Kohei Murao

Computer-Aided Volumetry of Pulmonary Nodules Exhibiting Ground-Glass Opacity at MDCT

OBJECTIVE The purpose of this study was to investigate the accuracy and reproducibility of results acquired with computer-aided volumetry software during MDCT of pulmonary nodules exhibiting ground-glass opacity. MATERIALS AND METHODS To evaluate the accuracy of computer-aided volumetry software, we performed thin-section helical CT of a chest phantom that included simulated 3-, 5-, 8-, 10-, and 12-mm-diameter ground-glass opacity nodules with attenuation of -800, -630, and -450 HU. Three radiologists measured the volume of the nodules and calculated the relative volume measurement error, which was defined as follows: (measured nodule volume minus assumed nodule volume / assumed nodule volume) x 100. Two radiologists performed two independent measurements of 59 nodules in humans. Intraobserver and interobserver agreement was evaluated with Bland-Altman methods. RESULTS The relative volume measurement error for simulated ground-glass opacity nodules measuring 3 mm ranged from 51.1% to 85.2% and for nodules measuring 5 mm or more in diameter ranged from -4.1% to 7.1%. In the clinical study, for intraobserver agreement, the 95% limits of agreement were -14.9% and -13.7% and -16.6% to 15.7% for observers A and B. For interobserver agreement, these values were -16.3% to 23.7% for nodules 8 mm in diameter or larger. CONCLUSION With computer-aided volumetry of ground-glass opacity nodules, the relative volume measurement error was small for nodules 5 mm in diameter or larger. Intraobserver and interobserver agreement was relatively high for nodules 8 mm in diameter or larger.

Volume-Doubling Time of Pulmonary Nodules with Ground Glass Opacity at Multidetector CT: Assessment with Computer-Aided Three-Dimensional Volumetry

RATIONALE AND OBJECTIVES To investigate the volume-doubling time (VDT) of histologically proved pulmonary nodules showing ground glass opacity (GGO) at multidetector CT (MDCT) using computer-aided three-dimensional volumetry. MATERIALS AND METHODS We retrospectively evaluated 47 GGO nodules (mixed n = 28, pure n = 19) that had been examined by thin-section helical CT more than once. They were histologically confirmed as atypical adenomatous hyperplasia (AAH, n = 13), bronchioloalveolar carcinoma (BAC, n = 22), and adenocarcinoma (AC, n = 12). Using computer-aided three-dimensional volumetry software, two radiologists independently performed volumetry of GGO nodules and calculated the VDT using data acquired from the initial and final CT study. We compared VDT among the three pathologies and also compared the VDT of mixed and pure GGO nodules. RESULTS The mean VDT of all GGO nodules was 486.4 ± 368.6 days (range 89.0-1583.0 days). The mean VDT for AAH, BAC, and AC was 859.2 ± 428.9, 421.2 ± 228.4, and 202.1 ± 84.3 days, respectively; there were statistically significant differences for all comparative combinations of AAH, BAC, and AC (Steel-Dwass test, P < .01). The mean VDT for pure and mixed GGO nodules was 628.5 ± 404.2 and 276.9 ± 155.9 days, respectively; it was significantly shorter for mixed than pure GGO nodules (Mann-Whitney U-test, P < .01). CONCLUSION The evaluation of VDT using computer-aided volumetry may be helpful in assessing the histological entities of GGO nodules.

Determination of point spread function in computed tomography accompanied with verification

A method for verifying the point spread function (PSF) measured by computed tomography has been previously reported [Med. Phys. 33, 2757-2764 (2006)]; however, this additional PSF verification following measurement is laborious. In the present study, the previously described verification method was expanded to PSF determination. First, an image was obtained by scanning a phantom. The image was then two-dimensionally deconvolved with the object function corresponding to the phantom structure, thus allowing the PSF to be obtained. Deconvolution is implemented simply by division of spatial frequencies (corresponding to inverse filtering), in which two parameters are used as adjustable ones. Second, an image was simulated by convolving the object function with the obtained PSF, and the simulated image was compared to the above-measured image of the phantom. The difference indicates the inaccuracy of the PSF obtained by deconvolution. As a criterion for evaluating the difference, the authors define the mean normalized standard deviation (SD) in the difference between simulated and measured images. The above two parameters for deconvolution can be adjusted by referring to the subsequent mean normalized SD (i.e., the PSF is determined so that the mean normalized SD is decreased). In this article, the parameters were varied in a fixed range with a constant increment to find the optimal parameter setting that minimizes the mean normalized SD. Using this method, PSF measurements were performed for various types of image reconstruction kernels (21 types) in four kinds of scanners. For the 16 types of kernels, the mean normalized SDs were less than 2.5%, indicating the accuracy of the determined PSFs. For the other five kernels, the mean normalized SDs ranged from 3.7% to 4.8%. This was because of a large amount of noise in the measured images, and the obtained PSFs would essentially be accurate. The method effectively determines the PSF, with an accompanying verification, after one scanning of a phantom.

