Keh-Shih Chuang

A three-dimensional registration method for automated fusion of micro PET-CT-SPECT whole-body images

Micro positron emission tomography (PET) and micro single-photon emission computed tomography (SPECT), used for imaging small animals, have become essential tools in developing new pharmaceuticals and can be used, among other things, to test new therapeutic approaches in animal models of human disease, as well as to image gene expression. These imaging techniques can be used noninvasively in both detection and quantification. However, functional images provide little information on the structure of tissues and organs, which makes the localization of lesions difficult. Image fusion techniques can be exploited to map the functional images to structural images, such as X-ray computed tomography (CT), to support target identification and to facilitate the interpretation of PET or SPECT studies. Furthermore, the mapping of two functional images of SPECT and PET on a structural CT image can be beneficial for those in vivo studies that require two biological processes to be monitored simultaneously. This paper proposes an automated method for registering PET, CT, and SPECT images for small animals. A calibration phantom and a holder were used to determine the relationship among three-dimensional fields of view of various modalities. The holder was arranged in fixed positions on the couches of the scanners, and the spatial transformation matrix between the modalities was held unchanged. As long as objects were scanned together with the holder, the predetermined matrix could register the acquired tomograms from different modalities, independently of the imaged objects. In this work, the PET scan was performed by Concordes microPET R4 scanner, and the SPECT and CT data were obtained using the Gamma Medicas X-SPECT/CT system. Fusion studies on phantoms and animals have been successfully performed using this method. For microPET-CT fusion, the maximum registration errors were 0.21 mm /spl plusmn/ 0.14 mm, 0.26 mm /spl plusmn/ 0.14 mm, and 0.45 mm /spl plusmn/ 0.34 mm in the X (right-left), Y (upper lower), and Z (rostral-caudal) directions, respectively; for the microPET-SPECT fusion, they were 0.24 mm /spl plusmn/ 0.14 mm, 0.28 mm /spl plusmn/ 0.15 mm, and 0.54 mm /spl plusmn/ 0.35 mm in the X, Y, and Z directions, respectively. The results indicate that this simple method can be used in routine fusion studies.

A novel image quality index using Moran I statistics

Measurement of image quality is very important for various applications such as image compression, restoration and enhancement. Conventional methods (e.g., mean squared error; MSE) use error summation to measure quality change pixel by pixel and do not correlate well with subjective quality measurement. This is due to the fact that human eyes extract structural information from the viewing field. In this study a new quality index using a Moran I statistics is proposed. The Moran statistic that measures the sharpness from a local area is a good index of quality as most image processing techniques alter the smoothness of the image. Preliminary results show that the new quality index outperforms the MSE significantly under various types of image distortions.

Quality of compressed medical images.

Previous studies have shown that Joint Photographic Experts Group (JPEG) 2000 compression is better than JPEG at higher compression ratio levels. However, some findings revealed that this is not valid at lower levels. In this study, the qualities of compressed medical images in these ratio areas (∼20), including computed radiography, computed tomography head and body, mammographic, and magnetic resonance T1 and T2 images, were estimated using both a pixel-based (peak signal to noise ratio) and two 8 × 8 window-based [Q index and Moran peak ratio (MPR)] metrics. To diminish the effects of blocking artifacts from JPEG, jump windows were used in both window-based metrics. Comparing the image quality indices between jump and sliding windows, the results showed that blocking artifacts were produced from JPEG compression, even at low compression ratios. However, even after the blocking artifacts were omitted in JPEG compressed images, JPEG2000 outperformed JPEG at low compression levels. We found in this study that the image contrast and the average gray level play important roles in image compression and quality evaluation. There were drawbacks in all metrics that we used. In the future, the image gray level and contrast effect should be considered in developing new objective metrics.

Quality Degradation in Lossy Wavelet Image Compression

The objective of this study was to develop a method for measuring quality degradation in lossy wavelet image compression. Quality degradation is due to denoising and edge blurring effects that cause smoothness in the compressed image. The peak Moran z histogram ratio between the reconstructed and original images is used as an index for degradation after image compression. The Moran test is applied to images randomly selected from each medical modality, computerized tomography, magnetic resonance imaging, and computed radiography and compressed using the wavelet compression at various levels. The relationship between the quality degradation and compression ratio for each image modality agrees with previous reports that showed a preference for mildly compressed images. Preliminary results show that the peak Moran z histogram ratio can be used to quantify the quality degradation in lossy image compression. The potential for this method is applications for determining the optimal compression ratio (the maximized compression without seriously degrading image quality) of an image for teleradiology.

Prediction of Nodal Metastasis in Head and Neck Cancer Using a 3T MRI ADC Map

BACKGROUND AND PURPOSE: The detection of cervical nodal metastases is important for the prognosis and treatment of head and neck tumors. The purpose of this study was to assess the ability of ADC values at 3T to distinguish malignant from benign lymph nodes. MATERIALS AND METHODS: From July 2009 to June 2010, twenty-two patients (21 men and 1 woman; mean age, 49.8 ± 9.5 years; age range, 28–66 years) scheduled for surgical treatment of biopsy-proved head and neck cancer were prospectively and consecutively enrolled in this study. All patients were scanned on a 3T imaging unit (Verio) by using a 12-channel head coil combined with a 4-channel neck coil. Histologic findings were the reference standard for the diagnosis of lymph node metastasis. RESULTS: The ADC values derived from the signal intensity averaged across images obtained with b-values of 0 and 800 s/mm2 were 1.086 ± 0.222 × 10−3 mm2/s for benign lymph nodes and 0.705 ± 0.118 × 10−3 mm2/s for malignant lymph nodes (P < .0001). When an ADC value of 0.851 × 10−3 mm2/s was used as a threshold value for differentiating benign from malignant lymph nodes, the best results were obtained with an accuracy of 91.0%, sensitivity of 91.3%, and specificity of 91.1%. CONCLUSIONS: The ADC value is a sensitive and specific parameter that can help to differentiate malignant from benign lymph nodes.

