A Task-dependent Investigation on Dose and Texture in CT Image Reconstruction.

Lung cancer low-dose computed tomography nodule characterization nodule localization texture-enhanced image reconstruction tissue texture

Journal

IEEE transactions on radiation and plasma medical sciences
ISSN: 2469-7311
Titre abrégé: IEEE Trans Radiat Plasma Med Sci
Pays: United States
ID NLM: 101705223

Informations de publication

Date de publication:
Jul 2020
Historique:
entrez: 28 4 2021
pubmed: 29 4 2021
medline: 29 4 2021
Statut: ppublish

Résumé

Localizing and characterizing clinically-significant lung nodules, a potential precursor to lung cancer, at the lowest achievable radiation dose is demanded to minimize the stochastic radiation effects from x-ray computed tomography (CT). A minimal dose level is heavily dependent on the image reconstruction algorithms and clinical task, in which the tissue texture always plays an important role. This study aims to investigate the dependence through a task-based evaluation at multiple dose levels and variable textures in reconstructions with prospective patient studies. 133 patients with a suspicious pulmonary nodule scheduled for biopsy were recruited and the data was acquired at120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images at random, blind to the CT dose and reconstruction algorithms. The radiologists identified the nodules in each image including the 133 biopsy target nodules and 66 other non-target nodules. For target nodule characterization, only MRF-T at 40mAs showed no statistically significant difference from FBP at 100mAs. For localizing both the target nodules and the non-target nodules, some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively. MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs. This investigation concluded that (1) the textures in the MRF-T reconstructions improves both the tasks of localizing and characterizing nodules at low dose CT and (2) the task of characterizing nodules is more challenging than the task of localizing nodules and needs more dose or enhanced textures from reconstruction.

Identifiants

pubmed: 33907724
doi: 10.1109/trpms.2019.2957459
pmc: PMC8075295
mid: NIHMS1065744
doi:

Types de publication

Journal Article

Langues

eng

Pagination

441-449

Subventions

Organisme : NCI NIH HHS
ID : R01 CA143111
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA206171
Pays : United States

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Auteurs

Yongfeng Gao (Y)

Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA.

Zhengrong Liang (Z)

Departments of Radiology, Biomedical Engineering, Computer Science, and Electrical Engineering, Stony Brook University, Stony Brook, NY 11794, USA.

Hao Zhang (H)

Departments of Radiology and Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA and now with the Department of Radiation Oncology, Stanford University, Stanford, CA 94035, USA.

Jie Yang (J)

Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA.

John Ferretti (J)

Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA.

Thomas Bilfinger (T)

Department of Surgery, Stony Brook University, Stony Brook, NY 11794, USA).

Kavitha Yaddanapudi (K)

Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA.

Mark Schweitzer (M)

Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA.

Priya Bhattacharji (P)

Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA, and now with the Department of Radiology, New York University, New York, NY 10016, USA.

William Moore (W)

Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA, and now with the Department of Radiology, New York University, New York, NY 10016, USA.

Classifications MeSH