Intra- and interobserver assessments of intestinal wall thickness and segmentations from transverse sections of feline abdominal ultrasound images.

Measurements of intestinal wall thicknesses from ultrasound imaging (US) are routinely used to support diagnoses of intestinal disorders in cats, however published studies describing observer agreement are currently lacking. The aim of this retrospective, observer agreement study was to quantify inter- and intraobserver repeatability and agreement in the measurement of intestinal wall layer thicknesses and the segmentation of transverse sections of small intestines in US images of 20 cats. Intestinal wall layer thickness measurements of the mucosa, submucosa, muscularis, serosa layer, and total thickness of these layers were performed on five cats with small cell epitheliotropic lymphoma, five with inflammatory bowel disease, and 10 with other conditions. Thickness measurements and the segmentation encompassing the serosa layer were obtained from five observers four times non-sequentially. The average standard deviation in thickness measurements (95% confidence interval) in the mucosa, submucosa, muscularis, serosa, and total thickness were 0.35 (0.07-0.95), 0.24 (0.07-0.52), 0.22 (0.06-0.49), 0.20 (0.05-0.49), and 0.57 (0.11-1.60) mm, respectively. The average intraclass correlation coefficients, which estimates the degree of consistency in thickness measurements and segmentation areas for each observer, ranged from 0.355 to 0.870 and 0.850 to 0.993, respectively. The interclass correlation coefficient, which estimates the degree of consistency when measuring a thickness or segmentation area over all observers ranged from 0.115 to 0.753, and 0.811 to 0.902, respectively. The overall average Dice Coefficient, which estimates the extent of overlap of the segmentations for all observers was 0.957 (0.933 to 0.972). Our results suggest segmentations of small intestines have a higher interobserver agreement than measurements of intestinal wall thicknesses.

Combining ultrasound radiomics, complete blood count, and serum biochemical biomarkers for diagnosing intestinal disorders in cats using machine learning.

This retrospective analytical observational cohort study aimed to model and predict the classification of feline intestinal diseases from segmentations of a transverse section from small intestine ultrasound (US) image, complete blood count (CBC), and serum biochemical profile data using a variety of machine-learning approaches. In 149 cats from three institutions, images were obtained from cats with biopsy-confirmed small cell epitheliotropic lymphoma (lymphoma), inflammatory bowel disease (IBD), no pathology ("healthy"), and other conditions (warrant a biopsy for further diagnosis). CBC, blood serum chemistry, small intestinal ultrasound, and small intestinal biopsy were obtained within a 2-week interval. CBC and serum biomarkers and radiomic features were combined for modeling. Four classification schemes were investigated: (1) normal versus abnormal; (2) warranting or not warranting a biopsy; (3) lymphoma, IBD, healthy, or other conditions; and (4) lymphoma, IBD, or other conditions. Two feature selection methods were used to identify the top 3, 5, 10, and 20 features, and six machine learning models were trained. The average (95% CI) performance of models for all combinations of features, numbers of features, and types of classifiers was 0.886 (0.871-0.912) for Model 1 (normal vs. abnormal), 0.751 (0.735-0.818) for Model 2 (biopsy vs. no biopsy), 0.504 (0.450-0.556) for Model 3 (lymphoma, IBD, healthy, or other), and 0.531 (0.426-0.589), for Model 4 (lymphoma, IBD, or other). Our findings suggest model accuracies above 0.85 can be achieved in Model 1 and 2, and that including CBC and biochemistry data with US radiomics data did not significantly improve accuracy in our models.

Proximal sesamoid bone microdamage is localized to articular subchondral regions in Thoroughbred racehorses, with similar fracture toughness between fracture and controls.