Beam Hardening Correction for Computed Tomography Images Using a Postreconstruction Method and Equivalent Tisssue Concept

A postreconstruction method for correcting the beam-hardening artifacts in computed tomography (CT) images is proposed. This method does not require x-ray spectrum measurement. The authors assumed that a pixel in a CT image can be decomposed into equivalent tissue percentages, depending on its CT number. A scout view of the step wedges made of these equivalent tissues was performed to obtain a beam-hardening correction curve for each tissue. Projecting through the CT image from various angles generated simulated projection data and the total thickness of each tissue along the ray. The correction term was estimated using the tissue thickness traveled by the ray, and this term was then added to its corresponding projection data. A second reconstruction using the corrected projection data yielded a beam-hardening corrected image. The preliminary results show that this method reduces beam hardening artifacts by 14% for aluminum and increased the object contrast by 18% near the aluminum-water boundary. The variation in CT numbers at different locations were reduced, and the aluminum CT number also was restored.

Shape-based grey-level image interpolation

The three-dimensional (3D) object data obtained from a CT scanner usually have unequal sampling frequencies in the x-, y- and z-directions. Generally, the 3D data are first interpolated between slices to obtain isotropic resolution, reconstructed, then operated on using object extraction and display algorithms. The traditional grey-level interpolation introduces a layer of intermediate substance and is not suitable for objects that are very different from the opposite background. The shape-based interpolation method transfers a pixel location to a parameter related to the object shape and the interpolation is performed on that parameter. This process is able to achieve a better interpolation but its application is limited to binary images only. In this paper, we present an improved shape-based interpolation method for grey-level images. The new method uses a polygon to approximate the object shape and performs the interpolation using polygon vertices as references. The binary images representing the shape of the object were first generated via image segmentation on the source images. The target object binary image was then created using regular shape-based interpolation. The polygon enclosing the object for each slice can be generated from the shape of that slice. We determined the relative location in the source slices of each pixel inside the target polygon using the vertices of a polygon as the reference. The target slice grey-level was interpolated from the corresponding source image pixels. The image quality of this interpolation method is better and the mean squared difference is smaller than with traditional grey-level interpolation.

A Blurring Index for Medical Images

This study was undertaken to investigate a useful image blurring index. This work is based on our previously developed method, the Moran peak ratio. Medical images are often deteriorated by noise or blurring. Image processing techniques are used to eliminate these two factors. The denoising process may improve image visibility with a trade-off of edge blurring and may introduce undesirable effects in an image. These effects also exist in images reconstructed using the lossy image compression technique. Blurring and degradation in image quality increases with an increase in the lossy image compression ratio. Objective image quality metrics [e.g., normalized mean square error (NMSE)] currently do not provide spatial information about image blurring. In this article, the Moran peak ratio is proposed for quantitative measurement of blurring in medical images. We show that the quantity of image blurring is dependent upon the ratio between the processed peak of Morans Z histogram and the original image. The peak ratio of Morans Z histogram can be used to quantify the degree of image blurring. This method produces better results than the standard gray level distribution deviation. The proposed method can also be used to discern blurriness in an image using different image compression algorithms.

Feasibility study of using PEImager scanner for positron emission mammography

The purpose of this work is to study the feasibility of using PEImager scanner for positron emission mammography (PEM). PEM can be performed by using two opposite detectors. The two-detector positron projection imaging has less depth information, because of the limited number of line of responses (LORs). In this work, an iterative back projection algorithm is employed for reconstruction of projection data. Although the number of LORs are limited, the locations and sizes of hot spots in breast phantom still can be determined from the reconstructed images.

Development of an Adjustable Model Breast for Mammographic Dosimetry Assessment in Taiwanese Women

OBJECTIVE The currently used model breast for mammographic dosimetry assessment lacks the flexibility to change dimensions. The aim of this study was to develop an adjustable model breast for mammographic dosimetry assessment of Taiwanese women. MATERIALS AND METHODS A retrospective review of 4226 craniocaudal (CC) views was conducted. The geometry of the model breast was defined as a semielliptical cylinder. Breast parameters, including compressed breast thickness, chest wall-to-nipple distance, compressed breast width, and percentage glandular content, were measured and analyzed. To validate the adjustable model breast, 44 mammograms were obtained. The expected values from the adjustable model breast were compared with the measured values. RESULTS The average values of compressed breast thickness, chest wall-to-nipple distance, compressed breast width, and percentage glandular content of the women studied were 4.1 cm, 6.9 cm, 16.9 cm, and 54%, respectively. Variations of chest wall-to-nipple distance, compressed breast width, and percentage glandular content can be expressed as functions of compressed breast thickness, and the adjustable model breast developed was based on compressed breast thickness. The average area of the CC view obtained is a factor of 0.81 lower than that defined by the American College of Radiology protocol. For validation, the difference in average values between the expected and measured did not exceed 0.5 cm in breast dimensions and 6% in percentage glandular content. CONCLUSION Compressed breast thickness is useful for quantifying dimensions and percentage glandular content of a model breast. The adjustable model breast developed in this study can offer greater flexibility in the determination of breast dimensions for mammographic dosimetry assessment of Taiwanese women.