Accuracy of lung nodule density on HRCT: analysis by PSF‐based image simulation

A computed tomography (CT) image simulation technique based on the point spread function (PSF) was applied to analyze the accuracy of CT‐based clinical evaluations of lung nodule density. The PSF of the CT system was measured and used to perform the lung nodule image simulation. Then, the simulated image was resampled at intervals equal to the pixel size and the slice interval found in clinical high‐resolution CT (HRCT) images. On those images, the nodule density was measured by placing a region of interest (ROI) commonly used for routine clinical practice, and comparing the measured value with the true value (a known density of object function used in the image simulation). It was quantitatively determined that the measured nodule density depended on the nodule diameter and the image reconstruction parameters (kernel and slice thickness). In addition, the measured density fluctuated, depending on the offset between the nodule center and the image voxel center. This fluctuation was reduced by decreasing the slice interval (i.e., with the use of overlapping reconstruction), leading to a stable density evaluation. Our proposed method of PSF‐based image simulation accompanied with resampling enables a quantitative analysis of the accuracy of CT‐based evaluations of lung nodule density. These results could potentially reveal clinical misreadings in diagnosis, and lead to more accurate and precise density evaluations. They would also be of value for determining the optimum scan and reconstruction parameters, such as image reconstruction kernels and slice thicknesses/intervals. PACS numbers: 87.57.‐s, 87.57.cf, 87.57.Q‐

A method for evaluating the performance of computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density

OBJECTIVE We propose the application of virtual nodules to evaluate the performance of computer-aided detection (CAD) of lung nodules in cancer screening using low-dose CT. METHODS The virtual nodules were generated based on the spatial resolution measured for a CT system used in an institution providing cancer screening and were fused into clinical lung images obtained at that institution, allowing site specificity. First, we validated virtual nodules as an alternative to artificial nodules inserted into a phantom. In addition, we compared the results of CAD analysis between the real nodules (n = 6) and the corresponding virtual nodules. Subsequently, virtual nodules of various sizes and contrasts between nodule density and background density (ΔCT) were inserted into clinical images (n = 10) and submitted for CAD analysis. RESULTS In the validation study, 46 of 48 virtual nodules had the same CAD results as artificial nodules (kappa coefficient = 0.913). Real nodules and the corresponding virtual nodules showed the same CAD results. The detection limits of the tested CAD system were determined in terms of size and density of peripheral lung nodules; we demonstrated that a nodule with a 5-mm diameter was detected when the nodule had a ΔCT > 220 HU. CONCLUSION Virtual nodules are effective in evaluating CAD performance using site-specific scan/reconstruction conditions. Advances in knowledge: Virtual nodules can be an effective means of evaluating site-specific CAD performance. The methodology for guiding the detection limit for nodule size/density might be a useful evaluation strategy.

Technical Note: Image filtering to make computer-aided detection robust to image reconstruction kernel choice in lung cancer CT screening.

