Impact of CT reconstruction algorithm on auto-segmentation performance.

Claudia Miller,Liran Goshen,Ahmed I Ghanem,Carri Glide‐Hurst,Eric D Morris,Noel Black,Daniel Mittelstaedt,Paul Klahr,Xiaoxia Han,Siamak Nejad‐Davarani,Heinrich Schulz

doi:10.1002/acm2.12710

Abstract

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.

Highlights

One of the largest sources of uncertainty in radiation therapy planning (RTP) is the delineation of the target and organs at risk (OARs) using computed tomography (CT) datasets.[1]
This study aims to evaluate the sensitivity of an auto‐segmentation algorithm to model‐based iterative reconstruction (MBIR) CT reconstructions for prostate cancer treatment planning and compare the results to the standard of care in CT simulation (CT‐SIM) (FBP)
As the underlying raw data acquisition parameters were fixed in this study, higher level reconstructions (i.e., L3) were expected to have the largest reduction in noise, for low dose protocols (e.g., ~60% noise reduction between L1 and L3 for an abdomen CT acquired at 300 mAs, 1 mm, Body Routine filter acquired with an Ingenuity CT scanner22)

Summary

Introduction

One of the largest sources of uncertainty in radiation therapy planning (RTP) is the delineation of the target and organs at risk (OARs) using computed tomography (CT) datasets.[1] Aside from the uncertainty introduced in the delineation process, manual delineation of OARs is time‐consuming.[2] efforts to implement auto‐segmentation routines are advantageous and have shown promise for several disease sites, most commonly for head and neck, and prostate cancers.[3,4,5] Current approaches to automated segmentation are commonly atlas‐based or a combination of atlas‐ and model‐ based techniques.[3] In the pelvis, auto‐segmentation has yielded good overall performance (~3 mm distance to mean surface in prostate segmentation from mean expert delineations).[6,7] Atlas‐ based segmentation algorithms have appeared promising for the segmentation of bladder, rectum, and prostate (Dice similarity coefficient (DSC) > 0.70 with respect to radiation oncologist ground truth delineations) for prostate cancer treatment planning.[8] Another auto‐segmentation toolkit, Smart Probabilistic Image Contouring Engine (SPICE), has been applied to CT scans in various disease sites and has demonstrated promise for clinical utility.[9,10]

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