Improving the Accuracy of the Effective Atomic Number (EAN) and Relative Electron Density (RED) with Stoichiometric Calibration on PCD-CT Images.

The photon counting detector (PCD) in computed tomography (CT) can count the number of incoming photons in order to obtain energy information for photons corresponding to user-defined thresholds. Research on the extraction of effective atomic number (EAN) and relative electron density (RED) using dual-energy CT (DECT) is currently underway. This study proposes a method for improving EAN and RED accuracy of tissue-equivalent materials by using PCD-CT-based stoichiometric calibration. After obtaining DECT images in energy bin (EB) and full spectrum (FS) modes for eight tissue-equivalent materials, the EAN was calculated with stoichiometric calibration. Using the EAN image, the RED image was acquired to evaluate the accuracy. The errors of both EAN and RED obtained with EB were within 4%. In particular, the accuracy of RED was higher than that of the FS method. Study results indicate that PCD-CT contributes to improving EAN and RED accuracy. Further studies will be aimed at reducing ring artifacts by pixel-correcting PCD images and improving stopping power ratio (SPR) measurements for dose calculation in particle therapy.

4D digital tomosynthesis image reconstruction using brute force-based adaptive total variation (BF-ATV) in a prototype LINAC system.

Respiratory-correlated cone-beam CT (CBCT) not only inhibits rapid scanning due to the slow speed of the LINAC head gantry rotation, but its implementation for routine patient imaging is impractical because of the high radiation dose delivered during the process. Digital tomosynthesis (DTS) is a potentially faster technique that delivers a much lower radiation dose by reducing the number of projections in a limited angular range. Unfortunately, 4D-DTS introduces strong aliasing artifacts in the reconstructed images due to the sparsely sampled projections in each respiratory phase bin. The authors hereby suggest a novel low-dose 4D-DTS image reconstruction method that achieves a compromise between the occurrence of aliasing artifacts and image smoothing using a brute force-based adaptive weighting parameter searching technique. We used a prototype LINAC system mounted with a flat-panel detector to acquire tomosynthesis projections of respiratory motion in a phantom in the anterior-posterior (AP) and lateral views. Three different 4D-DTS image reconstruction schemes that included conventional filtered back-projection (FBP), adaptive steepest descent projection onto convex sets (ASD-POCS), and the proposed brute force-based adaptive total variation (BF-ATV) were implemented in four different respiratory phase bins for both AP and lateral views. All reconstructions were accelerated using a single GPU card to reduce the computation time. To study the performance of the algorithm under various sparse conditions, we operated the prototype system in three different gantry sweep modes. The results indicate that the proposed BF-ATV method yields the largest structural similarities in the differenced image between the ground-truth dataset acquired using the slow gantry sweep mode and the sparse dataset from both moderate and fast sweep modes. In addition, the proposed method maintained the object sharpness with less streaking lines and small loss of sharpness compared to the conventional FBP and ASD-POCS methods. In conclusion, the proposed low-dose 4D-DTS reconstruction scheme may provide better performance due in part to its rapid scanning. Therefore, it is potentially applicable to practical 4D imaging for radiotherapy.

Decrease in interpretation time for both novice and experienced readers using a concurrent computer-aided detection system for digital breast tomosynthesis.

OBJECTIVES: To compare the diagnostic performance and interpretation time of digital breast tomosynthesis (DBT) for both novice and experienced readers with and without using a computer-aided detection (CAD) system for concurrent read. METHODS: CAD system was developed for concurrent read in DBT interpretation. In this observer performance study, we used an enriched sample of 100 DBT cases including 70 with and 30 without breast cancers. Image interpretation was performed by four radiologists with different experience levels (two experienced and two novice). Each reader completed two reading sessions (at a minimum 2-month interval), once with and once without CAD. Three different rating scales were used to record each reader's interpretation. Reader performance with and without CAD was reported and compared for each radiologist. Reading time for each case was also recorded. RESULTS: Average area under the receiver operating characteristic curve values for BI-RADS scale on using CAD were 0.778 and 0.776 without using CAD, demonstrating no statistically significant differences. Results were consistent when the probability of malignancy and percentage probability of malignancy scales were used. Reading times per case were 72.07 s and 62.03 s (SD, 37.54 s vs 34.38 s) without and with CAD, respectively. The average difference in reading time on using CAD was a statistically significant decrease of 10.04 ± 1.85 s, providing 14% decrease in time. The time-reducing effect was consistently observed in both novice and experienced readers. CONCLUSION: DBT combined with CAD reduced interpretation time without diagnostic performance loss to novice and experienced readers. KEY POINTS: • The use of a concurrent DBT-CAD system shortened interpretation time. • The shortened interpretation time with DBT-CAD did not come at a cost to diagnostic performance to novice or experienced readers. • The concurrent DBT-CAD system improved the efficiency of DBT interpretation.

