High sensitivity methods for automated rib fracture detection in pediatric radiographs.

Rib fractures are highly predictive of non-accidental trauma in children under 3 years old. Rib fracture detection in pediatric radiographs is challenging because fractures can be obliquely oriented to the imaging detector, obfuscated by other structures, incomplete, and non-displaced. Prior studies have shown up to two-thirds of rib fractures may be missed during initial interpretation. In this paper, we implemented methods for improving the sensitivity (i.e. recall) performance for detecting and localizing rib fractures in pediatric chest radiographs to help augment performance of radiology interpretation. These methods adapted two convolutional neural network (CNN) architectures, RetinaNet and YOLOv5, and our previously proposed decision scheme, "avalanche decision", that dynamically reduces the acceptance threshold for proposed regions in each image. Additionally, we present contributions of using multiple image pre-processing and model ensembling techniques. Using a custom dataset of 1109 pediatric chest radiographs manually labeled by seven pediatric radiologists, we performed 10-fold cross-validation and reported detection performance using several metrics, including F2 score which summarizes precision and recall for high-sensitivity tasks. Our best performing model used three ensembled YOLOv5 models with varied input processing and an avalanche decision scheme, achieving an F2 score of 0.725 ± 0.012. Expert inter-reader performance yielded an F2 score of 0.732. Results demonstrate that our combination of sensitivity-driving methods provides object detector performance approaching the capabilities of expert human readers, suggesting that these methods may provide a viable approach to identify all rib fractures.

Machine-Learned Algorithms to Predict the Risk of Pneumothorax Requiring Chest Tube Placement after Lung Biopsy.

PURPOSE: To develop a machine-learned algorithm to predict the risk of postlung biopsy pneumothorax requiring chest tube placement (CTP) to facilitate preprocedural decision making, optimize patient care, and improve resource allocation. MATERIALS AND METHODS: This retrospective study collected clinical and imaging features of biopsy samples obtained from patients with lung nodule biopsy and included information from 59 procedures resulting in pneumothorax requiring CTP and randomly selected 67 procedures without CTP (convenience sample). The data were divided into 70 and 30 as training and testing sets, respectively. Conventional machine-learned binary classifiers were explored with preprocedural imaging and clinical data as input features and CTP as the output. RESULTS: There was no single pathognomonic imaging or predictive clinical feature. For the independent test set under the high-specificity mode, a decision tree, logistic regression, and Naïve Bayes classifier achieved accuracies of identifying CTP at 0.79, 0.93, and 0.89 and area under receiver operating curves (AUROCs) of 0.68, 0.76, and 0.82, respectively. Under high-sensitivity mode, a decision tree, logistic regression, and Naïve Bayes achieved accuracies of identifying CTP of 0.60, 0.45, and 0.60 with AUROCs of 0.71, 0.81, and 0.82, respectively. High importance features included lesion character, chronic obstructive pulmonary disease, lesion depth, and age. A coarse decision tree requiring 4 inputs achieved comparable performance as other methods and previous machine learning prediction studies. CONCLUSIONS: The results support the possibility of predicting pneumothorax requiring CTP after biopsy based on an automated decision support, reliant on readily available preprocedural information.

Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring.

Current methods of in vivo imaging islet cell transplants for diabetes using magnetic resonance imaging (MRI) are limited by their low sensitivity. Simultaneous positron emission tomography (PET)/MRI has greater sensitivity and ability to visualize cell metabolism. However, this dual-modality tool currently faces two major challenges for monitoring cells. Primarily, the dynamic conditions of PET such as signal decay and spatiotemporal change in radioactivity prevent accurate quantification of the transplanted cell number. In addition, selection bias from different radiologists renders human error in segmentation. This calls for the development of artificial intelligence algorithms for the automated analysis of PET/MRI of cell transplantations. Here, we combined K-means++ for segmentation with a convolutional neural network to predict radioactivity in cell-transplanted mouse models. This study provides a tool combining machine learning with a deep learning algorithm for monitoring islet cell transplantation through PET/MRI. It also unlocks a dynamic approach to automated segmentation and quantification of radioactivity in PET/MRI.

Incorporating Tantalum Oxide Nanoparticles into Implantable Polymeric Biomedical Devices for Radiological Monitoring.

