Comparison of the quality of temporal subtraction images obtained with manual and automated methods of digital chest radiography

The authors have been developing a fully automated temporal subtraction scheme to assist radiologists in the detection of interval changes in digital chest radiographs. The temporal subtraction image is obtained by subtraction of a previous image from a current image. The authors' automated method includes not only image shift and rotation techniques but also a nonlinear geometric warping technique for reduction of misregistration artifacts in the subtraction image. However, a manual subtraction method that can be carried out only with image shift and rotation has been employed as a common clinical technique in angiography, and it might be clinically acceptable for detection of interval changes on chest radiographs as well. Therefore, the authors applied both the manual and automated temporal subtraction techniques to 181 digital chest radiographs, and compared the quality of the subtraction images obtained with the two methods. The numbers of clinically acceptable subtraction images were 147 (81.2%) and 176 (97.2%) for the manual and automated subtraction methods, respectively. The image quality of 148 (81.8%) subtraction images was improved by use of the automated method in comparison with the subtraction images obtained with the manual method. These results indicate that the automated method with the nonlinear warping technique can significantly reduce misregistration artifacts in comparison with the manual method. Therefore, the authors believe that the automated subtraction method is more useful for the detection of interval changes in digital chest radiographs.     

Development of a Voxel-Matching Technique for Substantial Reduction of Subtraction Artifacts in Temporal Subtraction Images Obtained from Thoracic MDCT

A temporal subtraction image, which is obtained by subtraction of a previous image from a current one, can be used for enhancing interval changes (such as formation of new lesions and changes in existing abnormalities) on medical images by removing most of the normal structures. However, subtraction artifacts are commonly included in temporal subtraction images obtained from thoracic computed tomography and thus tend to reduce its effectiveness in the detection of pulmonary nodules. In this study, we developed a new method for substantially removing the artifacts on temporal subtraction images of lungs obtained from multiple-detector computed tomography (MDCT) by using a voxel-matching technique. Our new method was examined on 20 clinical cases with MDCT images. With this technique, the voxel value in a warped (or nonwarped) previous image is replaced by a voxel value within a kernel, such as a small cube centered at a given location, which would be closest (identical or nearly equal) to the voxel value in the corresponding location in the current image. With the voxel-matching technique, the correspondence not only between the structures but also between the voxel values in the current and the previous images is determined. To evaluate the usefulness of the voxel-matching technique for removal of subtraction artifacts, the magnitude of artifacts remaining in the temporal subtraction images was examined by use of the full width at half maximum and the sum of a histogram of voxel values, which may indicate the average contrast and the total amount, respectively, of subtraction artifacts. With our new method, subtraction artifacts due to normal structures such as blood vessels were substantially removed on temporal subtraction images. This computerized method can enhance lung nodules on chest MDCT images without disturbing misregistration artifacts.     

Contralateral subtraction: a novel technique for detection of asymmetric abnormalities on digital chest radiographs

A novel contralateral subtraction technique has been developed to assist radiologists in the detection of asymmetric abnormalities on a single chest radiograph. With this method, the lateral inclination is first corrected by rotating and shifting the original chest image so that the midline of the thorax is aligned with the vertical centerline of the original chest image. The rotated image is then flipped laterally to produce a reversed "mirror" image. Finally, the mirror image is warped and subtracted from the original image for derivation of the contralateral subtraction image. The three key techniques which are employed in this study are applied successively to the initial contralateral subtraction technique for acquisition of improved subtraction images. One hundred PA chest radiographs, including 50 normals and 50 abnormals, were used as the database for this study. The percentage of chest images, which were rated as being adequate, good, or excellent quality of subtraction images by employing a subjective evaluation method, was improved from 73% to 91% by use of the three key techniques. The contralateral subtraction technique can be used for detection of any asymmetric abnormalities, such as lung nodules, pneumothorax, pneumonia, and emphysema, on a single chest radiograph, and therefore has potential utility in a high proportion of abnormal cases.     

