Cheng-Ting Shih

Improvements on a patient-specific dose estimation system in nuclear medicine examination

The purpose of this paper is to develop a patient-specific dose estimation system in nuclear medicine examination. A dose deposition routine to store the deposited energy of the photons during their flights was embedded in the widely used SimSET Monte Carlo code and a user-friendly interface for reading PET and CT images was developed. Dose calculated on ORNL phantom was used to validate the accuracy of this system. The ratios of S value for (99m)Tc, (18)F and (131)I computed by this system to those obtained with OLINDA for various organs were ranged from 0.93 to 1.18, which were comparable to that obtained from MCNPX2.6 code (0.88-1.22). Our system developed provides opportunity for tumor dose estimation which cannot be known from the MIRD. The radiation dose can provide useful information in the amount of radioisotopes to be administered in radioimmunotherapy.

A Novel Two-Compartment Model for Calculating Bone Volume Fractions and Bone Mineral Densities From Computed Tomography Images

Osteoporosis is a disease characterized by a degradation of bone structures. Various methods have been developed to diagnose osteoporosis by measuring bone mineral density (BMD) of patients. However, BMDs from these methods were not equivalent and were incomparable. In addition, partial volume effect introduces errors in estimating bone volume from computed tomography (CT) images using image segmentation. In this study, a two-compartment model (TCM) was proposed to calculate bone volume fraction (BV/TV) and BMD from CT images. The TCM considers bones to be composed of two sub-materials. Various equivalent BV/TV and BMD can be calculated by applying corresponding sub-material pairs in the TCM. In contrast to image segmentation, the TCM prevented the influence of the partial volume effect by calculating the volume percentage of sub-material in each image voxel. Validations of the TCM were performed using bone-equivalent uniform phantoms, a 3D-printed trabecular-structural phantom, a temporal bone flap, and abdominal CT images. By using the TCM, the calculated BV/TVs of the uniform phantoms were within percent errors of ±2%; the percent errors of the structural volumes with various CT slice thickness were below 9%; the volume of the temporal bone flap was close to that from micro-CT images with a percent error of 4.1%. No significant difference (p >0.01) was found between the areal BMD of lumbar vertebrae calculated using the TCM and measured using dual-energy X-ray absorptiometry. In conclusion, the proposed TCM could be applied to diagnose osteoporosis, while providing a basis for comparing various measurement methods.

Noise reduction of low-dose computed tomography using the multi-resolution total variation minimization algorithm

Computed tomography (CT) has become a popular tool in radiologic diagnosis due to the ability of obtaining highresolution anatomical images. However, radiation doses to patients are substantial and can increase the risk of cancer incidence. Although lowering the tube current is a direct way to reduce absorbed doses, insufficient photon numbers can cause severe quantum mottle and subsequently degrade the diagnostic value of CT images. In this study, we proposed an algorithm for noise reduction of low-dose computed tomography (LDCT) based on the multiresolution total variation minimization (MRTV) method. The discrete wavelet transform was used to decompose the CT image into high- and lowfrequency wavelet coefficients. The total variation minimization with suitable tuning parameters was then applied to reduce the variance among the wavelet coefficients. The noise-reduced image was reconstructed by the inverse wavelet transform. The results of the Shepp-Logan phantom added with Gaussian white noise showed that the noise was eliminated effectively and the SNR in the three compartments was increased from 2.04, 20.69 and 0.09 to 19.45, 187.77 and 0.27, respectively. In the CT image of the water phantom acquired with 50-mAs tube currents, the MRTV improved the smoothness of the water compartment. The average SNR was increased from 0.14 to 0.98, which is even better than the CT image acquired by 200 mAs. In the clinical head CT image with a tube current of 9.12 mAs, the MRTV successfully removed the severe noise in the parenchyma, and SNR was increased from 0.982 to 3.452 in average. In addition, the details of the septal structure of the sinus cavity were maintained. We conclude that the MRTV approach can effectively reduce the image noise caused by the tube current insufficiency, and thereby could improve the diagnostic value of LDCT images.

