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.

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.

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.

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%.

CT Detectability of Small Low-Contrast Hypoattenuating Focal Lesions: Iterative Reconstructions versus Filtered Back Projection.

Purpose To investigate performance in detectability of small (≤1 cm) low-contrast hypoattenuating focal lesions by using filtered back projection (FBP) and iterative reconstruction (IR) algorithms from two major CT vendors across a range of 11 radiation exposures. Materials and Methods A low-contrast detectability phantom consisting of 21 low-contrast hypoattenuating focal objects (seven sizes between 2.4 and 10.0 mm, three contrast levels) embedded into a liver-equivalent background was scanned at 11 radiation exposures (volume CT dose index range, 0.5-18.0 mGy; size-specific dose estimate [SSDE] range, 0.8-30.6 mGy) with four high-end CT platforms. Data sets were reconstructed by using FBP and varied strengths of image-based, model-based, and hybrid IRs. Sixteen observers evaluated all data sets for lesion detectability by using a two-alternative-forced-choice (2AFC) paradigm. Diagnostic performances were evaluated by calculating area under the receiver operating characteristic curve (AUC) and by performing noninferiority analyses. Results At benchmark exposure, FBP yielded a mean AUC of 0.79 ± 0.09 (standard deviation) across all platforms which, on average, was approximately 2% lower than that observed with the different IR algorithms, which showed an average AUC of 0.81 ± 0.09 (P = .12). Radiation decreases of 30%, 50%, and 80% resulted in similar declines of observer detectability with FBP (mean AUC decrease, -0.02 ± 0.05, -0.03 ± 0.05, and -0.05 ± 0.05, respectively) and all IR methods investigated (mean AUC decrease, -0.00 ± 0.05, -0.04 ± 0.05, and -0.04 ± 0.05, respectively). For each radiation level and CT platform, variance in performance across observers was greater than that across reconstruction algorithms (P = .03). Conclusion Iterative reconstruction algorithms have limited radiation optimization potential in detectability of small low-contrast hypoattenuating focal lesions. This task may be further complicated by a high degree of variation in radiologists' performances, seemingly exceeding real performance differences among reconstruction algorithms. © RSNA, 2018 Online supplemental material is available for this article.

Establishment of normative values for the fetal posterior fossa by magnetic resonance imaging.

OBJECTIVE: Suspected Dandy-Walker continuum anomalies constitute a significant percentage of prenatal cases evaluated by magnetic resonance imaging (MRI). To unify the description of posterior fossa malformations, we sought to establish objective measurements for the posterior fossa in normal fetuses between 18 and 37 weeks gestation. METHODS: T2-weighted images of normal fetal brains in sagittal projection were obtained from fetal magnetic resonance (MR) studies of normal brains performed from 2009 to 2017.121 fetal brains were included in the analysis. Three radiologists reviewed images and recorded the following for each case: superior posterior fossa angle (SPFA), posterior fossa perimeter, and tegmento-vermian angle (TVA). RESULTS: For each feature, the mean of the measurements, the percentage of absolute difference of the reader measurement compared with mean measurement, and the interclass correlation (ICC) were calculated. Values are reported as mean ± standard deviation. Perimeter increases linearly with age, whereas the SPFA and the TVA are independent of gestational age. For all included cases, the SPFA averaged 100.9° ± 8° and the TVA averaged 2.5° ± 2.3°. CONCLUSION: The superior posterior fossa angle, a novel measurement, and the posterior fossa perimeter can be used for establishing the expected size of the posterior fossa in second- and third-trimester fetuses by MRI.

Enhancing Cardiac PET by Motion Correction Techniques.

PURPOSE OF REVIEW: Cardiac positron emission tomography (PET) images often contain errors due to cardiac, respiratory, and patient motion during relatively long image acquisition. Advanced motion compensation techniques may improve PET spatial resolution, eliminate potential artifacts, and ultimately improve the research and clinical capabilities of PET. RECENT FINDINGS: Combined cardiac and respiratory gating has only recently been implemented in clinical PET systems. Considering that the gated image bins contain much lower counts than the original PET data, they need to be summed after correcting for motion, forming motion-corrected, high-count image volume. Furthermore, automated image registration techniques can be used to correct for motion between CT attenuation scan and PET acquisition. While motion correction methods are not yet widely used in clinical practice, approaches including dual-gated non-rigid motion correction and the incorporation of motion correction information into the reconstruction process have the potential to markedly improve cardiac PET imaging.