Fully automated nipple detection in digital breast tomosynthesis.

BACKGROUND AND OBJECTIVE: We propose a nipple detection algorithm for use with digital breast tomosynthesis (DBT) images. DBT images have been developed to overcome the weaknesses of 2D mammograms for denser breasts by providing 3D breast images. The nipple location acts as an invaluable landmark in DBT images for aligning the right and left breasts and describing the relative location of any existing lesions. METHODS: Nipples may be visible or invisible in a breast image, and therefore a nipple detection method must be able to detect the nipples for both cases. The detection method for visible nipples based on their shape is simple and highly efficient. However, it is difficult to detect invisible nipples because they do not have a prominent shape. Fibroglandular tissue in a breast is anatomically connected with the nipple. Thus, the nipple location can be detected by analyzing the location of such tissue. In this paper, we propose a method for detecting the location of both visible and invisible nipples using fibroglandular tissue and changes in the breast area. RESULTS: Our algorithm was applied to 138 DBT images, and its nipple detection accuracy was evaluated based on the mean Euclidean distance. The results indicate that our proposed method achieves a mean Euclidean distance of 3.10±2.58mm. CONCLUSIONS: The nipple location can be a very important piece of information in the process of a DBT image registration. This paper presents a method for the automatic nipple detection in a DBT image. The extracted nipple location plays an essential role in classifying any existing lesions and comparing both the right and left breasts. Thus, the proposed method can help with computer-aided detection for a more efficient DBT image analysis.

Automated Detection Algorithm of Breast Masses in Three-Dimensional Ultrasound Images.

OBJECTIVES: We propose an automatic breast mass detection algorithm in three-dimensional (3D) ultrasound (US) images using the Hough transform technique. METHODS: One hundred twenty-five cropped images containing 68 benign and 60 malignant masses are acquired with clinical diagnosis by an experienced radiologist. The 3D US images are masked, subsampled, contrast-adjusted, and median-filtered as preprocessing steps before the Hough transform is used. Thereafter, we perform 3D Hough transform to detect spherical hyperplanes in 3D US breast image volumes, generate Hough spheres, and sort them in the order of votes. In order to reduce the number of the false positives in the breast mass detection algorithm, the Hough sphere with a mean or grey level value of the centroid higher than the mean of the 3D US image is excluded, and the remaining Hough sphere is converted into a circumscribing parallelepiped cube as breast mass lesion candidates. Finally, we examine whether or not the generated Hough cubes were overlapping each other geometrically, and the resulting Hough cubes are suggested as detected breast mass candidates. RESULTS: An automatic breast mass detection algorithm is applied with mass detection sensitivity of 96.1% at 0.84 false positives per case, quite comparable to the results in previous research, and we note that in the case of malignant breast mass detection, every malignant mass is detected with false positives per case at a rate of 0.62. CONCLUSIONS: The breast mass detection efficiency of our algorithm is assessed by performing a ROC analysis.

Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis.

We propose computer-aided detection (CADe) algorithm for microcalcification (MC) clusters in reconstructed digital breast tomosynthesis (DBT) images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP) reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D) objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR) enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.

Glioma grading using apparent diffusion coefficient map: application of histogram analysis based on automatic segmentation.

The accurate diagnosis of glioma subtypes is critical for appropriate treatment, but conventional histopathologic diagnosis often exhibits significant intra-observer variability and sampling error. The aim of this study was to investigate whether histogram analysis using an automatically segmented region of interest (ROI), excluding cystic or necrotic portions, could improve the differentiation between low-grade and high-grade gliomas. Thirty-two patients (nine low-grade and 23 high-grade gliomas) were included in this retrospective investigation. The outer boundaries of the entire tumors were manually drawn in each section of the contrast-enhanced T1 -weighted MR images. We excluded cystic or necrotic portions from the entire tumor volume. The histogram analyses were performed within the ROI on normalized apparent diffusion coefficient (ADC) maps. To evaluate the contribution of the proposed method to glioma grading, we compared the area under the receiver operating characteristic (ROC) curves. We found that an ROI excluding cystic or necrotic portions was more useful for glioma grading than was an entire tumor ROI. In the case of the fifth percentile values of the normalized ADC histogram, the area under the ROC curve for the tumor ROIs excluding cystic or necrotic portions was significantly higher than that for the entire tumor ROIs (p < 0.005). The automatic segmentation of a cystic or necrotic area probably improves the ability to differentiate between high- and low-grade gliomas on an ADC map.

