Low-dose CT scan screening for lung cancer: comparison of images and radiation doses between low-dose CT and follow-up standard diagnostic CT

Ono, Koji; Hiraoka, Toru; Ono, Asami; Komatsu, Eiji; Shigenaga, Takehiko; Takaki, Haruyuki; Maeda, Takeyasu; Ogusu, Hiroyuki; Yoshida, Shintaro; Fukushima, Kiyoyasu; Kai, Michiaki

doi:10.1186/2193-1801-2-393

Cited by 23 publications

(15 citation statements)

References 25 publications

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“…The dose levels range between 20 and 400 mSv for CBCT and around 1000 mSv for multidetector row CT modalities, which is about 10-50 times more than a conventional panoramic radiograph (about 20 mSv). 13,14 Even though device manufacturers have been developing "ultra" low-dose CT scanning protocols, [15][16][17] any X-ray-based imaging technology inevitably results in some radiation exposure. Therefore, an alternative, radiation-free imaging modality is still being sought, especially in applications that require a large number of follow-up scans.…”

Section: Introductionmentioning

confidence: 99%

The accuracy of ultrashort echo time MRI sequences for medical additive manufacturing

Eijnatten¹,

Rijkhorst²,

Hofman³

et al. 2016

Dentomaxillofacial Radiology

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Objectives: Additively manufactured bone models, implants and drill guides are becoming increasingly popular amongst maxillofacial surgeons and dentists. To date, such constructs are commonly manufactured using CT technology that induces ionizing radiation. Recently, ultrashort echo time (UTE) MRI sequences have been developed that allow radiation-free imaging of facial bones. The aim of the present study was to assess the feasibility of UTE MRI sequences for medical additive manufacturing (AM). Methods: Three morphologically different dry human mandibles were scanned using a CT and MRI scanner. Additionally, optical scans of all three mandibles were made to acquire a "gold standard". All CT and MRI scans were converted into Standard Tessellation Language (STL) models and geometrically compared with the gold standard. To quantify the accuracy of the AM process, the CT, MRI and gold-standard STL models of one of the mandibles were additively manufactured, optically scanned and compared with the original gold-standard STL model. Results: Geometric differences between all three CT-derived STL models and the gold standard were ,1.0 mm. All three MRI-derived STL models generally presented deviations ,1.5 mm in the symphyseal and mandibular area. The AM process introduced minor deviations of ,0.5 mm. Conclusions: This study demonstrates that MRI using UTE sequences is a feasible alternative to CT in generating STL models of the mandible and would therefore be suitable for surgical planning and AM. Further in vivo studies are necessary to assess the usability of UTE MRI sequences in clinical settings.

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Section: Introductionmentioning

confidence: 99%

The accuracy of ultrashort echo time MRI sequences for medical additive manufacturing

Eijnatten¹,

Rijkhorst²,

Hofman³

et al. 2016

Dentomaxillofacial Radiology

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“…In order to reduce concerns regarding the ionizing radiation induced by PET/CT, one may consider reducing the radiation exposure of PET/CT scans. There are studies that show that low-dose CT scans have similar tissue HU levels as those in routine CT scans with no diagnostic differences noted, suggesting the use of low(er) dose CT scans in routine examinations [45]. On the other hand, lowering radiation dose in PET equipment is more difficult and expensive than its CT counterpart [46], [47].…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans

Hussein

Green

Watane

et al. 2017

IEEE Trans. Med. Imaging

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In this paper, we investigate the automatic detection of white and brown adipose tissues using Positron Emission Tomography/Computed Tomography (PET/CT) scans, and develop methods for the quantification of these tissues at the whole-body and body-region levels. We propose a patient-specific automatic adiposity analysis system with two modules. In the first module, we detect white adipose tissue (WAT) and its two sub-types from CT scans: Visceral Adipose Tissue (VAT) and Subcutaneous Adipose Tissue (SAT). This process relies conventionally on manual or semi-automated segmentation, leading to inefficient solutions. Our novel framework addresses this challenge by proposing an unsupervised learning method to separate VAT from SAT in the abdominal region for the clinical quantification of central obesity. This step is followed by a context driven label fusion algorithm through sparse 3D Conditional Random Fields (CRF) for volumetric adiposity analysis. In the second module, we automatically detect, segment, and quantify brown adipose tissue (BAT) using PET scans because unlike WAT, BAT is metabolically active. After identifying BAT regions using PET, we perform a co-segmentation procedure utilizing asymmetric complementary information from PET and CT. Finally, we present a new probabilistic distance metric for differentiating BAT from non-BAT regions. Both modules are integrated via an automatic body-region detection unit based on one-shot learning. Experimental evaluations conducted on 151 PET/CT scans achieve state-of-the-art performances in both central obesity as well as brown adiposity quantification.

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“…A 10 point likert scale was used for all scoring to characterize the 133 target nodule images according to the criteria of malignancy described in [39, 40]. Each of the 133 nodule images has 7 scores:…”

Section: Tissue-specific Mrf Clinical Performance In Nodule Diagnosismentioning

confidence: 99%

A Feasibility Study of Extracting Tissue Textures From a Previous Full-Dose CT Database as Prior Knowledge for Bayesian Reconstruction of Current Low-Dose CT Images

Gao

Zhang

Moore

et al. 2019

IEEE Trans. Med. Imaging

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Markov random field (MRF) has been widely used to incorporate a priori knowledge as penalty or regularizer to preserve edge sharpness while smoothing the region enclosed by the edge for pieces-wise smooth image reconstruction. In our earlier study, we proposed a type of MRF reconstruction method for low-dose CT (LdCT) scans using tissue-specific textures extracted from the same patient’s previous full-dose CT (FdCT) scans as prior knowledge. It showed advantages in clinical applications. This study aims to remove the constraint of using previous data of the same patient. We investigated the feasibility of extracting the tissue-specific MRF textures from an FdCT database to reconstruct a LdCT image of another patient. This feasibility study was carried out by experiments designed as follows. We constructed a tissue-specific MRF-texture database from 3990 FdCT scan slices of 133 patients who were scheduled for lung nodule biopsy. Each patient had one FdCT scan (120kVp/100mAs) and one LdCT scan (120kVp/20mAs) prior to biopsy procedure. When reconstructing the LdCT image of one patient among the 133 patients, we ranked the closeness of the MRF-textures from the other 132 patients saved in the database and used them as the a prior knowledge. Then, we evaluated the reconstructed image quality using Haralick texture measures. For any patient within our database, we found more than eighteen patients’ FdCT MRF texures can be used without noticeably changing the Haralick texture measures on the lung nodules (to be biopsied). These experimental outcomes indicate it is promising that a sizable FdCT texture database could be used to enhance Bayesian reconstructions of any incoming LdCT scans.

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Low-dose CT scan screening for lung cancer: comparison of images and radiation doses between low-dose CT and follow-up standard diagnostic CT

Cited by 23 publications

References 25 publications

The accuracy of ultrashort echo time MRI sequences for medical additive manufacturing

The accuracy of ultrashort echo time MRI sequences for medical additive manufacturing

Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans

A Feasibility Study of Extracting Tissue Textures From a Previous Full-Dose CT Database as Prior Knowledge for Bayesian Reconstruction of Current Low-Dose CT Images

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