Texture analysis for automated evaluation of Jaszczak phantom SPECT system tests

Abstract

Purpose: Routine quarterly quality assurance (QA) assessment of single photon emission computed tomography (SPECT) systems includes analysis of multipurpose phantoms containing spheres and rods of various sizes. When evaluated by accreditation agencies, criteria applied to assess image quality are largely subjective. Determining a quantified image characteristic metric that emulates human reader impressions of image quality could be quite useful. Our investigation was conducted to ascertain whether image texture analysis metrics, such as those applied to PET scans to detect neoplasms, could prove helpful in linking qualitative statements of phantom sphere and rod visibility to quantified parameters. Because it is not obvious whether it is preferable to submit reconstructions to accrediting agencies performed using typical clinical (CLIN) protocol processing parameters or to follow agencies' filtered backprojection (FBP) suggestions, we applied texture analysis metrics to determine the degree to which these choices affect equipment capability assessment. Methods and materials: Data were processed retrospectively for 125 different Tc-99 m SPECT scans of standardized phantoms for 14 rotating Anger detector systems as part of routine quarterly QA. Algorithms were written to compute several classes of image metrics: quantile curve metrics, image texture analysis gray-level co-occurrence matrix (GLCM) metrics, contrast metrics, and count histogram metrics. For qualitative image scores, two experienced physicists independently graded sphere and rod visibility on a 5-level scale and assigned dichotomous visibility scores, without knowledge of quantified texture analysis metrics or each other's readings. The same phantom was used to collect 15 additional data sets with two dual-detector SPECT/CT systems, reconstructed both by FBP parameters that have been suggested by accrediting agencies and by manufacturers' default settings for CLIN SPECT/CT bone imaging protocols by ordered subsets expectation maximization (OSEM), incorporating attenuation correction using the CT scan. Image characteristics metrics were compared for FBP and CLIN reconstructions. Results: For spheres, the metric with the strongest rank correlation with 5-level scale readings was the quantile curve slope (q = 0.83, P < 0.0001), while for rods it was GLCM Energy normalized to the maximum GLCM Energy value (EnergyNorm) (q = À0.88, P < 0.0001). Compared to dichotomous readings, the metric with the highest ROC area under curve (AUC) for spheres was the quantile curve slopes (AUC = 96 AE 1%, sensitivity = 91%, specificity = 90%), and for rods was Energy-Norm (AUC = 98 AE 1%, sensitivity = 92%, specificity = 95%). Image contrast was higher for all sphere sizes and rod EnergyNorm was lower for sectors of intermediate-sized rods for FBP compared to CLIN reconstructions, in agreement with more rods judged to be visible from FBP than CLIN reconstructions (47% vs 33%, P = 0.002). Conclusions: When preparing to submit quality assurance images of standardized phanto...

Quantification of SPECT Concentric Ring Artifacts by Radiomics and Radial Features

“…While the results of quality assurance phantom scans usually are assessed visually, recent progress has been reported in quantifying scanner performance automatically [7,8,9,11]. Among the parameters that are evaluated in SPECT phantom scans, non-uniformity is perhaps the most challenging.…”

“…Automated algorithms written in IDL 8.4 were run on all transaxial phantom data that generated a series of output jpg image files [7]. Because some accrediting agencies request displays of all transaxial slices [14], multiple jpg files were generated per phantom for all transaxial slices spanning the entire height of the phantom, and a separate composite jpg file was generated showing a composite of 3 images (Fig.…”

“…To address these issues, algorithms have been devised to analyze standardized SPECT QA phantoms, both to report the results of spatial resolution and contrast results [7,8], and to report the possibility of artifacts [9]. Some of these algorithms are based on texture analysis [7,9].…”

Assessment of PET and SPECT phantom image quality through automated binary classification of cold rod arrays

“…Although contrast measurements reflect imaging performance to some degree, they do not account for the effect of noise on image quality and do not consider the task of visualizing cold rods in a warm background. Another approach is to investigate the correlation of various automated texture analysis metrics to specific performance evaluation tasks scored by experienced human observers and to establish minimum quantitative scores for the most correlated metrics . One can envision similar investigations in the future using machine learning algorithms trained on phantom images scored by human observers.…”

“…Another approach is to investigate the correlation of various automated texture analysis metrics to specific performance evaluation tasks scored by experienced human observers and to establish minimum quantitative scores for the most correlated metrics. 7 One can envision similar investigations in the future using machine learning algorithms trained on phantom images scored by human observers.…”