OBJECTIVE: To determine whether proximal sesamoid bone (PSB) microdamage and fracture toughness differ between Thoroughbred racehorses sustaining PSB fracture and controls. STUDY DESIGN: Cadaveric case-control. ANIMALS: Twenty-four Thoroughbred racehorses (n = 12 PSB fracture, n = 12 control). METHODS: Proximal sesamoid bones were dissected, and gross pathological changes and morphological measurements were documented. High-speed exercise history data were evaluated. Microdamage was assessed in fracture, fracture-contralateral limb (FXCL) and control PSBs using whole bone lead uranyl acetate (LUA) staining with micro-CT imaging or basic fuchsin histological analysis. Fracture toughness mechanical testing was carried out in 3-point-bending of microbeams created from PSB flexor cortices. Data were analyzed using ordinal logistic and linear regression models. RESULTS: Microdamage was detected most commonly in the articular subchondral region of PSBs via LUA micro-CT and basic fuchsin histology. There were no differences in microdamage between FXCL and control PSBs. Fracture toughness values were similar for FXCL (1.31 MPa√m) and control (1.35 MPa√m) PSBs. Exercise histories were similar except that horses sustaining fracture spent a greater percentage of their careers in rest weeks. CONCLUSION: Microdamage was detected in the articular region of PSBs but was not greater in horses sustaining catastrophic PSB fracture. Fracture toughness of PSB flexor cortices did not differ between FXCL and control PSBs. CLINICAL SIGNIFICANCE: Although uncommon, microdamage is localized to the articular region of Thoroughbred racehorse PSBs. Catastrophic PSB failure is not associated with lower PSB flexor cortex fracture toughness.

Radiomics in veterinary medicine: Overview, methods, and applications.

Radiomics, or quantitative image analysis from radiographic image data, borrows the suffix from other emerging -omics fields of study, such as genomics, proteomics, and metabolomics. This report provides an overview of the general principles of how radiomic features are computed, describes major types of morphological, first order, and texture features, and the applications, challenges, and opportunities of radiomics as applied in veterinary medicine. Some advantages radiomics has over traditional semantic radiological features include standardized methodology in computing semantic features, the ability to compute features in multi-dimensional images, their newfound associations with genomic and pathological abnormalities, and the number of perceptible and imperceptible features available for regression or classification modeling. Some challenges in deploying radiomics in a clinical setting include sensitivity to image acquisition settings and image artifacts, pre- and post-image reconstruction and calculation settings, variability in feature estimates stemming from inter- and intra-observer contouring errors, and challenges with software and data harmonization and generalizability of findings given the challenges of small sample size and patient selection bias in veterinary medicine. Despite this, radiomics has enormous potential in patient-centric diagnostics, prognosis, and theragnostics. Fully leveraging the utility of radiomics in veterinary medicine will require inter-institutional collaborations, data harmonization, and data sharing strategies amongst institutions, transparent and robust model development, and multi-disciplinary efforts within and outside the veterinary medical imaging community.

The role of artificial intelligence in veterinary radiation oncology.

Veterinary radiation oncology regularly deploys sophisticated contouring, image registration, and treatment planning optimization software for patient care. Over the past decade, advances in computing power and the rapid development of neural networks, open-source software packages, and data science have been realized and resulted in new research and clinical applications of artificial intelligent (AI) systems in radiation oncology. These technologies differ from conventional software in their level of complexity and ability to learn from representative and local data. We provide clinical and research application examples of AI in human radiation oncology and their potential applications in veterinary medicine throughout the patient's care-path: from treatment simulation, deformable registration, auto-segmentation, automated treatment planning and plan selection, quality assurance, adaptive radiotherapy, and outcomes modeling. These technologies have the potential to offer significant time and cost savings in the veterinary setting; however, since the range of usefulness of these technologies have not been well studied nor understood, care must be taken if adopting AI technologies in clinical practice. Over the next several years, some practical and realizable applications of AI in veterinary radiation oncology include automated segmentation of normal tissues and tumor volumes, deformable registration, multi-criteria plan optimization, and adaptive radiotherapy. Keys in achieving success in adopting AI in veterinary radiation oncology include: establishing "truth-data"; data harmonization; multi-institutional data and collaborations; standardized dose reporting and taxonomy; adopting an open access philosophy, data collection and curation; open-source algorithm development; and transparent and platform-independent code development.

Artificial intelligence 101 for veterinary diagnostic imaging.

The prevalence and pervasiveness of artificial intelligence (AI) with medical images in veterinary and human medicine is rapidly increasing. This article provides essential definitions of AI with medical images with a focus on veterinary radiology. Machine learning methods common in medical image analysis are compared, and a detailed description of convolutional neural networks commonly used in deep learning classification and regression models is provided. A brief introduction to natural language processing (NLP) and its utility in machine learning is also provided. NLP can economize the creation of "truth-data" needed when training AI systems for both diagnostic radiology and radiation oncology applications. The goal of this publication is to provide veterinarians, veterinary radiologists, and radiation oncologists the necessary background needed to understand and comprehend AI-focused research projects and publications.

Population-based phase II trial of stereotactic ablative radiotherapy (SABR) for up to 5 oligometastases: SABR-5.