PURPOSE In lung cancer computed tomography (CT) screening, the performance of a computer-aided detection (CAD) system depends on the selection of the image reconstruction kernel. To reduce this dependence on reconstruction kernels, the authors propose a novel application of an image filtering method previously proposed by their group. METHODS The proposed filtering process uses the ratio of modulation transfer functions (MTFs) of two reconstruction kernels as a filtering function in the spatial-frequency domain. This method is referred to as MTFratio filtering. Test image data were obtained from CT screening scans of 67 subjects who each had one nodule. Images were reconstructed using two kernels: fSTD (for standard lung imaging) and fSHARP (for sharp edge-enhancement lung imaging). The MTFratio filtering was implemented using the MTFs measured for those kernels and was applied to the reconstructed fSHARP images to obtain images that were similar to the fSTD images. A mean filter and a median filter were applied (separately) for comparison. All reconstructed and filtered images were processed using their prototype CAD system. RESULTS The MTFratio filtered images showed excellent agreement with the fSTD images. The standard deviation for the difference between these images was very small, ∼6.0 Hounsfield units (HU). However, the mean and median filtered images showed larger differences of ∼48.1 and ∼57.9 HU from the fSTD images, respectively. The free-response receiver operating characteristic (FROC) curve for the fSHARP images indicated poorer performance compared with the FROC curve for the fSTD images. The FROC curve for the MTFratio filtered images was equivalent to the curve for the fSTD images. However, this similarity was not achieved by using the mean filter or median filter. CONCLUSIONS The accuracy of MTFratio image filtering was verified and the method was demonstrated to be effective for reducing the kernel dependence of CAD performance.

A study on the performance evaluation of computer-aided diagnosis for detecting pulmonary nodules for the various CT reconstruction

The purpose of this study was to evaluate the performance of computer-aided diagnosis (CAD) system detecting pulmonary nodules for the various CT image qualities of the low dose CT cancer screening. Sixty three chest examinations with sixty-four pulmonary nodules consisting mainly ground-glass opacity (GGO) were used. All the CT images were acquired by using a multi-slice CT scanner Asteion with 4 detector rows system (Toshiba Medical Systems, Japan) with 0.75-second rotating time and 30mA. After the examination, CT image reconstructions were performed for every CT data set using seven reconstruction kernels and three sorts of slice thickness. Totally twenty-one data sets for a patient, namely 1323 data sets with about 60 thousands CT images which is 30.1GB data sets were investigated. Nodule detections were carried out using a computer-aided diagnosis system developed by Fujitsu Ltd, Japan. The mean nodule size was 0.69±0.28 (SD)[cm](range, 0.3-1.7cm). The CAD system identified 42 to 48 nodules out of the 64 nodules, in the slice thickness of 8mm for the seven reconstruction kernels, yielding a true-positive rate (TPR) of 65% to 75%. In the slice thickness of 5mm our CAD system indicates a TPR from 70% to 80%. In the slice thickness 10mm, TPR were resulted from 50% to 64%. Some kernel indicated relatively high TPR with high FP, other kernel showed high sensitivity with relatively low FP. CT image data sets with multi-reconstruction conditions is useful in assessing the robust characteristics of a CAD system detecting pulmonary nodule by multi-slice low dose CT screening.

Generation of realistic virtual nodules based on three‐dimensional spatial resolution in lung computed tomography: A pilot phantom study

Purpose: The aim of this feasibility study using phantoms was to propose a novel method for obtaining computer‐generated realistic virtual nodules in lung computed tomography (CT). Methods: In the proposed methodology, pulmonary nodule images obtained with a CT scanner are deconvolved with the point spread function (PSF) in the scan plane and slice sensitivity profile (SSP) measured for the scanner; the resultant images are referred to as nodule‐like object functions. Next, by convolving the nodule‐like object function with the PSF and SSP of another (target) scanner, the virtual nodule can be generated so that it has the characteristics of the spatial resolution of the target scanner. To validate the methodology, the authors applied physical nodules of 5‐, 7‐ and 10‐mm‐diameter (uniform spheres) included in a commercial CT test phantom. The nodule‐like object functions were calculated from the sphere images obtained with two scanners (Scanner A and Scanner B); these functions were referred to as nodule‐like object functions A and B, respectively. From these, virtual nodules were generated based on the spatial resolution of another scanner (Scanner C). By investigating the agreement of the virtual nodules generated from the nodule‐like object functions A and B, the equivalence of the nodule‐like object functions obtained from different scanners could be assessed. In addition, these virtual nodules were compared with the real (true) sphere images obtained with Scanner C. As a practical validation, five types of laboratory‐made physical nodules with various complicated shapes and heterogeneous densities, similar to real lesions, were used. The nodule‐like object functions were calculated from the images of these laboratory‐made nodules obtained with Scanner A. From them, virtual nodules were generated based on the spatial resolution of Scanner C and compared with the real images of laboratory‐made nodules obtained with Scanner C. Results: Good agreement of the virtual nodules generated from the nodule‐like object functions A and B of the phantom spheres was found, suggesting the validity of the nodule‐like object functions. The virtual nodules generated from the nodule‐like object function A of the phantom spheres were similar to the real images obtained with Scanner C; the root mean square errors (RMSEs) between them were 10.8, 11.1, and 12.5 Hounsfield units (HU) for 5‐, 7‐, and 10‐mm‐diameter spheres, respectively. The equivalent results (RMSEs) using the nodule‐like object function B were 15.9, 16.8, and 16.5 HU, respectively. These RMSEs were small considering the high contrast between the sphere density and background density (approximately 674 HU). The virtual nodules generated from the nodule‐like object functions of the five laboratory‐made nodules were similar to the real images obtained with Scanner C; the RMSEs between them ranged from 6.2 to 8.6 HU in five cases. Conclusions: The nodule‐like object functions calculated from real nodule images would be effective to generate realistic virtual nodules. The proposed method would be feasible for generating virtual nodules that have the characteristics of the spatial resolution of the CT system used in each institution, allowing for site‐specific nodule generation.