Direct Reconstruction of CT-based Attenuation Correction Images for PET with Cluster-Based Penalties.

Extremely low-dose CT acquisitions used for PET attenuation correction have high levels of noise and potential bias artifacts due to photon starvation. This work explores the use of a priori knowledge for iterative image reconstruction of the CT-based attenuation map. We investigate a maximum a posteriori framework with cluster-based multinomial penalty for direct iterative coordinate decent (dICD) reconstruction of the PET attenuation map. The objective function for direct iterative attenuation map reconstruction used a Poisson log-likelihood data fit term and evaluated two image penalty terms of spatial and mixture distributions. The spatial regularization is based on a quadratic penalty. For the mixture penalty, we assumed that the attenuation map may consist of four material clusters: air+background, lung, soft tissue, and bone. Using simulated noisy sinogram data, dICD reconstruction was performed with different strengths of the spatial and mixture penalties. The combined spatial and mixture penalties reduced the RMSE by roughly 2 times compared to a weighted least square and filtered backprojection reconstruction of CT images. The combined spatial and mixture penalties resulted in only slightly lower RMSE compared to a spatial quadratic penalty alone. For direct PET attenuation map reconstruction from ultra-low dose CT acquisitions, the combination of spatial and mixture penalties offers regularization of both variance and bias and is a potential method to reconstruct attenuation maps with negligible patient dose. The presented results, using a best-case histogram suggest that the mixture penalty does not offer a substantive benefit over conventional quadratic regularization and diminishes enthusiasm for exploring future application of the mixture penalty.

Morphology supporting function: attenuation correction for SPECT/CT, PET/CT, and PET/MR imaging.

Both SPECT, and in particular PET, are unique in medical imaging for their high sensitivity and direct link to a physical quantity, i.e. radiotracer concentration. This gives PET and SPECT imaging unique capabilities for accurately monitoring disease activity for the purposes of clinical management or therapy development. However, to achieve a direct quantitative connection between the underlying radiotracer concentration and the reconstructed image values several confounding physical effects have to be estimated, notably photon attenuation and scatter. With the advent of dual-modality SPECT/CT, PET/CT, and PET/MR scanners, the complementary CT or MR image data can enable these corrections, although there are unique challenges for each combination. This review covers the basic physics underlying photon attenuation and scatter and summarizes technical considerations for multimodal imaging with regard to PET and SPECT quantification and methods to address the challenges for each multimodal combination.

Relationships of pediatric anthropometrics for CT protocol selection.

OBJECTIVE: Determining the optimal CT technique to minimize patient radiation exposure while maintaining diagnostic utility requires patient-specific protocols that are based on patient characteristics. This work develops relationships between different anthropometrics and CT image noise to determine appropriate protocol classification schemes. MATERIALS AND METHODS: We measured the image noise in 387 CT examinations of pediatric patients (222 boys, 165 girls) of the chest, abdomen, and pelvis and generated mathematic relationships between image noise and patient lateral and anteroposterior dimensions, age, and weight. RESULTS: At the chest level, lateral distance (ld) across the body is strongly correlated with weight (ld = 0.23 × weight + 16.77; R(2) = 0.93) and is less well correlated with age (ld = 1.10 × age + 17.13; R(2) = 0.84). Similar trends were found for anteroposterior dimensions and at the abdomen level. Across all studies, when acquisition-specific parameters are factored out of the noise, the log of image noise was highly correlated with lateral distance (R(2) = 0.72) and weight (R(2) = 0.72) and was less correlated with age (R(2) = 0.62). Following first-order relationships of image noise and scanner technique, plots were formed to show techniques that could achieve matched noise across the pediatric population. CONCLUSION: Patient lateral distance and weight are essentially equally effective metrics to base maximum technique settings for pediatric patient-specific protocols. These metrics can also be used to help categorize appropriate reference levels for CT technique and size-specific dose estimates across the pediatric population.

Near-pair patch generative adversarial network for data augmentation of focal pathology object detection models.