Longitudinal radiological monitoring of biomedical devices is increasingly important, driven by the risk of device failure following implantation. Polymeric devices are poorly visualized with clinical imaging, hampering efforts to use diagnostic imaging to predict failure and enable intervention. Introducing nanoparticle contrast agents into polymers is a potential method for creating radiopaque materials that can be monitored via computed tomography. However, the properties of composites may be altered with nanoparticle addition, jeopardizing device functionality. Thus, the material and biomechanical responses of model nanoparticle-doped biomedical devices (phantoms), created from 0-40 wt% tantalum oxide (TaOx ) nanoparticles in polycaprolactone and poly(lactide-co-glycolide) 85:15 and 50:50, representing non, slow, and fast degrading systems, respectively, are investigated. Phantoms degrade over 20 weeks in vitro in simulated physiological environments: healthy tissue (pH 7.4), inflammation (pH 6.5), and lysosomal conditions (pH 5.5), while radiopacity, structural stability, mechanical strength, and mass loss are monitored. The polymer matrix determines overall degradation kinetics, which increases with lower pH and higher TaOx content. Importantly, all radiopaque phantoms could be monitored for a full 20 weeks. Phantoms implanted in vivo and serially imaged demonstrate similar results. An optimal range of 5-20 wt% TaOx nanoparticles balances radiopacity requirements with implant properties, facilitating next-generation biomedical devices.

Incorporating Radiopacity into Implantable Polymeric Biomedical Devices for Clinical Radiological Monitoring.

Longitudinal radiological monitoring of biomedical devices is increasingly important, driven by risk of device failure following implantation. Polymeric devices are poorly visualized with clinical imaging, hampering efforts to use diagnostic imaging to predict failure and enable intervention. Introducing nanoparticle contrast agents into polymers is a potential method for creating radiopaque materials that can be monitored via computed tomography. However, properties of composites may be altered with nanoparticle addition, jeopardizing device functionality. This, we investigated material and biomechanical response of model nanoparticle-doped biomedical devices (phantoms), created from 0-40wt% TaO x nanoparticles in polycaprolactone, poly(lactide-co-glycolide) 85:15 and 50:50, representing non-, slow and fast degrading systems, respectively. Phantoms degraded over 20 weeks in vitro, in simulated physiological environments: healthy tissue (pH 7.4), inflammation (pH 6.5), and lysosomal conditions (pH 5.5), while radiopacity, structural stability, mechanical strength and mass loss were monitored. The polymer matrix determined overall degradation kinetics, which increased with lower pH and higher TaO x content. Importantly, all radiopaque phantoms could be monitored for a full 20-weeks. Phantoms implanted in vivo and serially imaged, demonstrated similar results. An optimal range of 5-20wt% TaO x nanoparticles balanced radiopacity requirements with implant properties, facilitating next-generation biomedical devices.

Phantom and methodology for comparison of small lesion detectability in PET.

PURPOSE: The main goal of this work is to describe a phantom design, data acquisition and data analysis methodology enabling comparison of small lesion detectability between PET imaging systems and reconstruction algorithms. Several methods are currently available to characterize intrinsic and image quality performance, but none focus exclusively on small lesion detectability. METHODS: We previously developed a small-lesion detection phantom and described initial results using a head-size phantom. Unlike most fillable nuclear medicine phantoms, this phantom offers a semi-realistic heterogenous background and wall-less contrast features. In this work, the methodology is extended to include (a) the use of both head- and body-sized phantoms and (b) a multi-scan data collection and analysis method. We present an example use case of the phantom and detection estimation methodology, comparing the small-lesion detection performance across four commercial PET/CT systems. RESULTS: Repeat acquisitions of the phantom enabled estimation of model observer performance and surrogates of detectability. As anticipated, estimated detectability increased with the square root of system sensitivity and TOF offered marked improvement in detectability, especially for the body sized object. The proposed approach characterizing detectability at different times during the decay of the phantom enabled comparison of small lesion detectability at matched activity concentrations (and scan durations) across different scanners. CONCLUSION: The proposed approach offers a reproducible tool for evaluating relative tradeoffs of system performance on small lesion detectability.

3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm.