[The spread of low-dose lung ct screening and future task].

15/01/16 08:22 v2.10 緒 言 日本におけるがんによる死亡数は年間 36 万人を超 え総死亡数の約 29%を占めている.その中で肺がん による死亡数は全がん死の 20%,7万人を超え,大腸, 乳房,前立腺の合計を上回る死亡数となっている (2012 年:厚労省人口動態統計より).また昭和 50 年 代の喫煙率が 70%(男性)を超えた世代,そして現在も 2万人を超える喫煙者の多くが今後,肺がんに罹患す る高危険群を構成することとなる.この状況は治り得 る時期にある早期肺がん発見のためのシステムを構築 することが既に社会的な責務であることを示してい る.日本における対策型検診としての肺がん検診は 40 歳以上の男女に対して胸部 X 線が推奨されてい る.しかし,米国でのThe Prostate, Lung, Colorectal, and Ovarian (PLCO) Randomized Trial において胸部 X 線による肺がん死の低減が証明されず,胸部 X 線 に代わる新しい検診システムの構築が求められてい た.この現状に対し,日本,欧米における多くの研 究 によって低線量肺がん computed tomography (CT)検診による肺がん検診の有用性が報告されてき た.しかし,いずれも死亡率を評価したものではなく 大規模な無作為化比較試験の実施の必要性が求められ ていた.このようななか,欧米において CT検診の有 効性を証明するため複数の無作為化比較試験が行わ れ,この中で米国のNational Lung Screening Trial (NLST)では胸部X線に比べ CT検診による肺がん死 亡率の 20%の減少が示された .日本においても NLSTの結果を受け,今後は年齢,喫煙指数,事前の 問診などによって高危険群を抽出し,対象者を明確に したうえで対策型検診として胸部 X 線と組み合わせ た新しい検診システムの構築が進むと考えられる.し かし国民のニーズに応じて,いつ,どこの施設でも安 全で精度の高いCT検診を受けることができるために は精度管理を軸とした新しい体制の構築されているこ とが前提となる. 本報告ではCT検診による肺がんの早期発見に伴う 経済効果,そしてCT検診普及にむけた体制構築と人 材育成としての認定制度について述べる.またCT検 診における線量管理(dose index registry: DIR)の意 義,更に肺がんだけでなく慢性閉塞性肺疾患(chronic obstructive pulmonary disease: COPD)への早期発見 と早期介入を目指した今後の CT 検診への展開を 33

Accurate determination of CT PSF with high precision

A method for verifying the point spread function (PSF) measured by computed tomography has been previously reported [1]. However, this additional PSF verification following measurement is laborious. In the present study, the previously described verification method was expanded to PSF determination [2], that is, the PSF determination and verification were performed concurrently by one scanning of the phantom. Using this method, PSF measurements were performed for 10 types of image reconstruction kernels in two kinds of scanners. As a result, accurate PSF determinations were achieved. Also, reproducibility for determining the PSF.. was investigated by using four slice images. Using each image, the PSF was determined by the proposed method. The MTFs were derived from the PSFs, and that variation was evaluated. The standard deviation (SD) of the MTFs was found to be small, suggesting the stability for determining the PSF. Our method was concluded to be accurate with high precision determining the CT PSF.