success of RFA liver cancer treatment. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. Methods: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimal feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. Results: Among curvature measures, the normalized total curvature (CT) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and CT yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and CT were conducted. The results showed that CT was able to replace the other four image features for the classification task. Conclusions: The normalized curvature measure contains useful information in classifying breast tumors. Using this, one can reduce the number of features in a classifier, which may result in more robust classifiers for different datasets. [ABSTRACT FROM AUTHOR]

Purpose: To compare two coverage-based planning (CP) techniques with fixed margin-based (FM) planning for high-risk prostate cancer treatments, with the exclusive consideration of the dosimetric impact of delineation uncertainties of target structures and normal tissues. Methods: In this work, 19-patient data sets were involved. To estimate structure dose for each delineated contour under the influence of interobserver contour variability and CT image quality limitations, 1000 alternative structures were simulated by an average-surface-of-standard-deviation model, which utilized the patient-specific information of delineated structure and CT image contrast. An IMRT plan with zero planning-target-volume (PTV) margin on the delineated prostate and seminal vesicles [clinical-target-volume (CTVprostate) and CTVSV] was created and dose degradation due to contour variability was quantified by the dosimetric consequences of 1000 alternative structures. When D98 failed to achieve a 95% coverage probability objective D98,95 = 78 Gy (CTVprostate) or D98,95 = 66 Gy (CTVSV), replanning was performed using three planning techniques: (1) FM (PTVprostate margin = 4,5,6 mm and PTVSV margin = 4,5,7 mm for RL, PA, and SI directions, respectively), (2) CPOM which optimized uniform PTV margins for CTVprostate and CTVSV to meet the D98,95 objectives, and (3) CPCOP which directly optimized coverage-based objectives for all the structures. These plans were intercompared by computing percentile dose-volume histograms and tumor-control probability/normal tissue complication probability (TCP/NTCP) distributions. Results: Inherent contour variability resulted in unacceptable CTV coverage for the zero-PTV-margin plans for all patients. For plans designed to accommodate contour variability, 18/19 CP plans were most favored by achieving desirable D98,95 and TCP/NTCP values. The average improvement of probability of complication free control was 9.3% for CPCOP plans and 3.4% for CPOM plans. Conclusions: When the delineation uncertainties need to be considered for prostate patients, CP techniques can produce more desirable plans than FM plans for most patients. The relative advantages between CPCOP and CPOM techniques are patient specific. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the feasibility of functional photoacoustic tomography (fPAT) for high resolution detection and characterization of breast cancer and to demonstrate for the first time quantitative hemoglobin concentration and oxygen saturation images of breasts that were formed with modelbased reconstruction of tomographic photoacoustic data. Methods: The study was HIPAA compliant and was approved by the university institutional review board. Written informed consents were obtained from all the participants. Ten cases, including six cancer and four healthy (mean age = 50 yr; age range = 41-66 yr), were examined. Functional images of breast tissue including absolute total hemoglobin concentration (HbT) and oxygen saturation (StO2%) were obtained by fPAT and cross validated with magnetic resonance imaging (MRI) readings and/or histopathology. Results: HbT and StO2% maps from all six pathology-confirmed cancer cases (60%) show clear detection of tumor, while MR images indicate clear detection of tumor for five of six cancer cases; one small tumor was read as near-complete-resolution by MRI. The average HbT and StO2% value of suspicious lesion area for the cancer cases was 61.6±18.9 μM/l and 67.5%±5.2% compared to 25.6±7.4 μM/l and 65.2%±3.8% for background normal tissue. Conclusions: fPAT has the potential to be a significant add-on in breast cancer detection and characterization as it provides submillimeter resolution functional images of breast lesions. [ABSTRACT FROM AUTHOR]

Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation-maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the "ground truth" for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm³ (range, 6.6-44.3 cm³), while the PET segmented GTV was 10.2 cm³ (range, 2.8-45.1 cm³). The median physician-defined GTV was 22.1 cm³ (range, 4.2-38.4 cm³). The median difference between the multichannel segmented and physician-defined GTVs was -10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was -19.2%, showing a statistically significant difference (p-value = 0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55-0.84), and the median sensitivity and positive predictive value between them were 0.76 and 0.81, respectively. Conclusions: The authors developed an automated multimodality segmentation algorithm for tumor volume delineation and validated this algorithm for head and neck cancer radiotherapy. The multichannel segmented GTV agreed well with the physician-defined GTV. The authors expect that their algorithm will improve the accuracy and consistency in target definition for radiotherapy. [ABSTRACT FROM AUTHOR]