Application of temporal subtraction for detection of interval changes on chest radiographs: Improvement of subtraction images using automated initial image matching

The authors developed a temporal subtraction scheme based on a nonlinear geometric warping technique to assist radiologists in the detection of interval changes in chest radiographs obtained on different occasions. The performance of the current temporal subtraction scheme is reasonably good; however, severe misregistration can occur in some cases. The authors evaluated the quality of 100 chest temporal subtraction images selected from their clinical image database. Severe misregistration was mainly attributable to initial incorrect global matching. Therefore, they attempted to improve the quality of the subtraction images by applying a new initial image matching technique to determine the global shift value between the current and the previous chest images. A cross-correlation method was employed for the initial image matching by use of blurred low-resolution chest images. Nineteen cases (40.4%) among 47 poor registered subtraction images were improved. These results show that the new initial image matching technique is very effective for improving the quality of chest temporal subtraction images, which can greatly enhance subtle changes in chest radiographs.     

Temporal subtraction of dual-energy chest radiographs

Temporal subtraction and dual-energy imaging are two enhanced radiography techniques that are receiving increased attention in chest radiography. Temporal subtraction is an image processing technique that facilitates the visualization of pathologic change across serial chest radiographic images acquired from the same patient; dual-energy imaging exploits the differential relative attenuation of x-ray photons exhibited by soft-tissue and bony structures at different x-ray energies to generate a pair of images that accentuate those structures. Although temporal subtraction images provide a powerful mechanism for enhancing visualization of subtle change, misregistration artifacts in these images can mimic or obscure abnormalities. The purpose of this study was to evaluate whether dual-energy imaging could improve the quality of temporal subtraction images. Temporal subtraction images were generated from 100 pairs of temporally sequential standard radiographic chest images and from the corresponding 100 pairs of dual-energy, soft-tissue radiographic images. The registration accuracy demonstrated in the resulting temporal subtraction images was evaluated subjectively by two radiologists. The registration accuracy of the soft-tissue-based temporal subtraction images was rated superior to that of the conventional temporal subtraction images. Registration accuracy also was evaluated objectively through an automated method, which achieved an area-under-the-ROC-curve value of 0.92 in the distinction between temporal subtraction images that demonstrated clinically acceptable and clinically unacceptable registration accuracy. By combining dual-energy soft-tissue images with temporal subtraction, misregistration artifacts can be reduced and superior image quality can be obtained.     

Iterative image warping technique for temporal subtraction of sequential chest radiographs to detect interval change.

A temporal subtraction technique has been developed to assist radiologists in the detection of interval changes on chest radiographs. Although the overall performance of the current temporal subtraction technique is relatively good, severe misregistration errors, mainly due to AP inclination and/or rotation, are observed in some cases. In order to reduce these errors, we attempted to improve the subtraction scheme by applying an iterative image warping technique. In cases obtained with the new temporal subtraction technique 177 (97.8%) of 181 showed adequate, good, or excellent quality. We also found that 156 (86.2%) of cases obtained with the new scheme showed improvements in the quality of the subtraction images compared with the previous scheme. The results indicate that the performance of the temporal subtraction technique was greatly improved by use of the iterative image warping technique.     

Improvement of temporal and dynamic subtraction images on abdominal CT using 3D global image matching and nonlinear image warping techniques

Accurate registration of the corresponding non-enhanced and arterial-phase CT images is necessary to create temporal and dynamic subtraction images for the enhancement of subtle abnormalities. However, respiratory movement causes misregistration at the periphery of the liver. To reduce these misregistration errors, we developed a temporal and dynamic subtraction technique to enhance small HCC by 3D global matching and nonlinear image warping techniques. The study population consisted of 21 patients with HCC. Using the 3D global matching and nonlinear image warping technique, we registered current and previous arterial-phase CT images or current non-enhanced and arterial-phase CT images obtained in the same position. The temporal subtraction image was obtained by subtracting the previous arterial-phase CT image from the warped current arterial-phase CT image. The dynamic subtraction image was obtained by the subtraction of the current non-enhanced CT image from the warped current arterial-phase CT image. The percentage of fair or superior temporal subtraction images increased from 52.4% to 95.2% using the new technique, while on the dynamic subtraction images, the percentage increased from 66.6% to 95.2%. The new subtraction technique may facilitate the diagnosis of subtle HCC based on the superior ability of these subtraction images to show nodular and/or ring enhancement.     

