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1.  Improvement of Partial Volume Segmentation for Brain Tissue on Diffusion Tensor Images Using Multiple-Tensor Estimation 
Journal of Digital Imaging  2013;26(6):1131-1140.
To improve evaluations of cortical and subcortical diffusivity in neurological diseases, it is necessary to improve the accuracy of brain diffusion tensor imaging (DTI) data segmentation. The conventional partial volume segmentation method fails to classify voxels with multiple white matter (WM) fiber orientations such as fiber-crossing regions. Our purpose was to improve the performance of segmentation by taking into account the partial volume effects due to both multiple tissue types and multiple WM fiber orientations. We quantitatively evaluated the overall performance of the proposed method using digital DTI phantom data. Moreover, we applied our method to human DTI data, and compared our results with those of a conventional method. In the phantom experiments, the conventional method and proposed method yielded almost the same root mean square error (RMSE) for gray matter (GM) and cerebrospinal fluid (CSF), while the RMSE in the proposed method was smaller than that in the conventional method for WM. The volume overlap measures between our segmentation results and the ground truth of the digital phantom were more than 0.8 in all three tissue types, and were greater than those in the conventional method. In visual comparisons for human data, the WM/GM/CSF regions obtained using our method were in better agreement with the corresponding regions depicted in the structural image than those obtained using the conventional method. The results of the digital phantom experiment and human data demonstrated that our method improved accuracy in the segmentation of brain tissue data on DTI compared to the conventional method.
PMCID: PMC3824924  PMID: 23589185
DTI; Brain tissue segmentation; Digital DTI phantom; Partial volume effect; Multiple-tensor estimation
2.  Integration of Temporal Subtraction and Nodule Detection System for Digital Chest Radiographs into Picture Archiving and Communication System (PACS): Four-year Experience 
Journal of Digital Imaging  2007;21(1):91-98.
Since May 2002, temporal subtraction and nodule detection systems for digital chest radiographs have been integrated into our hospital’s picture archiving and communication systems (PACS). Image data of digital chest radiographs were stored in PACS with the digital image and communication in medicine (DICOM) protocol. Temporal subtraction and nodule detection images were produced automatically in an exclusive server and delivered with current and previous images to the work stations. The problems that we faced and the solutions that we arrived at were analyzed. We encountered four major problems. The first problem, as a result of the storage of the original images’ data with the upside-down, reverse, or lying-down positioning on portable chest radiographs, was solved by postponing the original data storage for 30 min. The second problem, the variable matrix sizes of chest radiographs obtained with flat-panel detectors (FPDs), was solved by improving the computer algorithm to produce consistent temporal subtraction images. The third problem, the production of temporal subtraction images of low quality, could not be solved fundamentally when the original images were obtained with different modalities. The fourth problem, an excessive false-positive rate on the nodule detection system, was solved by adjusting this system to chest radiographs obtained in our hospital. Integration of the temporal subtraction and nodule detection system into our hospital’s PACS was customized successfully; this experience may be helpful to other hospitals.
PMCID: PMC3043823  PMID: 17333415
Nodule detection system; temporal subtraction; picture archiving and communication systems (PACS); computed radiography (CR) flat-panel detectors (FPDs)
3.  Evaluation of the Image Quality of Temporal Subtraction Images Produced Automatically in a PACS Environment 
Journal of Digital Imaging  2006;19(4):383-390.
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.
PMCID: PMC3045158  PMID: 16741663
Chest radiography; temporal subtraction; computer-assisted diagnosis; computer-assisted image interpretation; PACS
4.  Computer-Aided Nodule Detection on Digital Chest Radiography: Validation Test on Consecutive T1 Cases of Resectable Lung Cancer 
Journal of Digital Imaging  2006;19(4):376-382.
To evaluate the usefulness of a commercially available computer-assisted diagnosis (CAD) system on operable T1 cases of lung cancer by use of digital chest radiography equipment.
Materials and Methods
Fifty consecutive patients underwent surgery for primary lung cancer, and 50 normal cases were selected. All cancer cases were histopathologically confirmed T1 cases. All normal individuals were selected on the basis of chest computed tomography (CT) confirmation and were matched with cancer cases in terms of age and gender distributions. All chest radiographs were obtained with one computed radiography or two flat-panel detector systems. Eight radiologists (four chest radiologists and four residents) participated in observer tests and interpreted soft copy images by using an exclusive display system without and with CAD output. When radiologists diagnosed cases as positives, the locations of lesions were recorded on hard copies. The observers’ performance was evaluated by receiver operating characteristic analysis.
The overall detectability of lung cancer cases with CAD system was 74% (37/50), and the false-positive rate was 2.28 (114/50) false positives per case for normal cases. The mean Az value increased significantly from 0.896 without CAD output to 0.923 with CAD output (P = 0.018). The main cause of the improvement in performance is attributable to changes from false negatives without CAD to true positives with CAD (19/31, 61%). Moreover, improvement in the location of the tumor was observed in 1.5 cases, on average, for radiology residents.
This CAD system for digital chest radiographs is useful in assisting radiologists in the detection of early resectable lung cancer.
PMCID: PMC3045164  PMID: 16763934
Chest radiography; lung cancer; computer-aided nodule detection; screening; computer-assisted diagnosis; computer-assisted image interpretation; PACS

Results 1-4 (4)