Impact of CT reconstruction algorithm on auto-segmentation performance.

Model-based iterative reconstruction (MBIR) reduces CT imaging dose while maintaining image quality. However, MBIR reduces noise while preserving edges which may impact intensity-based tasks such as auto-segmentation. This work evaluates the sensitivity of an auto-contouring prostate atlas across multiple MBIR reconstruction protocols and benchmarks the results against filtered back projection (FBP). Images were created from raw projection data for 11 prostate cancer cases using FBP and nine different MBIR reconstructions (3 protocols/3 noise reduction levels) yielding 10 reconstructions/patient. Five bony structures, bladder, rectum, prostate, and seminal vesicles (SVs) were segmented using an auto-segmentation pipeline that renders 3D binary masks for analysis. Performance was evaluated for volume percent difference (VPD) and Dice similarity coefficient (DSC), using FBP as the gold standard. Nonparametric Friedman tests plus post hoc all pairwise comparisons were employed to test for significant differences (P < 0.05) for soft tissue organs and protocol/level combinations. A physician performed qualitative grading of 396 MBIR contours across the prostate, bladder, SVs, and rectum in comparison to FBP using a six-point scale. MBIR contours agreed with FBP for bony anatomy (DSC ≥ 0.98), bladder (DSC ≥ 0.94, VPD < 8.5%), and prostate (DSC = 0.94 ± 0.03, VPD = 4.50 ± 4.77% (range: 0.07-26.39%). Increased variability was observed for rectum (VPD = 7.50 ± 7.56% and DSC = 0.90 ± 0.08) and SVs (VPD and DSC of 8.23 ± 9.86% range (0.00-35.80%) and 0.87 ± 0.11, respectively). Over the all protocol/level comparisons, a significant difference was observed for the prostate VPD between BSPL1 and BSTL2 (adjusted P-value = 0.039). Nevertheless, 300 of 396 (75.8%) of the four soft tissue structures using MBIR were graded as equivalent or better than FBP, suggesting that MBIR offered potential improvements in auto-segmentation performance when compared to FBP. Future work may involve tuning organ-specific MBIR parameters to further improve auto-segmentation performance. Running title: Impact of CT Reconstruction Algorithm on Auto-segmentation Performance.

A patient-centred model to quality assure outputs from an echocardiography department: consensus guidance from the British Society of Echocardiography.

Background Quality assurance (QA) of echocardiographic studies is vital to ensure that clinicians can act on findings of high quality to deliver excellent patient care. To date, there is a paucity of published guidance on how to perform this QA. The British Society of Echocardiography (BSE) has previously produced an Echocardiography Quality Framework (EQF) to assist departments with their QA processes. This article expands on the EQF with a structured yet versatile approach on how to analyse echocardiographic departments to ensure high-quality standards are met. In addition, a process is detailed for departments that are seeking to demonstrate to external bodies adherence to a robust QA process. Methods The EQF consists of four domains. These include assessment of Echo Quality (including study acquisition and report generation); Reproducibility & Consistency (including analysis of individual variability when compared to the group and focused clinical audit), Education & Training (for all providers and service users) and Customer & Staff Satisfaction (of both service users and patients/their carers). Examples of what could be done in each of these areas are presented. Furthermore, evidence of participation in each domain is categorised against a red, amber or green rating: with an amber or green rating signifying that a quantifiable level of engagement in that aspect of QA has been achieved. Conclusion The proposed EQF is a powerful tool that focuses the limited time available for departmental QA on areas of practice where a change in patient experience or outcome is most likely to occur.

Experimental comparison of lesion detectability for four fully-3D PET reconstruction schemes.