Temporal subtraction in chest radiography: automated assessment of registration accuracy

Radiologists routinely compare multiple chest radiographs acquired from the same patient over time to more completely understand changes in anatomy and pathology. While such comparisons are achieved conventionally through a side-by-side display of images, image registration techniques have been developed to combine information from two separate radiographic images through construction of a "temporal subtraction image." Although temporal subtraction images provide a powerful mechanism for the enhanced visualization of subtle change, errors in the clinical evaluation of these images may arise from misregistration artifacts that can mimic or obscure pathologic change. We have developed a computerized method for the automated assessment of registration accuracy as demonstrated in temporal subtraction images created from radiographic chest image pairs. The registration accuracy of 150 temporal subtraction images constructed from the computed radiography images of 72 patients was rated manually using a five-point scale ranging from "5-excellent" to "1-poor;" ratings of 3, 4, or 5 reflected clinically acceptable subtraction images, and ratings of 1 or 2 reflected clinically unacceptable images. Gray-level histogram-based features and texture measures are computed at multiple spatial scales within a "lung mask" region that encompasses both lungs in the temporal subtraction images. A subset of these features is merged through a linear discriminant classifier. With a leave-one-out-by-patient training/testing paradigm, the automated method attained an A(z) value of 0.92 in distinguishing between temporal subtraction images that demonstrated clinically acceptable and clinically unacceptable registration accuracy. A second linear discriminant classifier yielded an A(z) value of 0.82 based on a feature subset selected from an independent database of digitized film images. These methods are expected to advance the clinical utility of temporal subtraction images for chest radiography.     

Evaluation of the Image Quality of Temporal Subtraction Images Produced Automatically in a PACS Environment

The aim of the study is to evaluate the reliable production of temporal subtraction images in a picture archiving and communication system environment and to establish objective criteria for the evaluation of image quality. A total of 117 temporal subtraction chest images (55 in the upright position, 62 in the supine position) were obtained in five consecutive days. In all of these, we confirmed that there were no interval changes on the original images, and cases with diffuse lung disease were excluded. The temporal subtraction images were classified by three chest radiologists into five levels: 5, excellent; 4, good; 3, acceptable; 2, poor; and 1, very poor. The following were examined: (1) the yield of adequate quality of the temporal subtraction images; (2) whether the temporal subtraction images were obtained in the warping or nonwarping mode; and (3) the correlation of the overall subjective image quality with the relative shift angles, relative shift distances, and the standard deviation of gray levels in the temporal subtraction images. The percentages of acceptable temporal subtraction images were 100% and 66% in the upright and supine positions, respectively. Sixteen (26%) of the 62 supine-position images were made in nonwarping mode, whereas all upright images were made in warping mode. Significant correlations were obtained in the relative shift angle (P < 0.05), relative horizontal shift distance (P < 0.05), and standard deviation of gray levels (P < 0.0001). Temporal subtraction images with acceptable image quality were obtained in the upright position. The objective criteria may be useful for the evaluation of image quality.     

Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification

We developed an advanced computer-aided diagnostic (CAD) scheme for the detection of various types of lung nodules on chest radiographs intended for implementation in clinical situations. We used 924 digitized chest images (992 noncalcified nodules) which had a 500 x 500 matrix size with a 1024 gray scale. The images were divided randomly into two sets which were used for training and testing of the computerized scheme. In this scheme, the lung field was first segmented by use of a ribcage detection technique, and then a large search area (448 x 448 matrix size) within the chest image was automatically determined by taking into account the locations of a midline and a top edge of the segmented ribcage. In order to detect lung nodule candidates based on a localized search method, we divided the entire search area into 7 x 7 regions of interest (ROIs: 64 x 64 matrix size). In the next step, each ROI was classified anatomically into apical, peripheral, hilar, and diaphragm/heart regions by use of its image features. Identification of lung nodule candidates and extraction of image features were applied for each localized region (128 x 128 matrix size), each having its central part (64 x 64 matrix size) located at a position corresponding to a ROI that was classified anatomically in the previous step. Initial candidates were identified by use of the nodule-enhanced image obtained with the average radial-gradient filtering technique, in which the filter size was varied adaptively depending on the location and the anatomical classification of the ROI. We extracted 57 image features from the original and nodule-enhanced images based on geometric, gray-level, background structure, and edge-gradient features. In addition, 14 image features were obtained from the corresponding locations in the contralateral subtraction image. A total of 71 image features were employed for three sequential artificial neural networks (ANNs) in order to reduce the number of false-positive candidates. All parameters for ANNs, i.e., the number of iterations, slope of sigmoid functions, learning rate, and threshold values for removing the false positives, were determined automatically by use of a bootstrap technique with training cases. We employed four different combinations of training and test image data sets which was selected randomly from the 924 cases. By use of our localized search method based on anatomical classification, the average sensitivity was increased to 92.5% with 59.3 false positives per image at the level of initial detection for four different sets of test cases, whereas our previous technique achieved an 82.8% of sensitivity with 56.8 false positives per image. The computer performance in the final step obtained from four different data sets indicated that the average sensitivity in detecting lung nodules was 70.1% with 5.0 false positives per image for testing cases and 70.4% sensitivity with 4.2 false positives per image for training cases. The advanced CAD scheme involving the localized search method with anatomical classification provided improved detection of pulmonary nodules on chest radiographs for 924 lung nodule cases.     

Computed radiography dual energy subtraction: Performance evaluation when detecting low-contrast lung nodules in an anthropomorphic phantom

A dedicated chest computed radiography (CR) system has an option of energy subtraction (ES) acquisition. Two imaging plates, rather than one, are separated by a copper filter to give a high-energy and low-energy image. This study compares the diagnostic accuracy of conventional computed radiography to that of ES obtained with two radiographic techniques. One soft tissue only image was obtained at the conventional CR technique (s = 254) and the second was obtained at twice the radiation exposure (s = 131) to reduce noise. An anthropomorphic phantom with superimposed low-contrast lung nodules was imaged 53 times for each radiographic technique. Fifteen images had no nodules; 38 images had a total of 90 nodules placed on the phantom. Three chest radiologists read the three sets of images in a receiver operating characteristic (ROC) study. Significant differences in Az were only found between (1) the higher exposure energy subtracted images and the conventional dose energy subtracted images (P = .095, 90% confidence), and (2) the conventional CR and the energy subtracted image obtained at the same technique (P = .024, 98% confidence). As a result of this study, energy subtracted images cannot be substituted for conventional CR images when detecting low-contrast nodules, even when twice the exposure is used to obtain them.     

Angiographic film substraction using a laser digitizer and computer processing

Digital subtraction angiography has been accepted as an invaluable clinical tool over the past decade; however, film-screen-based angiography is still performed routinely when high-resolution or large field-of-view angiograms are needed. A technique is presented whereby two films from an angiographic sequence are digitized using a high-resolution laser digitizer, and the digitized images are aligned, subtracted, and displayed using the computer. To accommodate for some types of patient motion, an image warping algorithm is presented and discussed in detail. The warping algorithm is piecewise linear, using triangular regions for warping, resulting in a global nonlinear transform across triangle elements. An algorithm describing optimal triangle selection also is discussed. The results show that subtraction images of excellent quality can be produced by the proposed technique, and suggest that, in some settings, digitized subtraction films may be preferred over conventional film subtraction.     

Enhancement of chest images by automatic adaptive spatial filtering

Postprocessing of the image data is an exciting capability of digital radiography that may improve diagnostic performance. We present a new algorithm that selectively enhances edges and contrast in both lungs and mediastinum while minimally amplifying noise in chest images. Using different size kernels, two smoothed images are generated from the original chest image. The two regions of interest (lungs and mediastinum) are identified based on the distribution of pixel values in the image. A modified nonlinear unsharp mask subtraction technique is then applied. The resulting image has enhanced high- and middle-frequency information in the mediastinum without distorting lung parenchyma or significantly enhancing noise. We consider that the technique employed in this study could be suitable for routine use although its true effectiveness in improving diagnostic accuracy awaits observer-performance evaluation that is currently under way.     