Stem cell-derived islet organoids constitute a promising treatment of type 1 diabetes. A major hurdle in the field is the lack of appropriate in vivo method to determine graft outcome. Here, we investigate the feasibility of in vivo tracking of transplanted stem cell-derived islet organoids using magnetic particle imaging (MPI) in a mouse model. Human induced pluripotent stem cells-L1 were differentiated to islet organoids and labeled with superparamagnetic iron oxide nanoparticles. The phantoms comprising of different numbers of labeled islet organoids were imaged using an MPI system. Labeled islet organoids were transplanted into NOD/scid mice under the left kidney capsule and were then scanned using 3D MPI at 1, 7, and 28 days post transplantation. Quantitative assessment of the islet organoids was performed using the K-means++ algorithm analysis of 3D MPI. The left kidney was collected and processed for immunofluorescence staining of C-peptide and dextran. Islet organoids expressed islet cell markers including insulin and glucagon. Image analysis of labeled islet organoids phantoms revealed a direct linear correlation between the iron content and the number of islet organoids. The K-means++ algorithm showed that during the course of the study the signal from labeled islet organoids under the left kidney capsule decreased. Immunofluorescence staining of the kidney sections showed the presence of islet organoid grafts as confirmed by double staining for dextran and C-peptide. This study demonstrates that MPI with machine learning algorithm analysis can monitor islet organoids grafts labeled with super-paramagnetic iron oxide nanoparticles and provide quantitative information of their presence in vivo.

Multi-Objective Evolutionary Algorithm for PET Image Reconstruction: Concept.

In many diagnostic imaging settings, including positron emission tomography (PET), images are typically used for multiple tasks such as detecting disease and quantifying disease. Unlike conventional image reconstruction that optimizes a single objective, this work proposes a multi-objective optimization algorithm for PET image reconstruction to identify a set of images that are optimal for more than one task. This work is reliant on a genetic algorithm to evolve a set of solutions that satisfies two distinct objectives. In this paper, we defined the objectives as the commonly used Poisson log-likelihood function, typically reflective of quantitative accuracy, and a variant of the generalized scan-statistic model, to reflect detection performance. The genetic algorithm uses new mutation and crossover operations at each iteration. After each iteration, the child population is selected with non-dominated sorting to identify the set of solutions along the dominant front or fronts. After multiple iterations, these fronts approach a single non-dominated optimal front, defined as the set of PET images for which none the objective function values can be improved without reducing the opposing objective function. This method was applied to simulated 2D PET data of the heart and liver with hot features. We compared this approach to conventional, single-objective approaches for trading off performance: maximum likelihood estimation with increasing explicit regularization and maximum a posteriori estimation with varying penalty strength. Results demonstrate that the proposed method generates solutions with comparable to improved objective function values compared to the conventional approaches for trading off performance amongst different tasks. In addition, this approach identifies a diverse set of solutions in the multi-objective function space which can be challenging to estimate with single-objective formulations.

Patient factors and outcomes associated with discordance between quantitative and qualitative cardiac PET ischemia information.

BACKGROUND: Cardiac PET can provide quantitative myocardial blood flow (MBF) estimates. The frequency and clinical significance of discordant ischemia information between quantitative and qualitative parameters is unclear. METHODS: This retrospective, cohort study analyzed 256 Rb-82 stress-rest PET/CT studies. Global MBF and myocardial flow reserve (MFR) were estimated in absolute units for quantitative results and sum-stress and difference scores were used for qualitative results. Four groups of patients were evaluated based on a specific definition of concordant and discordant quantitative and qualitative results. RESULTS: 31% of cases demonstrated discordance. Factors associated with microvascular disease were more common in the groups with abnormal quantitative results, regardless of the qualitative findings. Patients with concordant abnormal results had a significantly increased risk of myocardial infarction, heart failure, percutaneous intervention, and all-cause-mortality at 1 year compared to patients with concordant normal results. In patients with discordant results of abnormal quantitative and normal qualitative findings, there was a higher prevalence of heart failure than in controls (12.5% vs 0%, p = 0.01). CONCLUSIONS: Discordance in qualitative and quantitative ischemia measures from PET is common, and further study is needed to clarify its prognostic implications. Moreover, quantitative estimation of MBF and MFR appears to add value to qualitative visual interpretation by supporting qualitative findings when results are concordant. Abnormal quantitative findings, regardless of concordance or discordance with qualitative findings, occurred in patients with risk factors associated with diffuse disease and with increased risk of heart failure admission.

SNMMI Procedure Standard/EANM Practice Guideline on Pediatric 18F-FDG PET/CT for Oncology 1.0.