Purpose: The limited volume of medical training data remains one of the leading challenges for machine learning for diagnostic applications. Object detectors that identify and localize pathologies require training with a large volume of labeled images, which are often expensive and time-consuming to curate. To reduce this challenge, we present a method to support distant supervision of object detectors through generation of synthetic pathology-present labeled images. Approach: Our method employs the previously proposed cyclic generative adversarial network (cycleGAN) with two key innovations: (1) use of "near-pair" pathology-present regions and pathology-absent regions from similar locations in the same subject for training and (2) the addition of a realism metric (Fréchet inception distance) to the generator loss term. We trained and tested this method with 2800 fracture-present and 2800 fracture-absent image patches from 704 unique pediatric chest radiographs. The trained model was then used to generate synthetic pathology-present images with exact knowledge of location (labels) of the pathology. These synthetic images provided an augmented training set for an object detector. Results: In an observer study, four pediatric radiologists used a five-point Likert scale indicating the likelihood of a real fracture (1 = definitely not a fracture and 5 = definitely a fracture) to grade a set of real fracture-absent, real fracture-present, and synthetic fracture-present images. The real fracture-absent images scored 1.7±1.0, real fracture-present images 4.1±1.2, and synthetic fracture-present images 2.5±1.2. An object detector model (YOLOv5) trained on a mix of 500 real and 500 synthetic radiographs performed with a recall of 0.57±0.05 and an F2 score of 0.59±0.05. In comparison, when trained on only 500 real radiographs, the recall and F2 score were 0.49±0.06 and 0.53±0.06, respectively. Conclusions: Our proposed method generates visually realistic pathology and that provided improved object detector performance for the task of rib fracture detection.

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.

Comparison of positron emission tomography and bremsstrahlung imaging to detect particle distribution in patients undergoing yttrium-90 radioembolization for large hepatocellular carcinomas or associated portal vein thrombosis.

PURPOSE: To compare positron emission tomography/computed tomography (PET/CT) imaging with bremsstrahlung single photon emission computed tomography (SPECT) in patients after yttrium-90 ((90)Y) microsphere radioembolization to assess particle uptake. MATERIALS AND METHODS: This prospective study comprised patients with large (> 5 cm) hepatocellular carcinoma (HCC) or tumor-associated portal vein thrombus (PVT), or both. After radioembolization for HCC, patients underwent bremsstrahlung SPECT/CT and time-of-flight PET/CT imaging of (90)Y without additional tracer administration. Follow-up imaging and toxicity was analyzed. Imaging analyses of PET/CT and bremsstrahlung SPECT/CT were independently performed. RESULTS: There were 13 patients enrolled in the study, including 7 with PVT. Median tumor diameter was 7 cm. PET/CT demonstrated precise localization of (90)Y particles in the liver, with specific patterns of uptake in large tumors. In cases of PVT, PET/CT showed activity within the PVT. When correlated to short-term follow-up imaging, areas of necrosis correlated with regions of uptake seen on PET/CT. Compared with bremsstrahlung imaging, PET/CT demonstrated at least comparable spatial resolution with less scatter. Quantitative uptake in nontreated regions of interest showed significantly reduced scatter with PET/CT versus SPECT/CT (1% vs 14%, P < .001). CONCLUSIONS: Evaluation of (90)Y particle uptake with PET/CT potentially demonstrates high spatial resolution and low scatter compared with bremsstrahlung SPECT/CT. Confirmation of particles within PVT on PET/CT correlates with response on follow-up imaging and may account for the efficacy of radioembolization in patients with PVT.

Validation of an axially distributed model for quantification of myocardial blood flow using ¹³N-ammonia PET.

BACKGROUND: Estimation of myocardial blood flow (MBF) with cardiac PET is often performed with conventional compartmental models. In this study, we developed and evaluated a physiologically and anatomically realistic axially distributed model. Unlike compartmental models, this axially distributed approach models both the temporal and the spatial gradients in uptake and retention along the capillary. METHODS: We validated PET-derived flow estimates with microsphere studies in 19 (9 rest, 10 stress) studies in five dogs. The radiotracer, (13)N-ammonia, was injected intravenously while microspheres were administered into the left atrium. A regional reduction in hyperemic flow was forced by an external occluder in five of the stress studies. The flow estimates from the axially distributed model were compared with estimates from conventional compartmental models. RESULTS: The mean difference between microspheres and the axially distributed blood flow estimates in each of the 17 segments was 0.03 mL/g/minute (95% CI [-0.05, 0.11]). The blood flow estimates were highly correlated with each regional microsphere value for the axially distributed model (y = 0.98x + 0.06 mL/g/minute; r = 0.74; P < .001), for the two-compartment (y = 0.64x + 0.34; r = 0.74; P < .001), and for three-compartment model (y = 0.69x + 0.54; r = 0.74; P < .001). The variance of the error of the estimates is higher with the axially distributed model than the compartmental models (1.7 [1.3, 2.1] times higher). CONCLUSION: The proposed axially distributed model provided accurate regional estimates of MBF. The axially distributed model estimated blood flow with more accuracy, but less precision, than the evaluated compartmental models.