Adaptive Voxel Matching for Temporal CT Subtraction

Temporal subtraction (TS) technique calculates a subtraction image between a pair of registered images acquired from the same patient at different times. Previous studies have shown that TS is effective for visualizing pathological changes over time; therefore, TS should be a useful tool for radiologists. However, artifacts caused by partial volume effects degrade the quality of thick-slice subtraction images, even with accurate image registration. Here, we propose a subtraction method for reducing artifacts in thick-slice images and discuss its implementation in high-speed processing. The proposed method is based on voxel matching, which reduces artifacts by considering gaps in discretized positions of two images in subtraction calculations. There are two different features between the proposed method and conventional voxel matching: (1) the size of a searching region to reduce artifacts is determined based on discretized position gaps between images and (2) the searching region is set on both images for symmetrical subtraction. The proposed method is implemented by adopting an accelerated subtraction calculation method that exploit the nature of liner interpolation for calculating the signal value at a point among discretized positions. We quantitatively evaluated the proposed method using synthetic data and qualitatively using clinical data interpreted by radiologists. The evaluation showed that the proposed method was superior to conventional methods. Moreover, the processing speed using the proposed method was almost unchanged from that of the conventional methods. The results indicate that the proposed method can improve the quality of subtraction images acquired from thick-slice images.     

Scatter-glare corrections in quantitative dual-energy fluoroscopy

Previous attempts to use time subtraction intravenous digital subtraction angiography for ventricular imaging have been hampered by artifacts due to cardiac and respiratory motion. We have previously reported a motion-immune dual-energy technique in which kVp is switched between 60 and 120, at 300-500 mA, 30 times/s. In order to quantitate parameters such as ejection fraction and left ventricular volume, it is necessary to correct for scatter and veiling glare (SVG), which are the major sources of nonlinearities in videodensitometric digital subtraction angiography (DSA). In this report, a convolution filtering method has been investigated to estimate SVG in DSA images. In the first step, a grey level transformation of the detected image is utilized to get an estimated SVG image. In the second step this image is convolved to produce an image with appropriate spatial frequency content. Estimates of SVG in several Humanoid chest phantom images were obtained using Gaussian convolution kernels with a full width at half-maximum (FWHM) of 51-125 pixels. The root-mean-square (rms) percentage error of these estimates was obtained by comparison with direct SVG measurement. A convolution kernel with a FWHM of 75 pixels in each dimension applied to 16 Humanoid phantom images with various projections, thicknesses, and beam energies resulted in an average rms percentage error of 9.7% in the SVG estimate, for the 16 cases studied. The SVG estimation consisting of grey scale-to-SVG fraction lookup table (LUT) is made based on previous measurements. The x-ray settings required for each patient are utilized to alter the LUT in order to account for patient thickness variations.(ABSTRACT TRUNCATED AT 250 WORDS)     

A computer-aided temporal and dynamic subtraction technique of the liver for detection of small hepatocellular carcinomas on abdominal CT images

It is often difficult for radiologists to identify small hepatocellular carcinomas (HCCs) due to insufficient contrast enhancement. Therefore, we have developed a new computer-aided temporal and dynamic subtraction technique to enhance small HCCs, after automatically selecting images set at the same anatomical position from the present (non-enhanced and arterial-phase CT images) and previous images. The present study was performed with CT images from 14 subjects. First, we used template-matching based on similarities in liver shape between the present (non-enhanced and arterial-phase CT images) and previous arterial-phase CT images at the same position. Temporal subtraction images were then obtained by subtraction of the previous image from the present image taken at the same position of the liver. Dynamic subtraction images were also obtained by subtraction of non-enhanced CT images from arterial-phase CT images taken at the same position of the liver. Twenty-one of 22 nodules (95.5%) with contrast enhancement were visualized in temporal and dynamic subtraction images. Compared with present arterial-phase CT images, increases of 150% and 140% in nodule-to-liver contrast were observed on dynamic and temporal subtraction images, respectively. These subtraction images may be useful as reference images in the detection of small moderately differentiated HCCs.     