Purpose: There is evidence that computed tomography (CT) and positron emission tomography (PET) imaging metrics are prognostic and predictive in nonsmall cell lung cancer (NSCLC) treatment outcomes. However, few studies have explored the use of standardized uptake value (SUV)-based image features of nodal regions as predictive features. The authors investigated and compared the use of tumor and node image features extracted from the radiotherapy target volumes to predict relapse in a cohort of NSCLC patients undergoing chemoradiation treatment. Methods: A prospective cohort of 25 patients with locally advanced NSCLC underwent 4DPET/4DCT imaging for radiation planning. Thirty-seven image features were derived from the CT-defined volumes and SUVs of the PET image from both the tumor and nodal target regions. The machine learning methods of logistic regression and repeated stratified five-fold cross-validation (CV) were used to predict local and overall relapses in 2 yr. The authors used well-known feature selection methods (Spearman's rank correlation, recursive feature elimination) within each fold of CV. Classifiers were ranked on their Matthew's correlation coefficient (MCC) after CV. Area under the curve, sensitivity, and specificity values are also presented. Results: For predicting local relapse, the best classifier found had a mean MCC of 0.07 and was composed of eight tumor features. For predicting overall relapse, the best classifier found had a mean MCC of 0.29 and was composed of a single feature: the volume greater than 0.5 times the maximum SUV (N). Conclusions: The best classifier for predicting local relapse had only tumor features. In contrast, the best classifier for predicting overall relapse included a node feature. Overall, the methods showed that nodes add value in predicting overall relapse but not local relapse. [ABSTRACT FROM AUTHOR]

Purpose: To demonstrate the feasibility of proton dose calculation on scatter-corrected cone-beam computed tomographic (CBCT) images for the purpose of adaptive proton therapy. Methods: CBCT projection images were acquired from anthropomorphic phantoms and a prostate patient using an on-board imaging system of an Elekta infinity linear accelerator. Two previously introduced techniques were used to correct the scattered x-rays in the raw projection images: uniform scatter correction (CBCTus) and a priori CT-based scatter correction (CBCTap). CBCT images were reconstructed using a standard FDK algorithm and GPU-based reconstruction toolkit. Soft tissue ROI-based HU shifting was used to improve HU accuracy of the uncorrected CBCT images and CBCTus, while no HU change was applied to the CBCTap. The degree of equivalence of the corrected CBCT images with respect to the reference CT image (CTref) was evaluated by using angular profiles of water equivalent path length (WEPL) and passively scattered proton treatment plans. The CBCTap was further evaluated in more realistic scenarios such as rectal filling and weight loss to assess the effect of mismatched prior information on the corrected images. Results: The uncorrected CBCT and CBCTus images demonstrated substantial WEPL discrepancies (7.3±5.3 mm and 11.1±6.6 mm, respectively) with respect to the CTref, while the CBCTap images showed substantially reduced WEPL errors (2.4±2.0 mm). Similarly, the CBCTap-based treatment plans demonstrated a high pass rate (96.0%±2.5% in 2 mm/2% criteria) in a 3D gamma analysis. Conclusions: A priori CT-based scatter correction technique was shown to be promising for adaptive proton therapy, as it achieved equivalent proton dose distributions and water equivalent path lengths compared to those of a reference CT in a selection of anthropomorphic phantoms. [ABSTRACT FROM AUTHOR]

Purpose: Accurate visualization of lung motion is important in many clinical applications, such as radiotherapy of lung cancer. Advancement in imaging modalities [e.g., computed tomography (CT) and MRI] has allowed dynamic imaging of lung and lung tumor motion. However, each imaging modality has its advantages and disadvantages. The study presented in this paper aims at generating synthetic 4D-CT dataset for lung cancer patients by combining both continuous three-dimensional (3D) motion captured by 4D-MRI and the high spatial resolution captured by CT using the authors' proposed approach. Methods: A novel hybrid approach based on deformable image registration (DIR) and finite element method simulation was developed to fuse a static 3D-CT volume (acquired under breath-hold) and the 3D motion information extracted from 4D-MRI dataset, creating a synthetic 4D-CT dataset. Results: The study focuses on imaging of lung and lung tumor. Comparing the synthetic 4D-CT dataset with the acquired 4D-CT dataset of six lung cancer patients based on 420 landmarks, accurate results (average error <2 mm) were achieved using the authors' proposed approach. Their hybrid approach achieved a 40% error reduction (based on landmarks assessment) over using only DIR techniques. Conclusions: The synthetic 4D-CT dataset generated has high spatial resolution, has excellent lung details, and is able to show movement of lung and lung tumor over multiple breathing cycles. [ABSTRACT FROM AUTHOR]