PREAMBLEThe Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and professional organization founded in 1954 to promote the science, technology, and practical application of nuclear medicine. The European Association of Nuclear Medicine (EANM) is a professional nonprofit medical association founded in 1985 to facilitate communication worldwide among individuals pursuing clinical and academic excellence in nuclear medicine. SNMMI and EANM members are physicians, technologists, and scientists specializing in the research and practice of nuclear medicine.The SNMMI and EANM will periodically put forth new standards/guidelines for nuclear medicine practice to help advance the science of nuclear medicine and improve service to patients. Existing standards/guidelines will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner, if indicated. Each standard/guideline, representing a policy statement by the SNMMI/EANM, has undergone a thorough consensus process, entailing extensive review. The SNMMI and EANM recognize that the safe and effective use of diagnostic nuclear medicine imaging requires particular training and skills, as described in each document. These standards/guidelines are educational tools designed to assist practitioners in providing appropriate and effective nuclear medicine care for patients. These guidelines are consensus documents, and are not inflexible rules or requirements of practice. They are not intended, nor should they be used, to establish a legal standard of care. For these reasons and those set forth below, the SNMMI and the EANM cautions against the use of these standards/guidelines in litigation in which the clinical decisions of a practitioner are called into question.The ultimate judgment regarding the propriety of any specific procedure or course of action must be made by medical professionals taking into account the unique circumstances of each case. Thus, there is no implication that action differing from what is laid out in the standards/guidelines, standing alone, is below standard of care. To the contrary, a conscientious practitioner may responsibly adopt a course of action different from that set forth in the standards/guidelines when, in the reasonable judgment of the practitioner, such course of action is indicated by the condition of the patient, limitations of available resources, or advances in knowledge or technology subsequent to publication of the standards/guidelines.The practice of medicine involves not only the science, but also the art of dealing with the prevention, diagnosis, alleviation, and treatment of disease. The variety and complexity of human conditions make it impossible for general guidelines to consistently allow for an accurate diagnosis to be reached or a particular treatment response to be predicted. Therefore, it should be recognized that adherence to these standards/guidelines will not ensure a successful outcome. All that should be expected is that the practitioner follows a reasonable course of action, based on their level of training, the current knowledge, the available resources, and the needs/context of the particular patient being treated.PET and computerized tomography (CT) have been widely used in oncology. 18F-FDG is the most common radiotracer used for PET imaging. The purpose of this document is to provide imaging specialists and clinicians guidelines for recommending, performing, and interpreting 18F-FDG PET/CT in pediatric patients in oncology. There is not a high level of evidence for all recommendations suggested in this paper. These recommendations represent the expert opinions of experienced leaders in this field. Further studies are needed to have evidence-based recommendations for the application of 18F-FDG PET/CT in pediatric oncology. These recommendations should be viewed in the context of good practice of nuclear medicine and are not intended to be a substitute for national and international legal or regulatory provisions.

Sharing and Selling Images: Ethical and Regulatory Considerations for Radiologists.

Opportunities to share or sell images are common in radiology. But because these images typically originate as protected health information, their use admits a host of ethical and regulatory considerations. This article discusses four scenarios that reflect data sharing or selling arrangements in radiology, especially as they might occur in "big data" systems or applications. The objective of this article is to acquaint radiologists with a variety of regulatory standards and ethical perspectives that pertain to certain data use agreements, such that the attitudes and practices of data holders and their sharers or purchasers can withstand ethical or regulatory scrutiny and not invite undesirable outcomes.

Use of Dual-Energy CT for Quantification of Essential Trace Metals (Iron, Copper, and Zinc): Proof of Concept.

OBJECTIVE. Altered concentrations of essential trace metals have been associated with the development of abdominal tumors. We developed a method to quantify trace metals (iron, copper, and zinc) using monochromatic data from commercially available dual-energy CT (DECT) implementations. CONCLUSION. Our data provide a foundation for the use of DECT for noninvasive quantification of essential trace metals. Minimum detectable concentrations of iron and zinc estimated with DECT overlap with in vivo hepatic concentrations reported in the literature.

Correction to: Ovarian torsion: developing a machine-learned algorithm for diagnosis.

The original version of this paper included errors in Fig. 3. The corrected Fig. 3 is presented here.

Ovarian torsion: developing a machine-learned algorithm for diagnosis.