Low-cost soft-copy display accuracy in the detection of pulmonary nodules by single-exposure dual-energy subtraction: Comparison with hard-copy viewing

This study endeavored to clarify the usefulness of single-exposure dual-energy subtraction computed radiography (CR) of the chest and the ability of soft-copy images to detect low-contrast simulated pulmonary nodules. Conventional and bone-subtracted CR images of 25 chest phantom image sets with a low-contrast nylon nodule and 25 without a nodule were interpreted by 12 observers (6 radiologists, 6 chest physicians) who rated each on a continuous confidence scale and marked the position of the nodule if one was present. Hard-copy images were 7 x 7-inch laser-printed CR films, and soft-copy images were displayed on a 21-inch noninterlaced color CRT monitor with an optimized dynamic range. Soft-copy images were adjusted to the same size as hard-copy images and were viewed under darkened illumination in the reading room. No significant differences were found between hard- and soft-copy images. In conclusion, the soft-copy images were found to be useful in detecting low-contrast simulated pulmonary nodules.     

Computerized detection of pulmonary nodules in digital chest images: use of morphological filters in reducing false-positive detections

Currently, radiologists can fail to detect lung nodules in up to 30% of actually positive cases. If a computerized scheme could alert the radiologist to locations of suspected nodules, then potentially the number of missed nodules could be reduced. We are developing such a computerized scheme that involves a difference-image approach and various feature-extraction techniques. In this paper, we describe our use of digital morphological processing in the reduction of computer-identified false-positive detections. A feature-extraction technique, which includes the sequential application of nonlinear filters of erosion and dilation, is employed to reduce the camouflaging effect of ribs and vessels on nodule detection. This additional feature-extraction technique reduced the true-positive rate of the computerized scheme by 13% and the false-positive rate by 50%. In a comparison of the scheme with and without the additional feature-extraction technique, inclusion of the additional technique increased the detection sensitivity by about half at the level of three to four false-positive detections per chest image.     

Clinical evaluation of irreversible data compression for computed radiography of the chest

Efficient data compression is essential for practical daily operation of computed radiography (CR) systems. In this study the clinical applicability of type III irreversible high data compression using an FCR 9501 chest unit (Fuji Photo Film, Tokyo, Japan) was evaluated. Sixty-eight normal and 93 various abnormal cases, with an additional 15 cases of lung cancers with solitary lung nodules, were selected from the file. A pair of hard copies of original images and images reconstructed using type III compression was made for each case. Six radiologists evaluated the image quality by visual rating and receiver operating characteristic (ROC) curve analysis. For all five anatomic regions of normal cases, “original equal to compressed” was the most common response, followed by “original significantly better than compressed.” When abnormal cases were evaluated for diagnostic information, there was no significant difference between the compressed and original images. ROC curve analysis on lung nodules with lung cancer showed no significant difference between the two. Compressed CR images using the type III irreversible technique are clinically applicable and acceptable despite slight degradation of image quality.     

Diagnostic usefulness of chest computed radiography—Film versus cathode-ray tube images

Seventy-one plain chest images obtained by computed radiography (CR) with an imaging plate were interpreted on film and two kinds of cathode-ray tube (CRT) monitors installed separately at two facilities (1,024 × 1,536 pixels, 8 bits, and 1,024 × 1,280 pixels, 10 bits) by 20 radiologists and four chest internists. The clinical categories of these 71 cases included pulmonary nodules and interstitial abnormalities. Image reading sessions were held over a total of 4 days, ie, 2 days and then another 2 days, 3 weeks later. Twenty-four observers formed four groups with six members each. Two groups read either films or CRT images at one of the two facilities. In the second experiment, 26 of 71 images were compressed at 10∶1, 19 of 71 were compressed at 20∶1, and 26 were not compressed. Analyses of the areas under the receiver-operating characteristic curves showed no significant differences in detection of pulmonary abnormalities between film and CRT. In detecting interstitial pulmonary abnormalities, film was more sensitive than CRT monitor. There were no significant differences in observers’ performances between the two different kinds of CRT workstation. Subjective evaluation of image quality showed that images irreversibly compressed to the ratios of 10∶1 and 20∶1 were inferior to original images. Although further considerations are needed with regard to spatial resolution requirements, image processing, and image compression, the utilization of CR CRT image as a substitute for CR film image will be possible.