BACKGROUND: Oligometastases refer to a state of disease where cancer has spread beyond the primary site, but is not yet widely metastatic, often defined as 1-3 or 1-5 metastases in number. Stereotactic ablative radiotherapy (SABR) is an emerging radiotherapy technique to treat oligometastases that require further prospective population-based toxicity estimates. METHODS: This is a non-randomized phase II trial where all participants will receive experimental SABR treatment to all sites of newly diagnosed or progressing oligometastatic disease. We will accrue 200 patients to assess toxicity associated with this experimental treatment. The study was powered to give a 95% confidence on the risk of late grade 4 toxicity, anticipating a < 5% rate of grade 4 toxicity. DISCUSSION: SABR treatment of oligometastases is occurring off-trial at a high rate, without sufficient evidence of its efficacy or toxicity. This trial will provide necessary toxicity data in a population-based cohort, using standardized doses and organ at risk constraints, while we await data on efficacy from randomized phase III trials. TRIAL REGISTRATION: Registered through clinicaltrials.gov NCT02933242 on October 14, 2016 prospectively before patient accrual.

COMP Report: CPQR Technical Quality Control Guidelines for Data Management Systems.

The Canadian Organization of Medical Physicists (COMP), in close partnership with the Canadian Partnership for Quality Radiotherapy (CPQR) has developed a series of Technical Quality Control (TQC) guidelines for radiation treatment equipment. These guidelines outline the performance objectives that equipment should meet in order to ensure an acceptable level of radiation treatment quality. The TQC guidelines have been rigorously reviewed and field tested in a variety of Canadian radiation treatment facilities. The development process enables rapid review and update to keep the guidelines current with changes in technology (the most updated version of this guideline can be found on the CPQR website). This particular TQC details recommended quality control testing of radiation data management systems.

Reducing radiation risks to staff for patients with permanently implanted radioactive sources requiring unrelated surgery.

Permanent implant of sealed radioactive sources is an effective technique for treating cancer. Typically, the radioactive sources are implanted in and near the disease, depositing radiation absorbed dose locally over several months. There may be instances where these patients must undergo unrelated surgical procedures when the radioactive material remains active enough to pose risks. This work explores these risks, discusses strategies to mitigate those risks, and describes a case study for a permanent iodine-125 (I-125) prostate brachytherapy implant patient who developed colorectal cancer and required surgery six months after brachytherapy. The first consideration is identifying the radiological risk to the patient and staff before, during, and after the surgical procedure. The second is identifying the risk the surgical procedure may have on the efficacy of the brachytherapy implant. Finally, there are considerations for controlling the radioactive substances from a regulatory perspective. After these risks are defined, strategies to mitigate those risks are considered. We summarize this experience with some guidelines: If the surgical procedure is near (e.g., within 5-10 cm of) the implant; and, the surgical intervention may dislodge sources enough to compromise treatment or introduces radiation safety risks; and, the radioactivity has not sufficiently decayed to background levels; and, the surgery cannot be postponed, then a detailed analysis of risk is advised.

What's in the box? A toolbox for safe deployment of artificial intelligence in veterinary medicine.

This report describes a comprehensive framework for applying artificial intelligence (AI) in veterinary medicine. Our framework draws on existing research on AI implementation in human medicine and addresses the challenges of limited technology expertise and the need for scalability. The critical components of this framework include assembling a diverse team of experts in AI, promoting a foundational understanding of AI among veterinary professionals, identifying relevant use cases and objectives, ensuring data quality and availability, creating an effective implementation plan, providing team training, fostering collaboration, considering ethical and legal obligations, integrating AI into existing workflows, monitoring and evaluating performance, managing change effectively, and staying up-to-date with technological advancements. Incorporating AI into veterinary medicine requires addressing unique ethical and legal considerations, including data privacy, owner consent, and the impact of AI outputs on decision-making. Effective change management principles aid in avoiding disruptions and building trust in AI technology. Furthermore, continuous evaluation of AI's relevance in veterinary practice ensures that the benefits of AI translate into meaningful improvements in patient care.

AAPM WGVRTO report 390: A survey of veterinary radiation oncology equipment and infrastructure in 2022.