Purpose: A novel breast CT system capable of arbitrary 3D trajectories has been developed to address cone beam sampling insufficiency as well as to image further into the patient's chest wall. The purpose of this study was to characterize any trajectory-related differences in 3D x-ray dose distribution in a pendant target when imaged with different orbits. Methods: Two acquisition trajectories were evaluated: circular azimuthal (no-tilt) and sinusoidal (saddle) orbit with ±15? tilts around a pendant breast, using Monte Carlo simulations as well as physical measurements. Simulations were performed with tungsten (W) filtration of a W-anode source; the simulated source flux was normalized to the measured exposure of a W-anode source. A water-filled cylindrical phantom was divided into 1 cm³ voxels, and the cumulative energy deposited was tracked in each voxel. Energy deposited per voxel was converted to dose, yielding the 3D distributed dose volumes. Additionally, three cylindrical phantoms of different diameters (10, 12.5, and 15 cm) and an anthropomorphic breast phantom, initially filled with water (mimicking pure fibroglandular tissue) and then with a 75% methanol-25% water mixture (mimicking 50-50 fibroglandular-adipose tissues), were used to simulate the pendant breast geometry and scanned on the physical system. Ionization chamber calibrated radiochromic film was used to determine the dose delivered in a 2D plane through the center of the volume for a fully 3D CT scan using the different orbits. Results: Measured experimental results for the same exposure indicated that the mean dose measured throughout the central slice for different diameters ranged from 3.93 to 5.28 mGy, with the lowest average dose measured on the largest cylinder with water mimicking a homogeneously fibroglandular breast. These results align well with the cylinder phantom Monte Carlo studies which also showed a marginal difference in dose delivered by a saddle trajectory in the central slice. Regardless of phantom material or filled fluid density, dose delivered by the saddle scan was negligibly different than the simple circular, no-tilt scans. The average dose measured in the breast phantom was marginally higher for saddle than the circular no tilt scan at 3.82 and 3.87 mGy, respectively. Conclusions: Not only does nontraditional 3D-trajectory CT scanning yield more complete sampling of the breast volume but also has comparable dose deposition throughout the breast and anterior chest volume, as verified by Monte Carlo simulation and physical measurements. [ABSTRACT FROM AUTHOR]

Purpose: To provide a noninvasive technique to measure the intensity profile of the fan beam in a computed tomography (CT) scanner that is cost effective and easily implemented without the need to access proprietary scanner information or service modes. Methods: The fabrication of an inexpensive aperture is described, which is used to expose radiochromic film in a rotating CT gantry. A series of exposures is made, each of which is digitized on a personal computer document scanner, and the resulting data set is analyzed to produce a self-consistent calibration of relative radiation exposure. The bow tie profiles were analyzed to determine the precision of the process and were compared to two other measurement techniques, direct measurements from CT gantry detectors and a dynamic dosimeter. Results: The radiochromic film method presented here can measure radiation exposures with a precision of ~6% root-mean-square relative error. The intensity profiles have a maximum 25% root-mean-square relative error compared with existing techniques. Conclusions: The proposed radiochromic film method for measuring bowtie profiles is an inexpensive (~$100 USD+film costs), noninvasive method to measure the fan beam intensity profile in CT scanners. [ABSTRACT FROM AUTHOR]