A preliminary study of the effect of electrode placement in order to define a suitable location for two electrodes and obtain sufficiently reliable ECG signals when monitoring with wireless system.

Most countries face high and increasing rates of cardiovascular disease. Each year, heart disease kills more Americans than cancer. Therefore, there has been a promising market for portable ECG equipment and it is increasing. To use portable ECG measuring devices, it is essential to define a suitable location for the measuring as we need to reduced electrode size and distance. This research proposes to study how the inter-electrode distance affects the signal and how the electrode pair should be placed on the chest in order to obtain a sufficiently reliable ECG signal to detect heart arrhythmias in any environment, such as home or work. Therefore, we developed a compact, portable patch type ambulatory ECG monitoring system, Heart Tracker, using a microprocessor for preliminary study of signal analysis. To optimize the electrode arrangement in wireless environment, we compared HT and standard 12 lead with changing electrode position.

Development of a patch type embedded cardiac function monitoring system using dual microprocessor for arrhythmia detection in heart disease patient.

A patch type embedded cardiac function monitoring system was developed to detect arrhythmias such as PVC (Premature Ventricular Contraction), pause, ventricular fibrillation, and tachy/bradycardia. The overall system is composed of a main module including a dual processor and a Bluetooth telecommunication module. The dual microprocessor strategy minimizes power consumption and size, and guarantees the resources of embedded software programs. The developed software was verified with standard DB, and showed good performance.

Sequential algorithm for the detection of the shockable rhythms in electrocardiogram.

We suggest a sequential algorithm for the detection of the ventricular fibrillation (VF) and ventricular tachycardia (VT) of a rate above 180 bpm, so called shockable rhythms. The built-in algorithm for ECG analysis embedded in the portable bio-signal sensing module is aimed to discriminate between shockable and non-shockable rhythms and its accuracy is analyzed. An algorithm for VF/VT detection is proposed to analyze every 1 s ECG episode using the past 8 s episodes. The method is tested with 844,587 ECG episodes from the widely accepted databases. A sensitivity of 86.8 % and a specificity of 99.4 % were obtained and compared with the previous results.

Development of an integrated obesity management waist belt system composed of calorie tracking and waist circumference measuring module for long term monitoring.

A waist belt type simple device was designed to monitor the individuals who are obese and want to maintain their consumed calories and waist circumference changes. Relatively long term monitoring with this device could help them maintain their health conditions. This devised system is composed of calorie tracking and waist circumference module. This study suggests feasible and meaningful results. If users' data are collected largely with this developed system, we can reveal the relationship between obesity and daily life pattern.

A basic study for patch type ambulatory 3-electrode ECG monitoring system for the analysis of acceleration signal and the limb leads and augmented unipolar limb leads signal.

A compact and portable device was designed for preliminary study of patch type ambulatory 3-electrode ECG monitoring system for limb leads and augmented unipolar limb leads signal analysis. The developed system also can measure and send the acceleration signals wirelessly during daily life to analyze the user's information. The 3-electrode system is feasible to provide the information of limb leads and augmented unipolar limb leads for further arrhythmia analysis and simultaneously shows lots of issues to be more broadly applicable and more powerful device.

A preliminary study on discrimination of osteoporotic fractured group from nonfractured group using support vector machine.

Osteoporosis is characterized by an abnormal loss of bone mineral content, which leads to a tendency to non-traumatic bone fractures or to structural deformations of bone. Thus, bone density has been considered as a most reliable parameter to assess osteoporotic fracture risk. In past decades, by the way, bone texture measures have been studied to estimate other aspect of bone quality. Some studies have been performed on CT or MR images to assess bone quality using trabecular structure analysis. Other studies have been performed on plain x-ray images or ultrasound images to assess trabecular structure. However, most of the studies are focused on individual parameters to distinguish between osteoporotic fractured group and nonfractured group. In this preliminary study, we combine various texture parameters with bone density parameters using a support vector machine and point out the most promising combination of parameters to distinguish between osteoporotic fractured group and nonfractured group.