A Novel Method of Estimating Dose Responses for Polymer Gels Using Texture Analysis of Scanning Electron Microscopy Images

Polymer gels are regarded as a potential dosimeter for independent validation of absorbed doses in clinical radiotherapy. Several imaging modalities have been used to convert radiation-induced polymerization to absorbed doses from a macro-scale viewpoint. This study developed a novel dose conversion mechanism by texture analysis of scanning electron microscopy (SEM) images. The modified N-isopropyl-acrylamide (NIPAM) gels were prepared under normoxic conditions, and were administered radiation doses from 5 to 20 Gy. After freeze drying, the gel samples were sliced for SEM scanning with 50×, 500×, and 3500× magnifications. Four texture indices were calculated based on the gray level co-occurrence matrix (GLCM). The results showed that entropy and homogeneity were more suitable than contrast and energy as dose indices for higher linearity and sensitivity of the dose response curves. After parameter optimization, an R 2 value of 0.993 can be achieved for homogeneity using 500× magnified SEM images with 27 pixel offsets and no outlier exclusion. For dose verification, the percentage errors between the prescribed dose and the measured dose for 5, 10, 15, and 20 Gy were −7.60%, 5.80%, 2.53%, and −0.95%, respectively. We conclude that texture analysis can be applied to the SEM images of gel dosimeters to accurately convert micro-scale structural features to absorbed doses. The proposed method may extend the feasibility of applying gel dosimeters in the fields of diagnostic radiology and radiation protection.

Microscopic SEM Texture Analysis of NIPAM Gel Dosimeters

Polymer gel dosimeters have been proved as a useful tool in the verification of treatment planning for three-dimensional radiation therapy. The degree of radiation-induced polymerization (DP) is a function of absorbed dose, and it can be determined in macro-scale by several imaging modalities, such as magnetic resonance imaging (MRI), optical computed tomography, and computed tomography (CT). In this study, we determined the DP for the polymer gel dosimeter in micro-scale by texture analysis on scanning electron microscope (SEM) images. The irradiated n-NIPAM gels with absorbed doses of 5 to 20 Gy delivered by the linear accelerator were freeze-dried and sliced. The SEM images were acquired at magnification of 50×, 500 ×  and 3500× , respectively. Four image texture indices, including the entropy, contrast, energy and homogeneity, were estimated from the grey level co-occurrence matrix of each SEM image. The relationships between the radiation dose and texture indices were determined. The SEM images with 500× and 3500× magnification showed significant changes in morphological features of the irradiated gel. The r2 of the linear fitting from 3500× SEM images was 0.8194 (entropy), 0.6024 (contrast), 0.7681 (energy), and 0.9978 (homogeneity), respectively. We conclude that the texture analysis on SEM images is a feasible method to estimate the dose response of the polymer gel dosimeter.

Performance Enhancement of a Web-Based Picture Archiving and Communication System Using Commercial Off-the-Shelf Server Clusters

The rapid development of picture archiving and communication systems (PACSs) thoroughly changes the way of medical informatics communication and management. However, as the scale of a hospitals operations increases, the large amount of digital images transferred in the network inevitably decreases system efficiency. In this study, a server cluster consisting of two server nodes was constructed. Network load balancing (NLB), distributed file system (DFS), and structured query language (SQL) duplication services were installed. A total of 1 to 16 workstations were used to transfer computed radiography (CR), computed tomography (CT), and magnetic resonance (MR) images simultaneously to simulate the clinical situation. The average transmission rate (ATR) was analyzed between the cluster and noncluster servers. In the download scenario, the ATRs of CR, CT, and MR images increased by 44.3%, 56.6%, and 100.9%, respectively, when using the server cluster, whereas the ATRs increased by 23.0%, 39.2%, and 24.9% in the upload scenario. In the mix scenario, the transmission performance increased by 45.2% when using eight computer units. The fault tolerance mechanisms of the server cluster maintained the system availability and image integrity. The server cluster can improve the transmission efficiency while maintaining high reliability and continuous availability in a healthcare environment.