Purpose: To investigate constancy, within a treatment session, of the time lag relationship between implanted markers in abdominal tumors and an external motion surrogate. Methods: Six gastroesophageal junction and three pancreatic cancer patients (IRB-approved protocol) received two cone-beam CTs (CBCT), one before and one after treatment. Time between scans was less than 30 min. Each patient had at least one implanted fiducial marker near the tumor. In all scans, abdominal displacement (Varian RPM) was recorded as the external motion signal. Purpose-built software tracked fiducials, representing internal signal, in CBCT projection images. Time lag between superior-inferior (SI) internal and anterior-posterior external signals was found by maximizing the correlation coefficient in each breathing cycle and averaging over all cycles. Time-lag-induced discrepancy between internal SI position and that predicted from the external signal (external prediction error) was also calculated. Results: Mean ± standard deviation time lag, over all scans and patients, was 0.10±0.07 s (range 0.01-0.36 s). External signal lagged the internal in 17/18 scans. Change in time lag between pre- and post-treatment CBCT was 0.06±0.07 s (range 0.01-0.22 s), corresponding to 3.1%±3.7% (range 0.6%-10.8%) of gate width (range 1.6-3.1 s). In only one patient, change in time lag exceeded 10% of the gate width. External prediction error over all scans of all patients varied from 0.1±0.1 to 1.6±0.4 mm. Conclusions: Time lag between internal motion along SI and external signals is small compared to the treatment gate width of abdominal patients examined in this study. Change in time lag within a treatment session, inferred from pre- to post-treatment measurements is also small, suggesting that a single measurement of time lag at the session start is adequate. These findings require confirmation in a larger number of patients. [ABSTRACT FROM AUTHOR]

X-ray mammography of the compressed breast is well recognized as the "gold standard" for early detection of breast cancer, but its performance is not ideal. One limitation of screening mammography is tissue superposition, particularly for dense breasts. Since 2001, several research groups in the USA and in the European Union have developed computed tomography (CT) systems with digital detector technology dedicated to x-ray imaging of the uncompressed breast (breast CT or BCT) for breast cancer screening and diagnosis. This CT technology--tracing back to initial studies in the 1970s-- allows some of the limitations of mammography to be overcome, keeping the levels of radiation dose to the radiosensitive breast glandular tissue similar to that of two-view mammography for the same breast size and composition. This paper presents an evaluation of the research efforts carried out in the invention, development, and improvement of BCT with dedicated scanners with state-of-the-art technology, including initial steps toward commercialization, after more than a decade of R&D in the laboratory and/or in the clinic. The intended focus here is on the technological/engineering aspects of BCT and on outlining advantages and limitations as reported in the related literature. Prospects for future research in this field are discussed. [ABSTRACT FROM AUTHOR]

Purpose: Benign computed tomography (CT) changes due to radiation induced lung injury (RILI) are common following stereotactic ablative radiotherapy (SABR) and can be difficult to differentiate from tumor recurrence. The authors measured the ability of CT image texture analysis, compared to more traditional measures of response, to predict eventual cancer recurrence based on CT images acquired within 5 months of treatment. Methods: A total of 24 lesions from 22 patients treated with SABR were selected for this study: 13 with moderate to severe benign RILI, and 11 with recurrence. Three-dimensional (3D) consolidative and ground-glass opacity (GGO) changes were manually delineated on all follow-up CT scans. Two size measures of the consolidation regions (longest axial diameter and 3D volume) and nine appearance features of the GGO were calculated: 2 first-order features [mean density and standard deviation of density (first-order texture)], and 7 second-order texture features [energy, entropy, correlation, inverse difference moment (IDM), inertia, cluster shade, and cluster prominence]. For comparison, the corresponding response evaluation criteria in solid tumors measures were also taken for the consolidation regions. Prediction accuracy was determined using the area under the receiver operating characteristic curve (AUC) and two-fold cross validation (CV). Results: For this analysis, 46 diagnostic CT scans scheduled for approximately 3 and 6 months posttreatment were binned based on their recorded scan dates into 2-5 month and 5-8 month follow-up time ranges. At 2-5 months post-treatment, first-order texture, energy, and entropy provided AUCs of 0.79-0.81 using a linear classifier. On two-fold CV, first-order texture yielded 73% accuracy versus 76%-77% with the second-order features. The size measures of the consolidative region, longest axial diameter and 3D volume, gave two-fold CV accuracies of 60% and 57%, and AUCs of 0.72 and 0.65, respectively. Conclusions: Texture measures of the GGO appearance following SABR demonstrated the ability to predict recurrence in individual patients within 5 months of SABR treatment. Appearance changes were also shown to be more accurately predictive of recurrence, as compared to size measures within the same time period. With further validation, these results could form the substrate for a clinically useful computer-aided diagnosis tool which could provide earlier salvage of patients with recurrence. [ABSTRACT FROM AUTHOR]

