99m-Tc TRODAT Single-Photon Emission Computerized Tomography Scan Image Compression using Singular Value Decomposition.

Introduction: The objective of the study was to compress 99m-Tc TRODAT single-photon emission computerized tomography (SPECT) scan image using Singular Value Decomposition (SVD) into an acceptable compressed image and then calculate the compression factor. Materials and Methods: The SVD of every image from the image dataset of 2256 images (of forty-eight 99m-Tc TRODAT SPECT studies [48 studies X 47 trans-axial images = 2256 trans-axial images]) was computed and after truncating singular values smaller than a threshold, the compressed image was reconstructed. The SVD computation time and percentage compression achieved were calculated for each image. Two nuclear medicine physicians visually compared compressed image with its original image, and labeled it as either acceptable or unacceptable. Compressed image having loss of clinical details or presence of compression artifact was labeled unacceptable. The quality of compressed image was also assessed objectively using the following image quality metrics: Error, structural similarity (SSIM), brightness, global contrast factor (GCF), contrast per pixel (CPP), and blur. We also compared the TRODAT uptake in basal ganglia estimated from the compressed image and original image. Results: Nuclear Medicine Physician labeled each image acceptable, as they found compressed image identical to its original image. The values of brightness, GCF, CPP, and blur metrics show that compressed images are less noisy, brighter, and sharper than its original image. The median values of error (0.0006) and SSIM (0.93) indicate that the compressed images were approximately identical to its original image. In 39 out of 48 studies, the percentage difference in TRODAT uptake (in basal ganglia from compressed and original image) was negligible (approximately equal to zero). In remaining 9 studies, the maximum percentage difference was 13%. The SVD computation time and percentage compression achieved for a TRODAT study were 0.17398 s and up to 54.61%, respectively. Conclusions: The compression factor up to 54.61% was achieved during 99m-Tc TRODAT SPECT scan image compression using SVD, for an acceptable compressed image.

Estimating Center of Rotation of Single-Photon Emission Computerized Tomography Projection Images Using MATLAB for Standardization and Calibration.

Objective: The objective of the study was to develop a Personal Computer (PC) based tool to estimate the center of rotation (COR) offsets from COR projection datasets using methods mentioned in IAEA-TECDOC-602. Materials and Methods: Twenty-four COR studies were acquired on Discovery NM 630 Dual head gamma camera fitted with parallel hole collimator, and COR offsets were estimated with the software available at the terminal for processing a COR study. These COR projection images were exported in DICOM. A MATLAB script (software program) was written to estimate COR offset using Method A (using opposite pair of projections) and Method B (using curve fitting method) as mentioned in IAEA-TECDOC-602. Our program read the COR study (in DICOM) and estimated COR offsets using Method A and Method B. The accuracy of the program was verified using simulated projection dataset of a point source object acquired at 6° interval in the range of 0°-360° angle. Bland Altman plot was used for analyzing the agreement between the COR offsets estimated using (1) Method A and Method B mentioned in IAEA-TECDOC-602, and (2) Our program and vendor program available at Discovery NM 630 acquisition terminal. Results: On simulated data, offset from center of gravity (COG) in X direction (COGX) and COG in Y direction (COGY) estimated using Method A was constant (same) at each pair of angles while using Method B, it was found to be in the range (-2 × 10-10, 1 × 10-10) which is negligible. Most of the differences (23 out of 24) between the result of Method A and Method B, and between the results of our program and vendor program was found to be within 95% confidence interval (mean ± 1.96 standard deviation). Conclusions: Our PC-based tool to estimate COR offsets from COR projection datasets using methods mentioned in IAEA-TECDOC-602 was found to be accurate and provides results in agreement with vendor's program. It can be used as an independent tool to estimate COR offset for standardization and calibration purposes.

99m-Tc MDP Bone Scan Image Enhancement using Pipeline Application of Dynamic Stochastic Resonance Algorithm and Block-Matching 3D Filter.