Pediatric CT: strategies to lower radiation dose.

OBJECTIVE: The introduction of MDCT has increased the utilization of CT in pediatric radiology along with concerns for radiation sequelae. This article reviews general principles of lowering radiation dose, the basic physics that impact radiation dose, and specific CT integrated dose-reduction tools focused on the pediatric population. CONCLUSION: The goal of this article is to provide a comprehensive review of the recent literature regarding CT dose reduction methods, their limitations, and an outlook on future developments with a focus on the pediatric population. The discussion will initially focus on general considerations that lead to radiation dose reduction, followed by specific technical features that influence the radiation dose.

Model-based iterative reconstruction versus adaptive statistical iterative reconstruction and filtered back projection in liver 64-MDCT: focal lesion detection, lesion conspicuity, and image noise.

OBJECTIVE: The purpose of this study is to compare three CT image reconstruction algorithms for liver lesion detection and appearance, subjective lesion conspicuity, and measured noise. MATERIALS AND METHODS: Thirty-six patients with known liver lesions were scanned with a routine clinical three-phase CT protocol using a weight-based noise index of 30 or 36. Image data from each phase were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR). Randomized images were presented to two independent blinded reviewers to detect and categorize the appearance of lesions and to score lesion conspicuity. Lesion size, lesion density (in Hounsfield units), adjacent liver density (in Hounsfield units), and image noise were measured. Two different unblinded truth readers established the number, appearance, and location of lesions. RESULTS: Fifty-one focal lesions were detected by truth readers. For blinded reviewers compared with truth readers, there was no difference for lesion detection among the reconstruction algorithms. Lesion appearance was statistically the same among the three reconstructions. Although one reviewer scored lesions as being more conspicuous with MBIR, the other scored them the same. There was significantly less background noise in air with MBIR (mean [± SD], 2.1 ± 1.4 HU) than with ASIR (8.9 ± 1.9 HU; p < 0.001) or FBP (10.6 ± 2.6 HU; p < 0.001). Mean lesion contrast-to-noise ratio was statistically significantly higher for MBIR (34.4 ± 29.1) than for ASIR (6.5 ± 4.9; p < 0.001) or FBP (6.3 ± 6.0; p < 0.001). CONCLUSION: In routine-dose clinical CT of the liver, MBIR resulted in comparable lesion detection, lesion characterization, and subjective lesion conspicuity, but significantly lower background noise and higher contrast-to-noise ratio compared with ASIR or FBP. This finding suggests that further investigation of the use of MBIR to enable dose reduction in liver CT is warranted.

Performance Evaluation of Small Animal PET Scanners With Different System Designs.

This study evaluated the image quality metrics of small animal PET scanners based upon measured single detector module positioning performance. A semi-analytical approach was developed to study PET scanner performance in the scenario of multiple realizations. Positron range blurring, scanner system response function (SRF) and statistical noise were included in the modeling procedure. The scanner sensitivity map was included in the system matrix during maximum likelihood expectation maximization (MLEM) reconstruction. Several image quality metrics were evaluated for octagonal ring PET scanners consisting of continuous miniature crystal element (cMiCE) detector modules with varying designs. These designs included 8 mm and 15 mm thick crystal detectors using conventional readout with the photosensors on the exit surface of the crystal and a 15 mm thick crystal detector using our proposed sensor-on-the-entrance (SES) design. For the conventional readout design, the results showed that there was a tradeoff between bias and variance with crystal thickness. The 15 mm crystal detector had better detection task performance, while quantitation task performance was degraded. On the other hand, our SES detector had similar detection efficiency as the conventional design using a 15 mm thick crystal and had similar intrinsic spatial resolution as the conventional design using an 8 mm thick crystal. The end result was that by using the SES design, one could improve scanner quantitation task performance without sacrificing detection task performance.

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.

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.