Gold nanoparticles, especially positron-emitter- labeled gold nanostructures, have gained steadily increasing attention in biomedical applications. Of the radionuclides used for nanoparticle positron emission tomography imaging, radiometals such as 64Cu have been widely employed. Currently, radiolabeling through macrocyclic chelators is the most commonly used strategy. However, the radiolabel stability may be a limiting factor for further translational research. We report the integration of 64Cu into the structures of gold nanoparticles. With this approach, the specific radioactivity of the alloyed gold nanoparticles could be freely and precisely controlled by the addition of the precursor 64CuCl2 to afford sensitive detection. The direct incorporation of 64Cu into the lattice of the gold nanoparticle structure ensured the radiolabel stability for accurate localization in vivo. The superior pharmacokinetic and positron emission tomography imaging capabilities demonstrate high passive tumor targeting and contrast ratios in a mouse breast cancer model, as well as the great potential of this unique alloyed nanostructure for preclinical and translational imaging. [ABSTRACT FROM AUTHOR]

Purpose: It is estimated that every year, 1 × 106 women are diagnosed with breast cancer, and more than 410 000 die annually worldwide. Digital Image Elasto Tomography (DIET) is a new noninvasive breast cancer screening modality that induces mechanical vibrations in the breast and images its surface motion with digital cameras to detect changes in stiffness. This research develops a new automated approach for diagnosing breast cancer using DIET based on a modal analysis model. Methods: The first and second natural frequency of silicone phantom breasts is analyzed. Separate modal analysis is performed for each region of the phantom to estimate the modal parameters using imaged motion data over several input frequencies. Statistical methods are used to assess the likelihood of a frequency shift, which can indicate tumor location. Phantoms with 5, 10, and 20 mm stiff inclusions are tested, as well as a homogeneous (healthy) phantom. Inclusions are located at four locations with different depth. Results: The second natural frequency proves to be a reliable metric with the potential to clearly distinguish lesion like inclusions of different stiffness, as well as providing an approximate location for the tumor like inclusions. The 10 and 20 mm inclusions are always detected regardless of depth. The 5 mm inclusions are only detected near the surface. The homogeneous phantom always yields a negative result, as expected. Conclusions: Detection is based on a statistical likelihood analysis to determine the presence of significantly different frequency response over the phantom, which is a novel approach to this problem. The overall results show promise and justify proof of concept trials with human subjects. [ABSTRACT FROM AUTHOR]

Aim Patients with unresectable hepatocellular carcinoma ( HCC) often undergo transcatheter arterial chemoembolization ( TACE). Miriplatin is a lipophilic cisplatin derivative used in TACE that is effective in HCC. However, the difference in antitumor efficacy between warmed versus room temperature miriplatin is unclear. Methods Chemotherapy efficacy was evaluated by dynamic computed tomography 1-3 months after TACE, according to the Modified Response Evaluation Criteria in Solid Tumors. A total of 203 patients with HCC who received TACE with miriplatin for the first time were included in a follow-up study to retrospectively investigate its efficacy and safety. Overall, 45 patients underwent TACE with warmed (40° C) miriplatin and 158 patients received TACE with room temperature miriplatin. Results Seventy patients (44.3%) treated with room temperature miriplatin and 32 patients (71.1%) who received warmed miriplatin experienced complete or partial responses. Multivariate analysis identified miriplatin temperature (warmed miriplatin, risk ratio ( RR) = 2.26, P = 0.047), tumor number (solitary, RR = 3.48, P = 0.007), α-fetoprotein ( AFP) level (<50 ng/ mL, RR = 2.35, P = 0.012) and history of TACE (no history, RR = 2.22, P = 0.041) as predictors of objective response following TACE with miriplatin, and no serious complications were observed. Conclusion Warm temperature, solitary tumors, low AFP level and first TACE are significant and independent predictors of objective response after TACE using miriplatin. These results suggest that warmed miriplatin can be considered as one of the standard treatments for unresectable HCC. [ABSTRACT FROM AUTHOR]