Introduction: In this pilot study, we have proposed and evaluated pipelined application of the dynamic stochastic resonance (DSR) algorithm and block-matching 3D (BM3D) filter for the enhancement of nuclear medicine images. The enhanced images out of the pipeline were compared with the corresponding enhanced images obtained using individual applications of DSR and BM3D algorithm. Materials and Methods: Twenty 99m-Tc MDP bone scan images acquired on SymbiaT6 SPECT/CT gamma camera system fitted with low-energy high-resolution collimators were exported in DICOM format to a personal computer and converted into PNG format. These PNG images were processed using the proposed algorithm in MATLAB. Two nuclear medicine physicians visually compared each input and its corresponding three enhanced images to select the best-enhanced image. The image quality metrics (Brightness, Global Contrast Factor (GCF), Contrast per pixel (CPP), and Blur) were used to assess the image quality objectively. The Wilcoxon signed test was applied to find a statistically significant difference in Brightness, GCF, CPP, and Blur of enhanced and its input images at a level of significance. Results: Images enhanced using the pipelined application of SR and BM3D were selected as the best images by both nuclear medicine physicians. Based on Brightness, Global Contrast Factor (GCF), CPP, and Blur, the image quality of our proposed pipeline was significantly better than enhanced images obtained using individual applications of DSR and BM3D algorithm. The proposed method was found to be very successful in enhancing details in the low count region of input images. The enhanced images were bright, smooth, and had better target-to-background ratio compared to input images. Conclusion: The pipelined application of DSR and BM3D algorithm produced enhancement in nuclear medicine images having following characteristics: bright, smooth, better target-to-background ratio, and improved visibility of details in the low count regions of the input image, as compared to individual enhancements by application of DSR or BM3D algorithm.

Near-lossless Compression of Tc-99 m DMSA Scan Images Using Discrete Cosine Transformation.

Aim and Objective: The objective of this study was to optimize the threshold for discrete cosine transform (DCT) coefficients for near-lossless compression of Tc-99 m Dimercaptosuccinic acid (DMSA) scan images using discrete cosine transformation. Materials and Methods: Two nuclear medicine (NM) Physicians after reviewing several Tc-99 m DMSA scan images provided 242 Tc-99 m DMSA scan images that had scar. These Digital imaging and communication in medicine (DICOM) images were converted in the Portable Network Graphics (PNG) format. DCT was applied on these PNG images, which resulted in DCT coefficients corresponding to each pixel of the image. Four different thresholds equal to 5, 10, 15, and 20 were applied and then inverse discrete cosine transformation was applied to get the compressed Tc-99 m DMSA scan images. Compression factor was calculated as the ratio of the number of nonzero elements after thresholding DCT coefficients to the number of nonzero elements before thresholding DCT coefficients. Two NM physicians who had provided the input images visually compared the compressed images with its input image, and categorized the compressed images as either acceptable or unacceptable. The quality of compressed images was also assessed objectively using the following eight image quality metrics: perception-based image quality evaluator, structural similarity index measure (SSIM), multiSSIM, feature similarity indexing method, blur, global contrast factor, contrast per pixel, and brightness. Pairwise Wilcoxon signed-rank sum tests were applied to find the statistically significant difference between the value of image quality metrics of the compressed images obtained at different thresholds and the value of the image quality metrics of its input images at the level of significance = 0.05. Results: At threshold 5, (1) all compressed images (242 out of 242 Tc-99 m DMSA scan images) were acceptable to both the NM Physicians, (2) Compressed image looks identical to its original image and no loss of clinical details was noticed in compressed images, (3) Up to 96.65% compression (average compression: 82.92%) was observed, and (4) Result of objective assessment supported the visual assessment. The quality of compressed images at thresholds 10, 15, and 20 was significantly better than that of input images at P < 0.0001. However, the number of unacceptable compressed images at thresholds 10, 15, and 20 was 6, 38, and 70, respectively. Conclusions: Up to 96.65%, near-losses compression of Tc-99 m DMSA images was found using DCT by thresholding DCT coefficients at a threshold value equal to 5.