Since 2010, there has been little published data on the state of equipment and infrastructure in veterinary radiation oncology clinical practice. These data are important not only to identify the status and use of technology within the veterinary radiation oncology community but also to help identify the extent of medical physics support. The purpose of our study is to report findings from a survey of veterinary radiation oncologists in the USA, Canada, and select centers outside of North America in 2022. A 40-question survey covering topics such as type of radiotherapy equipment, techniques offered, treatment planning systems and dose calculation algorithms, special techniques, board-certified radiation oncologists and residents, and extent of medical physics support was distributed through an online survey tool. Results from 40 veterinary radiation oncology institutions, with equipment explicitly used for veterinary care, suggest that the current state of practice is not dissimilar to what currently exists in human radiation oncology facilities; techniques and technologies commonly employed include flattening filter-free mode megavoltage beams, volumetric arc therapy, daily cone-beam computed tomography, image-guided radiation therapy, and sophisticated dose calculation algorithms. These findings suggest the need for modern radiation oncology acceptance testing, commissioning, and quality assurance programs within the veterinary community. The increase in veterinary radiation oncology residency positions and increasing sophistication of equipment suggests that increased levels of standardized medical physics support would benefit the veterinary radiation oncology community.

Description and efficacy of a response-based "QUAD" cyclical hypofractionated palliative-intent radiation protocol in dogs with macroscopic solid tumours: 108 cases.

Palliative-intent radiation therapy can alleviate pain and clinical signs in dogs with cancer, but optimal fractionation scheme is unknown. The objective of this retrospective case series is to evaluate clinical benefit, objective response, adverse effects, and outcomes in 108 dogs with macroscopic solid tumours treated with a cyclical "QUAD" hypofractionated palliative-intent radiation therapy protocol. Median QUAD dose was 14 Gy (14-16 Gy). Median total dose was 28 Gy (14-48 Gy). Clinical benefit rate was 93%, with median onset of subjective palliation 21 days after the first QUAD, lasting a median of 134 days. Tumour volumetric objective response was assessed with CT prior to the third QUAD in 36 dogs, with stable disease in 24 dogs (67%) and partial response in 9 dogs (25%). Sinonasal and oral were the most common tumour locations in 32 and 30 dogs, respectively. Median progression-free survival was 153 days (95% CI 114-200). Median overall survival was 212 days (95% CI 152-259). Number of QUAD cycles completed, clinical benefit achieved, anti-inflammatory received, total radiation dose, time to maximum clinical benefit, and response duration were positively associated with progression-free and overall survival. Acute toxicities were observed in 15 dogs (14%) with 3 high-grade (grade 3) toxicities (3%). Low-grade (grade 1 and 2) late skin and ocular toxicities were observed in 31 dogs (29%), predominantly leukotrichia, alopecia, keratoconjunctivitis sicca, and cataracts. This report demonstrates that QUAD radiation is an alternative protocol to be considered for palliation of dogs with inoperable or advanced stage solid tumours.

Metacarpophalangeal Joint Pathology and Bone Mineral Density Increase with Exercise but Not with Incidence of Proximal Sesamoid Bone Fracture in Thoroughbred Racehorses.

Proximal sesamoid bone (PSB) fracture is the leading cause of fatal musculoskeletal injury in Thoroughbred racehorses in Hong Kong and the US. Efforts are underway to investigate diagnostic modalities that could help identify racehorses at increased risk of fracture; however, features associated with PSB fracture risk are still poorly understood. The objectives of this study were to (1) investigate third metacarpal (MC3) and PSB density and mineral content using dual-energy X-ray absorptiometry (DXA), computed tomography (CT), Raman spectroscopy, and ash fraction measurements, and (2) investigate PSB quality and metacarpophalangeal joint (MCPJ) pathology using Raman spectroscopy and CT. Forelimbs were collected from 29 Thoroughbred racehorse cadavers (n = 14 PSB fracture, n = 15 control) for DXA and CT imaging, and PSBs were sectioned for Raman spectroscopy and ash fraction measurements. Bone mineral density (BMD) was greater in MC3 condyles and PSBs of horses with more high-speed furlongs. MCPJ pathology, including palmar osteochondral disease (POD), MC3 condylar sclerosis, and MC3 subchondral lysis were greater in horses with more high-speed furlongs. There were no differences in BMD or Raman parameters between fracture and control groups; however, Raman spectroscopy and ash fraction measurements revealed regional differences in PSB BMD and tissue composition. Many parameters, including MC3 and PSB bone mineral density, were strongly correlated with total high-speed furlongs.

CT, MR, and ultrasound image artifacts from prostate brachytherapy seed implants: the impact of seed size.