A novel adaptive discrete cosine transform-domain filter for gap-inpainting of high resolution PET scanners.

PURPOSE Several positron emission tomography (PET) scanners with special detector block arrangements have been developed in recent years to improve the resolution of PET images. However, the discontinuous detector blocks cause gaps in the sinogram. This study proposes an adaptive discrete cosine transform-based (aDCT) filter for gap-inpainting. METHODS The gap-corrupted sinogram was morphologically closed and subsequently converted to the DCT domain. A certain number of the largest coefficients in the DCT spectrum were identified to determine the low-frequency preservation region. The weighting factors for the remaining coefficients were determined by an exponential weighting function. The aDCT filter was constructed and applied to two digital phantoms and a simulated phantom introduced with various levels of noise. RESULTS For the Shepp-Logan head phantom, the aDCT filter filled the gaps effectively. For the Jaszczak phantom, no secondary artifacts were induced after aDCT filtering. The percent mean square error and mean structure similarity of the aDCT filter were superior to those of the DCT2 filter at all noise levels. For the simulated striatal dopamine innervation study, the aDCT filter recovered the shape of the striatum and restored the striatum to reference activity ratios to the ideal value. CONCLUSIONS The proposed aDCT filter can recover the missing gap data in the sinogram and improve the image quality and quantitative accuracy of PET images.

Physical Model-Based Contrast Enhancement of Computed Tomography Images: Contrast Enhancement of Computed Tomography

Computed tomography (CT) can rapidly provide high-resolution cross-section images for clinical diagnosis. The image contrast of the CT strongly influences the visibility of lesions in the images. However, the low material-dependent characteristic of the Compton scattering (CS) lowers the image contrast. In this study, a novel physical model-based method was proposed to enhance the contrast of CT images. At first, relationships between CT number and tissue parameters were determined using the CT images and elemental composition of tissue equivalent rods. Then, the CT images to be enhanced were converted to tissue parameter maps using pre-determined relationships. By using a classical parametric fit model, partial attenuation images with enhanced image contrast can be calculated. A phantom CT image and an abdominal CT image were used to evaluate the performance of the proposed method. For the phantom CT image, the image contrast between rods and background solid water were enhanced. For the abdominal CT image, the visibility of a low-attenuation lesion in the right lobe of the liver was improved. In conclusion, the proposed method could be applied in clinical diagnosis to improve the visibility of CT images.

Rapid deployment of a Monte Carlo simulation system using diskless remote boot in Linux in a PACS environment

The Monte Carlo (MC) technique has been widely used as the gold standard for interaction of radiation with matter in the fields of medical physics, radiation therapy, and nuclear medicine. However, MC simulation is time consuming and requires a lot of computational resources. Generally, a dedicated high performance computing cluster is use to improve efficiency, but it is costly and lacks of the ability to run routine errands in healthcare facilities. In this study, we proposed a method for rapid deployment of computing platform for MC simulation in the PACS environment using review workstations as computing nodes. The workstations were booted from the network and initialed a RAM disk as the boot sector. The simplified Linux operating system and the Monte Carlo N-Particle Transport Code Version 5 (MCNP5) were transferred from the DRBL (Diskless Remote Boot in Linux) server to each node automatically. The cluster computing environment can be established within four minutes. We compared a commercially available dedicated cluster with the DRBL cluster. The results showed that the commercial cluster had a slightly higher acceleration factor than the DRBL cluster. The simulation time of the commercial and the DRBL clusters for 2×108 particle histories was 37,151 and 40,021 sec, respectively. When the number of rendezvous increased to 20, the maximum time differences between both clusters were 95 and 85 sec for the megabit and the gigabit switches. We conclude that the DRBL cluster can be quickly deployed to the non-workloaded review workstations in the PACS. Thus, the MC technique could be broadly used to enhance the research capability of radiological sciences in healthcare facilities.