BACKGROUND: Ovarian torsion is a common concern in girls presenting to emergency care with pelvic or abdominal pain. The diagnosis is challenging to make accurately and quickly, relying on a combination of physical exam, history and radiologic evaluation. Failure to establish the diagnosis in a timely fashion can result in irreversible ovarian ischemia with implications for future fertility. Ultrasound is the mainstay of evaluation for ovarian torsion in the pediatric population. However, even with a high index of suspicion, imaging features are not pathognomonic. OBJECTIVE: We sought to develop an algorithm to aid radiologists in diagnosing ovarian torsion using machine learning from sonographic features and to evaluate the frequency of each sonographic element. MATERIALS AND METHODS: All pediatric patients treated for ovarian torsion at a quaternary pediatric hospital over an 11-year period were identified by both an internal radiology database and hospital-based International Statistical Classification of Diseases and Related Health Problems (ICD) code review. Inclusion criteria were surgical confirmation of ovarian torsion and available imaging. Patients were excluded if the diagnosis could not be confirmed, no imaging was available for review, the ovary was not identified by imaging, or torsion involved other adnexal structures but spared the ovary. Data collection included: patient age; laterality of torsion; bilateral ovarian volumes; torsed ovarian position, i.e. whether medialized with respect to the mid-uterine line; presence or absence of Doppler signal within the torsed ovary; visualization of peripheral follicles; and presence of a mass or cyst, and free peritoneal fluid. Subsequently, we evaluated a non-torsed control cohort from April 2015 to May 2016. This cohort consisted of sequential girls and young adults presenting to the emergency department with abdominopelvic symptoms concerning for ovarian torsion but who were ultimately diagnosed otherwise. These features were then fed into supervised machine learning systems to identify and develop viable decision algorithms. We divided data into training and validation sets and assessed algorithm performance using sub-sets of the validation set. RESULTS: We identified 119 torsion-confirmed cases and 331 torsion-absent cases. Of the torsion-confirmed cases, significant imaging differences were evident for girls younger than 1 year; these girls were then excluded from analysis, and 99 pediatric patients older than 1 year were included in our study. Among these 99, all variables demonstrated statistically significant differences between the torsion-confirmed and torsion-absent groups with P-values <0.005. Using any single variable to identify torsion provided only modest detection performance, with areas under the curve (AUC) for medialization, peripheral follicles, and absence of Doppler flow of 0.76±0.16, 0.66±0.14 and 0.82±0.14, respectively. The best decision tree using a combination of variables yielded an AUC of 0.96±0.07 and required knowledge of the presence of intra-ovarian flow, peripheral follicles, the volume of both ovaries, and the presence of cysts or masses. CONCLUSION: Based on the largest series of pediatric ovarian torsion in the literature to date, we quantified sonographic features and used machine learning to create an algorithm to identify the presence of ovarian torsion - an algorithm that performs better than simple approaches relying on single features. Although complex combinations using multiple-interaction models provide slightly better performance, a clinically pragmatic decision tree can be employed to detect torsion, providing sensitivity levels of 95±14% and specificity of 92±2%.

Technical Note: A digital reference object representing Hoffman's 3D brain phantom for PET scanner simulations.

PURPOSE: Physical and digital phantoms play a key role in the development and testing of nuclear medicine instrumentation and processing algorithms for clinical and research applications, including neuroimaging using positron emission tomography (PET). We have developed and tested a digital reference object (DRO) version of the original segmented magnetic resonance imaging (MRI) data used for the three-dimensional (3D) PET brain phantom developed by Hoffman et al., which is used as the basis of a commercially available physical test phantom. METHODS: The DRO was constructed by subdividing the MRI image planes the original phantom was based on to create equal-thickness slices and re-labeling voxels. The digital data was then embedded in a PET Digital Imaging and Communications in Medicine format and tested for compliance. RESULTS: We then tested the DRO by comparing it to computed tomography (CT) images of the physical phantom summed to form composite slices with axial extent similar to the DRO, but with a factor of two better in-slice resolution. For composite slices, 91% of voxels were labeled in full agreement, 5% of the voxels were 50-75% accurate, and the remaining 4% of voxels had 25% or less agreement. CONCLUSIONS: This DRO can be used as an input for PET scanner simulation studies or for comparing simulations to measured Hoffman phantom images.

Optimization of reconstruction and quantification of motion-corrected coronary PET-CT.