PURPOSE: To investigate the effects of brachytherapy seed size on the quality of x-ray computed tomography (CT), ultrasound (US), and magnetic resonance (MR) images and seed localization through comparison of the 6711 and 9011 (125)I sources. METHODS: For CT images, an acrylic phantom mimicking a clinical implantation plan and embedded with low contrast regions of interest (ROIs) was designed for both the 0.774 mm diameter 6711 (standard) and the 0.508 mm diameter 9011 (thin) seed models (Oncura, Inc., and GE Healthcare, Arlington Heights, IL). Image quality metrics were assessed using the standard deviation of ROIs between the seeds and the contrast to noise ratio (CNR) within the low contrast ROIs. For US images, water phantoms with both single and multiseed arrangements were constructed for both seed sizes. For MR images, both seeds were implanted into a porcine gel and imaged with pelvic imaging protocols. The standard deviation of ROIs and CNR values were used as metrics of artifact quantification. Seed localization within the CT images was assessed using the automated seed finder in a commercial brachytherapy treatment planning system. The number of erroneous seed placements and the average and maximum error in seed placements were recorded as metrics of the localization accuracy. RESULTS: With the thin seeds, CT image noise was reduced from 48.5 ± 0.2 to 32.0 ± 0.2 HU and CNR improved by a median value of 74% when compared with the standard seeds. Ultrasound image noise was measured at 50.3 ± 17.1 dB for the thin seed images and 50.0 ± 19.8 dB for the standard seed images, and artifacts directly behind the seeds were smaller and less prominent with the thin seed model. For MR images, CNR of the standard seeds reduced on average 17% when using the thin seeds for all different imaging sequences and seed orientations, but these differences are not appreciable. Automated seed localization required an average (±SD) of 7.0 ± 3.5 manual corrections in seed positions for the thin seed scans and 3.0 ± 1.2 manual corrections in seed positions for the standard seed scans. The average error in seed placement was 1.2 mm for both seed types and the maximum error in seed placement was 2.1 mm for the thin seed scans and 1.8 mm for the standard seed scans. CONCLUSIONS: The 9011 thin seeds yielded significantly improved image quality for CT and US images but no significant differences in MR image quality.

The unmet potential of artificial intelligence in veterinary medicine.

Veterinary medicine is a broad and growing discipline that includes topics such as companion animal health, population medicine and zoonotic diseases, and agriculture. In this article, we provide insight on how artificial intelligence works and how it is currently applied in veterinary medicine. We also discuss its potential in veterinary medicine. Given the rapid pace of research and commercial product developments in this area, the next several years will pose challenges to understanding, interpreting, and adopting this powerful and evolving technology. Artificial intelligence has the potential to enable veterinarians to perform tasks more efficiently while providing new insights for the management and treatment of disorders. It is our hope that this will translate to better quality of life for animals and those who care for them.

Artificial intelligence in veterinary medicine.

Artificial intelligence (AI) is a branch of computer science in which computer systems are designed to perform tasks that mimic human intelligence. Today, AI is reshaping day-to-day life and has numerous emerging medical applications poised to profoundly reshape the practice of veterinary medicine. In this Currents in One Health, we discuss the essential elements of AI for veterinary practitioners with the aim to help them make informed decisions in applying AI technologies into their practices. Veterinarians will play an integral role in ensuring the appropriate uses and good curation of data. The expertise of veterinary professionals will be vital to ensuring good data and, subsequently, AI that meets the needs of the profession. Readers interested in an in-depth description of AI and veterinary medicine are invited to explore a complementary manuscript of this Currents in One Health available in the May 2022 issue of the American Journal of Veterinary Research.

Separable Confident Transductive Learning for Dairy Cows Teat-End Condition Classification.

Teat-end health assessments are crucial to maintain milk quality and dairy cow health. One approach to automate teat-end health assessments is by using a convolutional neural network to classify the magnitude of teat-end alterations based on digital images. This approach has been demonstrated as feasible with GoogLeNet but there remains a number of challenges, such as low performance and comparing performance with different ImageNet models. In this paper, we present a separable confident transductive learning (SCTL) model to improve the performance of teat-end image classification. First, we propose a separation loss to ameliorate the inter-class dispersion. Second, we generate high confident pseudo labels to optimize the network. We further employ transductive learning to narrow the gap between training and test datasets with categorical maximum mean discrepancy loss. Experimental results demonstrate that the proposed SCTL model consistently achieves higher accuracy across all seventeen different ImageNet models when compared with retraining of original approaches.

Radiomics Modeling of Catastrophic Proximal Sesamoid Bone Fractures in Thoroughbred Racehorses Using µCT.