Aim To demonstrate the usefulness of the computed tomography ( CT) fusion imaging for the evaluation of treatment effect of radiofrequency ablation ( RFA) for hepatocellular carcinoma ( HCC). Methods Eighty-five patients with 94 HCC with complete ablation judged on conventional side-by-side interpretation of pre- RFA and post- RFA CT at the time of RFA were included in this retrospective study. CT data was retrospectively used to create fusion images of pre- RFA and post- RFA CT using automatic rigid registration and manual correction referring to intrahepatic structures and hepatic contours around a tumor. Clinical factors including a minimal ablative margin ( MAM) measured on fusion images were examined to prove risk factors for local tumor progression ( LTP). Results LTP was observed in 13 (13.8%) tumors with a median follow up of 21.0 months (range, 2-75). The mean MAM on the fusion image was 1.4 ± 3.1 mm and 23 tumors (24.5%) were judged to be protruding from the ablation zone. Multivariate analysis revealed that protruding from the ablation zone was the only significant factor for LTP (hazard ratio, 7.09; 95% confidential interval, 2.26-22.3; P < 0.001). Conclusion Some HCC were assessed as incomplete ablation on the CT fusion images, although considered completely ablated on side-by-side images at the time of treatment, and incomplete ablation was revealed to be the only independent risk factor for LTP. The CT fusion imaging enables quantitative and accurate evaluation of treatment effect of RFA. [ABSTRACT FROM AUTHOR]

Purpose: The introduction of radioembolization with microspheres represents a significant step forward in the treatment of patients with metastatic disease to the liver. This technique uses semiempirical formulae based on body surface area or liver and target volumes to calculate the required total activity for a given patient. However, this treatment modality lacks extremely important information, which is the three-dimensional (3D) dose delivered by microspheres to different organs after their administration. The absence of this information dramatically limits the clinical efficacy of this modality, specifically the predictive power of the treatment. Therefore, the aim of this study is to develop a 3D dose calculation technique that is based on the PET imaging of the infused microspheres. Methods: The Fluka Monte Carlo code was used to calculate the voxel dose kernel for 90Y source with voxel size equal to that of the PET scan. The measured PET activity distribution was converted to total activity distribution for the subsequent convolution with the voxel dose kernel to obtain the 3D dose distribution. In addition, dose-volume histograms were generated to analyze the dose to the tumor and critical structures. Results: The 3D inpatient dose distribution can be reconstructed from the PET data of a patient scanned after the infusion of microspheres. A total of seven patients have been analyzed so far using the proposed reconstruction method. Four patients underwent treatment with SIR-Spheres for liver metastases from colorectal cancer and three patients were treated with Therasphere for hepatocellular cancer. A total of 14 target tumors were contoured on post-treatment PET-CT scans for dosimetric evaluation. Mean prescription activity was 1.7 GBq (range: 0.58-3.8 GBq). The resulting mean maximum measured dose to targets was 167 Gy (range: 71-311 Gy). Mean minimum dose to 70% of target (D70) was 68 Gy (range: 25-155 Gy). Mean minimum dose to 90% of target (D90) was 53 Gy (range: 13-125 Gy). Conclusions: A three-dimensional inpatient dose reconstruction method has been developed that is based on the PET/CT data of a patient treated with 90Y microspheres. It allows for a complete description of the absorbed dose by the tumor and critical structures. It represents the first step in building predictive models for treatment outcomes for patients receiving this therapeutic modality as well as it allows for better analysis of patients' dose response and will ultimately improve future treatment administration. [ABSTRACT FROM AUTHOR]

Purpose: The measurement of malignant pleural mesothelioma is critical to the assessment of tumor response to therapy. Current response assessment standards utilize summed linear measurements acquired on three computed tomography (CT) sections. The purpose of this study was to evaluate manual area measurements as an alternate response assessment metric, specifically through the study of measurement interobserver variability. Methods: Two CT scans from each of 31 patients were collected. Using a computer interface, five observers contoured tumor on three selected CT sections from each baseline scan. Four observers also constructed matched follow-up scan tumor contours for the same 31 patients. Area measurements extracted from these contours were compared using a random effects analysis of variance model to assess relative interobserver variability. The sums of section area measurements were also analyzed, since these area sums are more clinically relevant for response assessment. Results: When each observer's measurements were compared with those of the other four observers, strong correlation was observed. The 95% confidence interval for relative interobserver variability of baseline scan summed area measurements was [-71%, +240%], spanning 311%. For the follow-up scan summed area measurements, the 95% confidence interval for relative interobserver variability was [-41%, +70%], spanning 111%. At both baseline and follow-up, the variability among observers was a significant component of the total variability in both per-section and summed area measurements (p < 0.0001). Conclusions: Despite the ability of tumor area measurements to capture tumor burden with greater fidelity than linear tumor thickness measurements, manual area measurements may not be a robust means of response assessment in mesothelioma patients. [ABSTRACT FROM AUTHOR]