BACKGROUND: Coronary PET shows promise in the detection of high-risk atherosclerosis, but there remains a need to optimize imaging and reconstruction techniques. We investigated the impact of reconstruction parameters and cardiac motion-correction in 18F Sodium Fluoride (18F-NaF) PET. METHODS: Twenty-two patients underwent 18F-NaF PET within 22 days of an acute coronary syndrome. Optimal reconstruction parameters were determined in a subgroup of six patients. Motion-correction was performed on ECG-gated data of all patients with optimal reconstruction. Tracer uptake was quantified in culprit and reference lesions by computing signal-to-noise ratio (SNR) in diastolic, summed, and motion-corrected images. RESULTS: Reconstruction using 24 subsets, 4 iterations, point-spread-function modelling, time of flight, and 5-mm post-filtering provided the highest median SNR (31.5) compared to 4 iterations 0-mm (22.5), 8 iterations 0-mm (21.1), and 8 iterations 5-mm (25.6; all P < .05). Motion-correction improved SNR of culprit lesions (n = 33) (24.5[19.9-31.5]) compared to diastolic (15.7[12.4-18.1]; P < .001) and summed data (22.1[18.9-29.2]; P < .001). Motion-correction increased the SNR difference between culprit and reference lesions (10.9[6.3-12.6]) compared to diastolic (6.2[3.6-10.3]; P = .001) and summed data (7.1 [4.8-11.6]; P = .001). CONCLUSIONS: The number of iterations and extent of post-filtering has marked effects on coronary 18F-NaF PET quantification. Cardiac motion-correction improves discrimination between culprit and reference lesions.

PET/CT-guided biopsy with respiratory motion correction.

PURPOSE: Given the ability of positron emission tomography (PET) imaging to localize malignancies in heterogeneous tumors and tumors that lack an X-ray computed tomography (CT) correlate, combined PET/CT-guided biopsy may improve the diagnostic yield of biopsies. However, PET and CT images are naturally susceptible to problems due to respiratory motion, leading to imprecise tumor localization and shape distortion. To facilitate PET/CT-guided needle biopsy, we developed and investigated the feasibility of a workflow that allows to bring PET image guidance into interventional CT suite while accounting for respiratory motion. METHODS: The performance of PET/CT respiratory motion correction using registered and summed phases method was evaluated through computer simulations using the mathematical 4D extended cardiac-torso phantom, with motion simulated from real respiratory traces. The performance of PET/CT-guided biopsy procedure was evaluated through operation on a physical anthropomorphic phantom. Vials containing radiolabeled 18F-fluorodeoxyglucose were placed within the physical phantom thorax as biopsy targets. We measured the average distance between target center and the simulated biopsy location among multiple trials to evaluate the biopsy localization accuracy. RESULTS: The computer simulation results showed that the RASP method generated PET images with a significantly reduced noise of 0.10 ± 0.01 standardized uptake value (SUV) as compared to an end-of-expiration image noise of 0.34 ± 0.04 SUV. The respiratory motion increased the apparent liver lesion size from 5.4 ± 1.1 to 35.3 ± 3.0 cc. The RASP algorithm reduced this to 15.7 ± 3.7 cc. The distances between the centroids for the static image lesion and two moving lesions in the liver and lung, when reconstructed with the RASP algorithm, were 0.83 ± 0.72 mm and 0.42 ± 0.72 mm. For the ungated imaging, these values increased to 3.48 ± 1.45 mm and 2.5 ± 0.12 mm, respectively. For the ungated imaging, this increased to 1.99 ± 1.72 mm. In addition, the lesion activity estimation (e.g., SUV) was accurate and constant for images reconstructed using the RASP algorithm, whereas large activity bias and variations (± 50%) were observed for lesions in the ungated images. The physical phantom studies demonstrated a biopsy needle localization error of 2.9 ± 0.9 mm from CT. Combined with the localization errors due to respiration for the PET images from simulations, the overall estimated lesion localization error would be 3.08 mm for PET-guided biopsies images using RASP and 3.64 mm when using ungated PET images. In other words, RASP reduced the localization error by approximately 0.6 mm. The combined error analysis showed that replacing the standard end-of-expiration images with the proposed RASP method in PET/CT-guided biopsy workflow yields comparable lesion localization accuracy and reduced image noise. CONCLUSION: The RASP method can produce PET images with reduced noise, attenuation artifacts and respiratory motion, resulting in more accurate lesion localization. Testing the PET/CT-guided biopsy workflow using computer simulation and physical phantoms with respiratory motion, we demonstrated that guided biopsy procedure with the RASP method can benefit from improved PET image quality due to noise reduction, without compromising the accuracy of lesion localization.