Renal Dynamic Image Compression using Singular Value Decomposition.

Aims and Objective: The objective of this study was to evaluate the compression of renal dynamic (RD) study images using singular value decomposition (SVD) technique. Materials and Methods: 4600 images of fifty RD study were compressed by using SVD technique. Two Nuclear Medicine (NM) Physicians compared compressed images with their corresponding input images and labeled these as acceptable or unacceptable. The SVD computation time and compression ratio were calculated for each image. The quality of compressed image was also assessed objectively using the following image quality metrics: Error, structural similarity (SSIM), Brightness, global contrast factor, contrast per pixel (CPP), and blur. The error in split function (i.e., the error between split function calculated from compressed image and split function calculated from original image) was computed for every RD study. Wilcoxon signed-rank test with continuity correction was applied to find a statistically significant difference in ROI counts on compressed and original image at. Results: As per NM physicians compressed image frames look identical to the original image frames. Objectively the compressed images were brighter, less noisy, and also have better CPP. Based on the visual assessment, time activity curve generated from original and compressed image frames was identical. There was insignificant difference of ROI counts between the input and compressed image frames of 99m-Tc LLEC RD Study. There was no significant difference between the split renal function estimated from original and its compressed RD study. The average SSIM value, average compression ratio, and SVD computation time were found to be 0.7521, 1.475, and 0.1200. Conclusions: Visually, compressed image was identical to the original image. The percentage compression achieved was found to be up to 58% (compression factor achieved = 1.57). The SVD computation time was approximately 0.12 s for 64 × 64 matrix size image frame.

Contrast Enhancement of Scintigraphic Image Using Fuzzy Intensification.

Introduction: The objective of this study was to see the effect of fuzzy intensification (INT) operator on enhancement of scintigraphic image. Materials and Methods: Nuclear medicine physician (NMP) provided 25 scintigraphic images that required enhancement. The image pixels value was converted into fuzzy plane and was subjected to contrast INT operator with parameters of INT operator i.e., cross-over = 0.5 and number of iterations = 1 and 2. The enhanced image was again brought back into spatial domain (de-fuzzification) whose intensity value was in the range 0-255. NMP compared the enhanced image with its input image and labeled it as acceptable or unacceptable. The quality of enhanced image was also accessed objectively using four different image metrics namely: Entropy, edge content, absolute mean brightness error and saturation metrics. Results: Most of the enhanced images (18 out of 25 images) obtained at cross-over = 0.5 and number of iterations = 1 are acceptable and found to have overall better contrast compared to the corresponding input image. Four images (two brain positron emission tomography scan and two I-131 scan) obtained at cross-over = 0.5 and with iteration = 2 are acceptable. Three input images (one dimercaptosuccinic acid (DMSA), one I-131 and one I-131- metaiodo-benzyl-guanidine (MIBG) scan) were better than their enhanced images. Conclusions: The enhancement produced by fuzzy INT operator was encouraging. Majority of enhanced images were acceptable at cross-over = 0.5 and number iterations = 1.

Compression of 99mTc Methylene Diphosphonate Bone Scan Images using Discrete Cosine Transformation.

