Model-based<i>vs</i>hybrid iterative reconstruction technique in ultralow-dose submillisievert CT colonography

Lambert, Lukáš; Ouředníček, Petr; Jahoda, Jiri; Lambertová, Alena; Daneš, Jan

doi:10.1259/bjr.20140667

Cited by 16 publications

(20 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While limited segmentation data are available for direct comparison, several groups have evaluated the impact of model‐based iterative reconstruction on image quality tasks. For example, FBP has been found to have inferior image quality as compared to both MBIR and hybrid iterative reconstruction (HIR) and that MBIR yielded superior image quality to HIR for the cross‐sectional view . A study conducted by Hèrin et al concluded that MBIR reconstructions of reduced‐dose CT and FBP reconstruction of standard‐dose CT both obtained the same low contrast detectability on a phantom .…”

Section: Discussionmentioning

confidence: 99%

“…One potential way to overcome image noise is to employ advanced reconstruction algorithms to maintain the same image quality while increasing the contrast to noise ratio . Advanced CT reconstruction methods such as hybrid iterative reconstructions (HIR), model‐based iterative reconstruction (MBIR), and adaptive statistical iterative reconstruction (ASIR) have been integrated into clinical diagnostic CT scanners, whereas their application in radiation oncology has been limited to date. In a study conducted by Price et al ., HIR was found to maintain image quality with dose reduction protocols of up to ~ 70% when compared to FBP for CT‐SIM datasets in the female pelvis .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impact of CT reconstruction algorithm on auto‐segmentation performance

Miller

Mittelstaedt²,

Black

et al. 2019

J Applied Clin Med Phys

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of CT reconstruction algorithm on auto‐segmentation performance

Miller

Mittelstaedt²,

Black

et al. 2019

J Applied Clin Med Phys

View full text Add to dashboard Cite

show abstract

“…Polyps with lower density are more prone to disappear in the electronic noise in low-dose studies, especially if the reader prefers increasing the endoluminal rendering threshold (attenuation value above which voxels appear as colonic wall instead of intraluminal air), whereas polyps with too high density may escape CAD detection (25, 28). …”

Section: Discussionmentioning

confidence: 99%

“…Although a slice thickness of 2.5 mm is the recommended maximum, thinner slices are commonly used for a more accurate depiction of internal structure of a polyp candidate (28, 33). …”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Computed Tomography Colonography Phantom: Construction, Validation and Literature Review

et al. 2016

View full text Add to dashboard Cite

BackgroundSeveral dedicated computed tomography (CT) colonography phantoms have been described previously.ObjectivesTo compare their pros and cons and describe the construction of a dedicated phantom that can be easily manufactured.Materials and MethodsWe found 15 different phantom designs by literature search and compared their advantages and disadvantages based on their description and images. We tested various materials for density and mechanical properties and constructed a phantom from polypropylene pipes (30 mm and 50 mm in diameter, 52 cm in length). Haustral folds were created by heat shaping and 39 intermediate sessile polyps with a target size of 6-10 mm and two flat lesions were created from silicone. Nine polyps were attached to a fold. The model was placed in a 30-cm barrel filled with water to simulate attenuation of human body. Attenuation of polyps was compared to intermediate polyps found in patients.ResultsNone of the earlier colonic phantoms found in the literature incorporated all the properties that would ensure both reproducibility and validity of the model (including a rigid wall, density of the wall and polyps similar to human colon, at least two levels of distension and durability). In the present phantom, the average size of sessile polyps was 8.6 ± 0.9 mm and their density was 53 ± 24 HU. We found no significant difference in polyp density between simulated polyps in the phantom and polyps in human subjects (P = 0.70). All polyps, with the exception of one flat lesion, were detected by computer aided detection.ConclusionWe constructed and validated a CT colonography phantom with correct density allowing performance of reproducible experiments.

show abstract

Applying a CT texture analysis model trained with deep‐learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis

Wang

Zhang

et al. 2022

J Applied Clin Med Phys

View full text Add to dashboard Cite

Objective To investigate the feasibility and accuracy of applying a computed tomography (CT) texture analysis model trained with deep‐learning reconstruction images to iterative reconstruction images for classifying pulmonary nodules. Materials and methods CT images of 102 patients, with a total of 118 pulmonary nodules (52 benign, 66 malignant) were retrospectively reconstructed with a deep‐learning reconstruction (artificial intelligence iterative reconstruction [AIIR]) and a hybrid iterative reconstruction (HIR) technique. The AIIR data were divided into a training (n = 96) and a validation set (n = 22), and the HIR data were set as the test set (n = 118). Extracted texture features were compared using the Mann‐Whitney U test and t‐test. The diagnostic performance of the classification model was analyzed with the receiver operating characteristic curve (ROC), the area under ROC (AUC), sensitivity, specificity, and accuracy. Results Among the obtained 68 texture features, 51 (75.0%) were not influenced by the change of reconstruction algorithm (p > 0.05). Forty‐four features were significantly different between benign and malignant nodules (p < 0.05) for the AIIR dataset, which were selected to build the classification model. The accuracy and AUC of the classification model were 92.3% and 0.91 (95% confidence interval [CI], 0.74−0.90) with the validation set, which were 80.0% and 0.80 (95% CI, 0.68−0.86) with the test set. Conclusion With the CT texture analysis model trained with deep‐learning reconstruction (AIIR) images showing favorable diagnostic accuracy in discriminating benign and malignant pulmonary nodules, it also has certain applicability to the iterative reconstruction (HIR) images.

show abstract

Model-basedvshybrid iterative reconstruction technique in ultralow-dose submillisievert CT colonography

Cited by 16 publications

References 27 publications

Impact of CT reconstruction algorithm on auto‐segmentation performance

Impact of CT reconstruction algorithm on auto‐segmentation performance

Computed Tomography Colonography Phantom: Construction, Validation and Literature Review

Applying a CT texture analysis model trained with deep‐learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis

Contact Info

Product

Resources

About