The objective of this work was to evaluate the lesion detection performance of four fully-3D positron emission tomography (PET) reconstruction schemes using experimentally acquired data. A multi-compartment anthropomorphic phantom was set up to mimic whole-body (18)F-fluorodeoxyglucose (FDG) cancer imaging and scanned 12 times in 3D mode, obtaining count levels typical of noisy clinical scans. Eight of the scans had 26 (68)Ge "shell-less" lesions (6, 8-, 10-, 12-, 16-mm diameter) placed throughout the phantom with various target:background ratios. This provided lesion-present and lesion-absent datasets with known truth appropriate for evaluating lesion detectability by localization receiver operating characteristic (LROC) methods. Four reconstruction schemes were studied: 1) Fourier rebinning (FORE) followed by 2D attenuation-weighted ordered-subsets expectation-maximization, 2) fully-3D AW-OSEM, 3) fully-3D ordinary-Poisson line-of-response (LOR-)OSEM; and 4) fully-3D LOR-OSEM with an accurate point-spread function (PSF) model. Two forms of LROC analysis were performed. First, a channelized nonprewhitened (CNPW) observer was used to optimize processing parameters (number of iterations, post-reconstruction filter) for the human observer study. Human observers then rated each image and selected the most-likely lesion location. The area under the LROC curve ( A(LROC)) and the probability of correct localization were used as figures-of-merit. The results of the human observer study found no statistically significant difference between FORE and AW-OSEM3D ( A(LROC)=0.41 and 0.36, respectively), an increase in lesion detection performance for LOR-OSEM3D ( A(LROC)=0.45, p=0.076), and additional improvement with the use of the PSF model ( A(LROC)=0.55, p=0.024). The numerical CNPW observer provided the same rankings among algorithms, but obtained different values of A(LROC). These results show improved lesion detection performance for the reconstruction algorithms with more sophisticated statistical and imaging models as compared to the previous-generation algorithms.

Rapid Multi-Tracer PET Tumor Imaging With F-FDG and Secondary Shorter-Lived Tracers.

Rapid multi-tracer PET, where two to three PET tracers are rapidly scanned with staggered injections, can recover certain imaging measures for each tracer based on differences in tracer kinetics and decay. We previously showed that single-tracer imaging measures can be recovered to a certain extent from rapid dual-tracer (62)Cu - PTSM (blood flow) + (62)Cu - ATSM (hypoxia) tumor imaging. In this work, the feasibility of rapidly imaging (18)F-FDG plus one or two of these shorter-lived secondary tracers was evaluated in the same tumor model. Dynamic PET imaging was performed in four dogs with pre-existing tumors, and the raw scan data was combined to emulate 60 minute long dual- and triple-tracer scans, using the single-tracer scans as gold standards. The multi-tracer data were processed for static (SUV) and kinetic (K(1), K(net)) endpoints for each tracer, followed by linear regression analysis of multi-tracer versus single-tracer results. Static and quantitative dynamic imaging measures of FDG were both accurately recovered from the multi-tracer scans, closely matching the single-tracer FDG standards (R > 0.99). Quantitative blood flow information, as measured by PTSM K(1) and SUV, was also accurately recovered from the multi-tracer scans (R = 0.97). Recovery of ATSM kinetic parameters proved more difficult, though the ATSM SUV was reasonably well recovered (R = 0.92). We conclude that certain additional information from one to two shorter-lived PET tracers may be measured in a rapid multi-tracer scan alongside FDG without compromising the assessment of glucose metabolism. Such additional and complementary information has the potential to improve tumor characterization in vivo, warranting further investigation of rapid multi-tracer techniques.

Evaluation of rapid dual-tracer (62)Cu-PTSM + (62)Cu-ATSM PET in dogs with spontaneously occurring tumors.

We are developing methods for imaging multiple PET tracers in a single scan with staggered injections, where imaging measures for each tracer are separated and recovered using differences in tracer kinetics and radioactive decay. In this work, signal separation performance for rapid dual-tracer (62)Cu-PTSM (blood flow) + (62)Cu-ATSM (hypoxia) tumor imaging was evaluated in a large animal model. Four dogs with pre-existing tumors received a series of dynamic PET scans with (62)Cu-PTSM and (62)Cu-ATSM, permitting evaluation of a rapid dual-tracer protocol designed by previous simulation work. Several imaging measures were computed from the dual-tracer data and compared with those from separate, single-tracer imaging. Static imaging measures (e.g. SUV) for each tracer were accurately recovered from dual-tracer data. The wash-in (k(1)) and wash-out (k(2)) rate parameters for both tracers were likewise well recovered (r = 0.87-0.99), but k(3) was not accurately recovered for PTSM (r = 0.19) and moderately well recovered for ATSM (r = 0.70). Some degree of bias was noted, however, which may potentially be overcome through further refinement of the signal separation algorithms. This work demonstrates that complementary information regarding tumor blood flow and hypoxia can be acquired by a single dual-tracer PET scan, and also that the signal separation procedure works effectively for real physiologic data with realistic levels of kinetic model mismatch. Rapid multi-tracer PET has the potential to improve tumor assessment for image-guide therapy and monitoring, and further investigation with these and other tracers is warranted.