2016-12-26T14:56:40

Purpose: To present a methodology for reducing cone beam CT (CBCT) dose using low dose imaging protocols and sparse representation techniques to achieve similar image quality as regular or high dose imaging protocols. Method and Materials: The frequent usage of CBCT raises the concern of excessive non‐therapeutic dose delivered to patients, especially to pediatric patients. Sparse representation (SR) was applied to low dose CBCT imaging, achieved by using low mAs, to suppress image noise and streak artifacts. The denoising algorithm was applied to low dose clinical images based on the concept of sparse representation using the training dictionary, which was constructed from the regular dose clinical images to approximate the anatomical site of interest. Validation was carried out using two phantoms (a CATPHAN phantom and an anthropomorphic head & neck phantom) and clinical prostate images. Comparisons with the modified edge preserving curvature anisotropic diffusion filter (MCDF) were carried out. Results: For CATPHAN phantom images, the SR algorithm outperformed the MCDF algorithm in terms of contrast to noise ratio (CNR). CNR was improved by an average of 32% for the SR algorithm. In contrast, MCDF only improved CNR by an average of 2.4%. Visual assessments of the anthropomorphic head & neck phantom and patient images indicated the SR algorithm not only suppressed the image noise but also preserved fine details of anatomical structures and was significantly better than MCDF. Conclusion: SR can be used to reduce image noise and CBCT imaging artifacts present in low dose CBCT images. Dose reductions up to 88% were observed without loss of image quality based on visual assessment.

Purpose: To present a deformable image registration (DIR) model based on maximum likelihood estimation (MLE) with improved robustness to image noise and selection of registration parameters. Method and Materials: Optical flow based DIR, such as free‐form deformable registration (FDR), usually assumes invariance of pixel intensities, which may not be true for noisy images. Moreover, the smoothing parameter must be judiciously chosen and typically remains fixed over all iterations of the registration. A robust registration model (FDR‐MLE) based on minimization of smoothing term, standard for FDR, and MLE of the residual image is proposed. The advantages of the proposed model are 1) matching image pixels between the source image and the target image within an optimized variation to account for image noise; and 2) adaptive adjustment of the smoothing parameter during the registration process. Validations of FDR‐MLE were carried out using a simulated digital phantom and clinical lung images. Validation criteria included correlation coefficient (CC) and average phantom error (PE). Results: FDR‐MLE outperformed FDR and was more robust to selection of registration parameters. For the same parameters (step size = 0.3, smoothing weighting factor = 2.0), in the simulated phantom, FDR‐MLE increased the CC by 11% compared to FDR. FDR‐MLE had PE=0.49 pixels, compared to 1.08 pixels for FDR. Robustness was measured by computing standard deviations (STD) of CC and PE for various step sizes. Using CC, STD of FDR‐MLE was 0.0066, which was only 12% of STD of CC for FDR (0.053). PE for FDR‐MLE was 0.044 pixels, compared to 0.21 pixels for FDR. Conclusion: The proposed FDR‐MLE model can significantly improve the registration accuracy, convergence, and robustness. It is well suited for DIR in a clinical environment where time constraints and limited expertise of the user limit optimization of the registration parameters.

Background Hypertension (HTN) and coronary artery disease (CAD) are a prevalent combination in women, however limited data are available to guide blood pressure (BP) management. We hypothesize older women with HTN and CAD may not derive the same prognostic benefit from systolic BP (SBP) lowering <130 mmHg. Purpose To investigate the long-term mortality implications of different achieved SBP levels in hypertensive women with CAD. Methods Long-term, all-cause mortality data were analyzed for 9216 women, stratified by risk attributable to clinical severity of CAD (women with prior myocardial infarction or revascularization considered at high, all others at low risk) and by age (50 - <65 or ≥65 yo). The prognostic impact of achieving mean in-trial SBP <130 (referent group) was compared with 130 to <140 and ≥140 mmHg using Cox proportional hazards, adjusting for demographic and clinical characteristics. Results During 108,838 person-years of follow-up, 2945 deaths occurred. High risk women (n=3011) had increased long-term mortality in comparison to low risk women (n=6205) (adjusted HR 1.38, CI 1.28–1.5, p<0.001). Within risk groups, crude mortality percentages decreased according to BP values (table). As expected, high risk women were more likely to be ≥65 yo (68.68% vs. 50.51%, p<0.0001) or have SBP ≥140 mmHg (43.08% vs. 31.18%, p<0.0001). In adjusted analyses, an SBP ≥140 mmHg was associated with worse outcomes than SBP <130 mmHg in the entire cohort (HR 1.3, CI 1.2–1.5, p<0.0001) and when stratifying by risk (low risk group, HR = 1.47, CI 1.28–1.7, p<0.0001; high risk group, HR = 1.71, CI 1.01–1.35, p=0.03). In analyses stratified by age and risk, women ≥65 years and at high risk had decreased mortality in the 130 - <140 SBP category vs. <130 mmHg (HR 0.812, 95% CI 0.689–0.957, p=0.0133; figure). Women and deaths by risk and SBP group Group SBP category Women (n) Mortality (n) Mortality (%) High risk <130 773 338 44 130–<140 941 414 44 ≥140 1297 694 54 Low risk <130 2187 390 18 130–<140 2083 451 22 ≥140 1935 658 34 SBP = systolic blood pressure; n = number; % = percent per each group. Mortality adjusted HRs Conclusion In women ≥65 yo with hypertension and prior myocardial infarction and/or coronary revascularization enrolled in INVEST, a SBP between 130 to <140 mmHg was associated with lower all-cause, long-term mortality versus SBP <130 mmHg. Acknowledgement/Funding The main INVEST (International Verapamil [SR]/Trandolapril Study) was funded by grants from BASF Pharma, Ludwigshafen, Germany; Abbott Laboratories, A

Purpose:Recently, compressed sensing (CS) based iterative reconstruction (IR) method is receiving attentions to reconstruct high quality cone beam computed tomography (CBCT) images using sparsely sampled or noisy projections. The aim of this study is to develop a novel baseline algorithm called Mask Guided Image Reconstruction (MGIR), which can provide superior image quality for both low‐dose 3DCBCT and 4DCBCT under single mathematical framework.Methods:In MGIR, the unknown CBCT volume was mathematically modeled as a combination of two regions where anatomical structures are 1) within the priori‐defined mask and 2) outside the mask. Then we update each part of images alternatively thorough solving minimization problems based on CS type IR. For low‐dose 3DCBCT, the former region is defined as the anatomically complex region where it is focused to preserve edge information while latter region is defined as contrast uniform, and hence aggressively updated to remove noise/artifact. In 4DCBCT, the regions are separated as the common static part and moving part. Then, static volume and moving volumes were updated with global and phase sorted projection respectively, to optimize the image quality of both moving and static part simultaneously.Results:Examination of MGIR algorithm showed that high quality of both low‐dose 3DCBCT and 4DCBCT images can be reconstructed without compromising the image resolution and imaging dose or scanning time respectively. For low‐dose 3DCBCT, a clinical viable and high resolution head‐and‐neck image can be obtained while cutting the dose by 83%. In 4DCBCT, excellent quality 4DCBCT images could be reconstructed while requiring no more projection data and imaging dose than a typical clinical 3DCBCT scan.Conclusion:The results shown that the image quality of MGIR was superior compared to other published CS based IR algorithms for both 4DCBCT and low‐dose 3DCBCT. This makes our MGIR algorithm potentially useful in various on‐line clinical applications.Provisional Patent: UF#15476; WGS Ref. No. U1198.70067US00