Purpose: Accurate volumetric tumor delineation is of increasing importance in radiation treatment planning. Many tumors exhibit only moderate tracer uptake heterogeneity and delineation methods using an adaptive threshold lead to robust results. These methods use a tumor reference value R (e.g., ROI maximum) and the tumor background Bg to compute the volume reproducing threshold. This threshold corresponds to an isocontour which defines the tumor boundary. However, the boundaries of strongly heterogeneous tumors can not be described by an isocontour anymore and therefore conventional threshold methods are not suitable for accurate delineation. The aim of this work is the development and validation of a delineation method for heterogeneous tumors. Methods: The new method (voxel-specific threshold method, VTM) can be considered as an extension of an adaptive threshold method (lesion-specific threshold method, LTM), where instead of a lesion-specific threshold for the whole ROI, a voxel-specific threshold is computed by determining for each voxel Bg and R in the close vicinity of the voxel. The absolute threshold for the considered voxel is then given by Tabs = T × (R - Bg) + Bg, where T = 0.39 was determined with phantom measurements. Validation: 30 clinical datasets from patients with non-small-cell lung cancer were used to generate 30 realistic anthropomorphic software phantoms of tumors with different heterogeneities and well-known volumes and boundaries. Volume delineation was performed with VTM and LTM and compared with the known lesion volumes and boundaries. Results: In contrast to LTM, VTM was able to reproduce the true tumor boundaries accurately, independent of the heterogeneity. The deviation of the determined volume from the true volume was (0.8 ± 4.2)% for VTM and (11.0 ± 16.4)% for LTM. Conclusions: In anthropomorphic software phantoms, the new method leads to promising results and to a clear improvement of volume delineation in comparison to conventional background-corrected thresholding. In the next step, the suitability for clinical routine will be further investigated. [ABSTRACT FROM AUTHOR]

A 20-month-old intact female Mastiff dog presented for evaluation of acute blindness. Computed tomography confirmed a tumor involved the optic chiasm and optic nerves. A B-cell lymphoma was confirmed with postmortem examinations. This case report documents a central nervous system neoplasm in a young dog. [ABSTRACT FROM AUTHOR]

A 16-year-old spayed female cat was evaluated for lagophthalmos and chronic exposure keratitis in both eyes. Ophthalmic examination revealed upper and lower eyelid entropion of the left eye ( OS) and markedly decreased retropulsion, restricted eye movement, marked episcleral congestion, and severe keratitis of both eyes ( OU). Magnetic resonance imaging of both orbits revealed extensive, irregular, contrast-enhancing tissue without evidence of osteolysis considered compatible with diffuse inflammatory tissue. Feline herpesvirus DNA was not detected in conjunctival samples. Partial temporary tarsorrhaphies were placed OU, and the cat was treated with topically administered erythromycin ointment OU, orally administered famciclovir and prednisolone, and sublingually administered buprenorphine. Little improvement was noted after 2 weeks. Six weeks after initial presentation, a left exenteration was performed and histopathology was consistent with idiopathic sclerosing orbital pseudotumor ( ISOP). Ten weeks after initial presentation, the patient represented for weight loss and jaw pain. Computed tomography demonstrated disease progression in the right orbit and the patient was euthanized. Histopathology of the decalcified skull revealed an aggressive and highly infiltrative mass involving the right orbit with extension to the maxilla, hard palate, nasal cavity and gingiva most consistent with feline restrictive orbital myofibroblastic sarcoma ( FROMS). Clinical data from this patient support the reclassification of ISOP as FROMS. MRI and CT may provide supportive evidence for FROMS, but histopathology is necessary for definitive diagnosis. Aggressive and early surgical treatment, including bilateral exenteration, with adjunctive radiotherapy and/or chemotherapy should be considered for patients with FROMS. [ABSTRACT FROM AUTHOR]