Bone mineral density estimation using the filling factor of the radius X-ray image.

Osteoporosis is characterized by an abnormal loss of bone mineral content, which leads to a tendency to non-traumatic bone fractures or to structural deformations of bone. Thus, bone density measurement has been considered as a most reliable method to assess bone fracture risk due to osteoporosis. In past decades, x-ray images have been studied in connection with the bone mineral density estimation. However, the estimated bone mineral density from the x-ray image can undergo a relatively large accuracy or precision error. The most relevant origin of the accuracy or precision error may be unstable x-ray image acquisition condition. In the previous study, we presented a bone mineral density estimation method that uses the trabecular bone filling factor in the radius x-ray image and that is relatively insensitive to the x-ray image acquisition condition. In this paper, we investigate the method using larger patient data and point out a preliminary result related to the fracture risk prediction using the method.

The echotextural characteristics for the diagnosis of the liver cirrhosis using the sonographic images.

Various statistical parameters have been tried for the computer-aided diagnosis of the liver fibrosis. The region of interest (ROI) for the liver and spleen parenchymas have been chosen, and the hepatolienal textural contrast for each ultrasound (US) image has been examined. The selectively chosen textural parameters are linearly combined with the pre-determined coefficients to give the computer-aided diagnostic parameter for the liver fibrosis, whose final stage is named as cirrhosis. From the comparison with the clinical diagnosis it is suggested that the proposed calculation scheme using the textural parameters show the quite promising classification performance for the computer-aided diagnosis of the liver cirrhosis.

The GGO lesions detected by computer-aided detection system on chest MDCT images.

The detection of abnormal lesions in the early stages of lung cancer is important to improve survival. Computer-aided detection (CAD) system can be useful for early detection of pulmonary nodules on computed tomography (CT) images for screening. Moreover, CAD system can be 'second opinion' when a radiologist detects the pulmonary nodules on multi-slice CT images. We developed a computer-aided detection system for pulmonary nodule detection on multi-detector row computed tomography (MDCT) images. We applied the nodule isolation technique using radial distribution function and additional algorithms. In this paper, we reported the ground-glass opacity (GGO) lesions detected by self-developed computer-aided pulmonary nodule detection system.

Distal radius bone mineral density estimation using the filling factor of trabecular bone in the x-ray image.

Osteoporosis is characterized by an abnormal loss of bone mineral content, which leads to a tendency to non-traumatic bone fractures or to structural deformations of bone. Thus, bone density measurement has been considered as a most reliable method to assess bone fracture risk due to osteoporosis. In past decades, X-ray images have been studied in connection with the bone mineral density estimation. However, the estimated bone mineral density from the X-ray image can undergo a relatively large accuracy or precision error. The most relevant origin of the accuracy or precision error may be unstable X-ray image acquisition condition. Thus, we focus our attentions on finding a bone mineral density estimation method that is relatively insensitive to the X-ray image acquisition condition. In this paper, we develop a simple technique for distal radius bone mineral density estimation using the trabecular bone filling factor in the X-ray image and apply the technique to the wrist X-ray images of 20 women. Estimated bone mineral density shows a high linear correlation with a dual-energy X-ray absorptiometry (r=0.87).

The preliminary study of differentiating osteoporotic fractured group from nonfractured group.

Osteoporosis is characterized by an abnormal loss of bone mineral content, which leads to a tendency to nontraumatic bone fractures or to structural deformations of bone. Thus bone density measurement has been considered as a most reliable method to assess bone fracture risk due to osteoporosis. In past decades bone texture measures have been also studied in connection with the bone quality estimation. However, most studies have been focused on texture analysis of CT or MR images. Though studies on plain radiographs have been also performed to assess in vivo trabecular structure these studies are mainly done on anatomic sites such as femur, spine, and calcaneus. In this preliminary study we apply various texture measures to distal radius plain radiographs and point out several promising texture measures that significantly distinguish between osteoporotic fractured group and nonfractured group.

Computer-assisted Sonographic Analysis of the Hepatorenal and Textural Features for the Diagnosis of the Fatty Liver.

We investigated the B-mode ultrasound (US) images of abdomen by normalizing the gray level values of the liver with the representative gray levels (RGL's) of the liver parenchyma, renal cortex, subcutaneous fat, to quantify the fatty infiltration of the liver (FIL). The RGL's were compared with the clinical diagnosis of the FIL showing good correlation between them.