Accuracy of Myocardial Blood Flow Estimation From Dynamic Contrast-Enhanced Cardiac CT Compared With PET.

Background The accuracy of absolute myocardial blood flow (MBF) from dynamic contrast-enhanced cardiac computed tomography acquisitions has not been fully characterized. We evaluate computed tomography (CT) compared with rubidium-82 positron emission tomography (PET) MBF estimates in a high-risk population. Methods In a prospective trial, patients receiving clinically indicated rubidium-82 PET exams were recruited to receive a dynamic contrast-enhanced cardiac computed tomography exam. The CT protocol included a rest and stress dynamic portion each acquiring 12 to 18 cardiac-gated frames. The global MBF was estimated from the PET and CT exam. Results Thirty-four patients referred for cardiac rest-stress PET were recruited. Of the 68 dynamic contrast-enhanced cardiac computed tomography scans, 5 were excluded because of injection errors or mismatched hemodynamics. The CT-derived global MBF was highly correlated with the PET MBF (r=0.92; P<0.001) with a mean difference of 0.7±26.4%. The CT MBF estimates were within 20% of PET estimates ( P<0.02) with a mean of (1) MBF for resting flow of PET versus CT of 0.9±0.3 versus 1.0±0.2 mL/min per gram and (2) MBF for stress flow of 2.1±0.7 versus 2.0±0.8 mL/min per gram. Myocardial flow reserve was -14±28% underestimated with CT (PET versus CT myocardial flow reserve, 2.5±0.6 versus 2.2±0.6). The proposed rest+stress+computed tomography angiography protocol had a dose length product of 598±76 mGy×cm resulting in an approximate effective dose of 8.4±1.1 mSv. Conclusions In a high-risk clinical population, a clinically practical dynamic contrast-enhanced cardiac computed tomography provided unbiased MBF estimates within 20% of rubidium-82 PET. Although unbiased, the CT estimates contain substantial variance with an standard error of the estimate of 0.44 mL/min per gram. Myocardial flow reserve estimation was not as accurate as individual MBF estimates.

Performance evaluation of the 5-Ring GE Discovery MI PET/CT system using the national electrical manufacturers association NU 2-2012 Standard.

The GE Discovery MI PET/CT system has a modular digital detector design allowing three, four, or five detector block rings that extend the axial field-of-view (FOV) from 15 to 25 cm in 5 cm increments. This study investigated the performance of the 5-ring system and compared it to 3- and 4-ring systems; the GE Discovery IQ system that uses conventional photomultiplier tubes; and the GE Signa PET/MR system that has a reduced transaxial FOV. METHODS: PET performance was evaluated at three different institutions. Spatial resolution, sensitivity, counting rate performance, accuracy, and image quality were measured in accordance with National Electrical Manufacturers Association NU 2-2012 standards. The mean energy resolution, mean timing resolution, and PET/CT subsystem alignment were also measured. Phantoms were used to determine the effects of varying acquisition time and reconstruction parameters on image quality. Retrospective patient scans were reconstructed with various scan durations to evaluate the impact on image quality. RESULTS: Results from all three institutions were similar. Radial/tangential/axial full width at half maximum spatial resolution measurements using the filtered back projection algorithm were 4.3/4.3/5.0 mm, 5.5/4.6/6.5 mm, and 7.4/5.0/6.6 mm at 1, 10, and 20 cm from the center of the FOV, respectively. Measured sensitivity at the center of the FOV (20.84 cps/kBq) was significantly higher than systems with reduced axial FOV. The peak noise-equivalent counting rate was 266.3 kcps at 20.8 kBq/ml, with a corresponding scatter fraction of 40.2%. The correction accuracy for count losses up to the peak noise-equivalent counting rate was 3.6%. For the 10-, 13-, 17-, 22-, 28-, and 37-mm spheres, contrast recoveries in the image quality phantom were measured to be 46.2%, 54.3%, 66.1%, 71.1%, 85.3%, and 89.3%, respectively. The mean energy and timing resolution were 9.55% and 381.7 ps, respectively. Phantom and patient images demonstrated excellent image quality, even at short acquisition times or low injected activity. CONCLUSION: Compared to other PET/CT models, the extended axial FOV improved the overall PET performance of the 5-ring GE Discovery MI scanner. This system offers the potential to reduce scan times or injected activities through increased sensitivity.