Proximal sesamoid bone (PSB) fractures are the most common musculoskeletal injury in race-horses. X-ray CT imaging can detect expressed radiological features in horses that experienced catastrophic fractures. Our objective was to assess whether expressed radiomic features in the PSBs of 50 horses can be used to develop machine learning models for predicting PSB fractures. The µCTs of intact contralateral PSBs from 50 horses, 30 of which suffered catastrophic fractures, and 20 controls were studied. From the 129 intact µCT images of PSBs, 102 radiomic features were computed using a variety of voxel resampling dimensions. Decision Trees and Wrapper methods were used to identify the 20 top expressed features, and six machine learning algorithms were developed to model the risk of fracture. The accuracy of all machine learning models ranged from 0.643 to 0.903 with an average of 0.754. On average, Support Vector Machine, Random Forest (RUS Boost), and Log-regression models had higher performance than K-means Nearest Neighbor, Neural Network, and Random Forest (Bagged Trees) models. Model accuracy peaked at 0.5 mm and decreased substantially when the resampling resolution was greater than or equal to 1 mm. We find that, for this in vitro dataset, it is possible to differentiate between unfractured PSBs from case and control horses using µCT images. It may be possible to extend these findings to the assessment of fracture risk in standing horses.

Determining Planning Priorities for SABR for Oligometastatic Disease: A Secondary Analysis of the SABR-COMET Phase II Randomized Trial.

PURPOSE: SABR may improve survival in patients with oligometastases, but for some lesions, safe delivery of SABR may require a reduction in delivered dose or target coverage. This study assessed the association between target coverage compromise and oncologic and survival outcomes. METHODS AND MATERIALS: Patients with a controlled primary malignancy and 1 to 5 oligometastases were randomized (1:2) between standard of care (SOC) treatment and SOC plus SABR. In patients receiving SABR, the target dose coverage was reduced to meet organ at risk (OAR) constraints, if necessary. The D99 value (minimum dose received by the hottest 99% of the planning target volume [PTV]) was used as a measure of PTV coverage for each treatment plan, and the relationship between the coverage compromise index (CCI, defined as D99/prescription dose) and patient outcomes was assessed. RESULTS: Sixty-two patients in the SABR arm had dosimetric information available and a total of 109 lesions were evaluated. The mean CCI per lesion was 0.96 (95% CI, 0.56-1.61). Of the 109 lesions evaluated, 29.4% (n = 32) required coverage compromise (CCI <0.9). Adrenal metastases required coverage compromise in 100% of analyzed lesions (n = 7). CCI was not significantly associated with lesional control, adverse events, overall survival (OS), or progression-free survival (PFS). CONCLUSIONS: Target compromise was required in a substantial minority of cases, but PTV coverage was not associated with OS, progression-free survival, or lesional control. This suggests that OAR constraints used for SABR treatments in the oligometastatic setting should continue to be prioritized during planning.

CT image artifacts from brachytherapy seed implants: a postprocessing 3D adaptive median filter.

PURPOSE: To design a postprocessing 3D adaptive median filter that minimizes streak artifacts and improves soft-tissue contrast in postoperative CT images of brachytherapy seed implantations. METHODS: The filter works by identifying voxels that are likely streaks and estimating more reflective voxel intensity by using voxel intensities in adjacent CT slices and applying a median filter over voxels not identified as seeds. Median values are computed over a 5 x 5 x 5 mm region of interest (ROI) within the CT volume. An acrylic phantom simulating a clinical seed implant arrangement and containing nonradioactive seeds was created. Low contrast subvolumes of tissuelike material were also embedded in the phantom. Pre- and postprocessed image quality metrics were compared using the standard deviation of ROIs between the seeds, the CT numbers of low contrast ROIs embedded within the phantom, the signal to noise ratio (SNR), and the contrast to noise ratio (CNR) of the low contrast ROIs. The method was demonstrated with a clinical postimplant CT dataset. RESULTS: After the filter was applied, the standard deviation of CT values in streak artifact regions was significantly reduced from 76.5 to 7.2 HU. Within the observable low contrast plugs, the mean of all ROI standard deviations was significantly reduced from 60.5 to 3.9 HU, SNR significantly increased from 2.3 to 22.4, and CNR significantly increased from 0.2 to 4.1 (all P < 0.01). The mean CT in the low contrast plugs remained within 5 HU of the original values. CONCLUSION: An efficient postprocessing filter that does not require access to projection data, which can be applied irrespective of CT scan parameters has been developed, provided the slice thickness and spacing is 3 mm or less.