Aims and Objectives: The objective of this study was to find the optimum value of threshold for compression of 99mTc-methylene diphosphonate (MDP) bone scan images using discrete cosine transformation (DCT). Materials and Methods: DCT was applied to 51 99mTc-MDP bone scan images and then the image of logarithmic value of DCT coefficients was inspected to determine the threshold. After inspecting the number of images of DCT coefficients, we estimated the appropriate value of the threshold to be 10. After the application of threshold = 10, compressed image was reconstructed by applying the inverse DCT. Compression factor was calculated by dividing the nonzero element after thresholding to the nonzero element before thresholding DCT coefficients. Nuclear medicine physicians compared the compressed images with its input images and labeled them as acceptable or unacceptable. During comparison of input and compressed images, we considered points such as smoothening, blocking artifacts, body contour, gap between closely placed lesions, and detectability of lesion. Results: Forty-four compressed images (out of 51 images) obtained at threshold 10 were acceptable to Nuclear Medicine Physician (NMP). Compressed images were less noisy compared to its input image. Compression factor was found to be 13.03 ± (minimum = 2.71, maximum = 42.92). Conclusion: The optimum value of threshold for compression of 99mTc-MDP bone scan images was found to be 10, and the average compression factor achieved was equal to 13.03 (92.30%).

Optimum Value of Scale and Threshold for Compression of 99mTc-MDP Bone Scan Images using Haar Wavelet Transform.

Introduction: Wavelet transforms of an image result in set of wavelet coefficients. Thresholding eliminates insignificant coefficients while retaining the significant ones (resulting in matrix having few nonzero elements that need to be stored). The compressed image is reconstructed by applying inverse wavelet transform. The quality of compressed image deteriorates with increase in compression. Hence, finding optimum value of scale and threshold is a challenging task. The objective of the study was to find the optimum value of scale and threshold for compressing 99mTc-methylene diphosphonate (99 mTc-MDP) bone scan images using Haar wavelet transform. Materials and Methods: Haar wavelet transform at scale 1-8 was applied on 106 99 mTc-MDP whole-body bone scan images, and wavelet coefficients were threshold at 90, 95, 97, and 99 percentiles, followed by inverse wavelet transform to get 3392 compressed images. Nuclear medicine physician (NMP) compared compressed image with its corresponding input to label it as acceptable or unacceptable. The values of scale and threshold that resulted in majority of acceptable images were considered to be optimum. The quality of compressed image was also evaluated using perception image quality evaluator (PIQE) image quality metrics. Compression ratio was calculated by dividing the number of nonzero elements after thresholding wavelet coefficients by the number of nonzero elements in Haar decomposed matrix. Results: NMP found quality of compressed images (obtained at scale 2 and 90 percentile threshold) identical to the quality of the corresponding input images. As per PIQE score, quality of compressed images was perceptually better than that of the corresponding input images. Conclusions: The optimum values of scale and threshold were determined to be 2 and 90 percentiles, respectively.

Comparison of noise estimation methods used in denoising 99mTc-sestamibi parathyroid images using wavelet transform.

The objective of this study was to compare the performance of variance, median absolute deviation, and the square of median absolute deviation methods of noise estimation in denoising of 99mTc-sestamibi parathyroid images using wavelet transform. Sixty-eight 99mTc-sestamibi parathyroid images including 33 images acquired at zoom 1.0 and 35 acquired at zoom 2.0 were denoised using the wavethresh package in R. The image decomposition and reconstruction method discrete wavelet transform, wavelet filter db4, shrinkage method hard, and thresholding policy universal were used. The noise estimation in the process was made using var, mad and madmad functions, which use variance, mean absolute deviation, and the square of mean absolute deviation, respectively. The quality of denoised images was assessed both qualitatively and quantitatively. A nonparametric two-sample Kolmogorov-Smirnov test was applied to find whether the difference in image quality produced by these three noise estimation methods was significant at 95% confidence. Noise estimation using madmad function produced the best quality denoised image. Further, the quality of the denoised image using madmad function was significantly better than the quality of the denoised image obtained with var or mad function (P = 1). The estimation of noise using madmad functions in wavelet transforms provides the best-denoised image for both zoom 1.0 and zoom 2.0 99mTc-sestamibi parathyroid images.

Improving technetium-99m methylene diphosphonate bone scan images using histogram specification technique.

In this study, we have proposed and validated that histogram of a good-quality bone scan image can enhance a poor-quality bone scan image. The histograms of two good-quality technetium-99m methyl diphosphonate bone scan images IA and IB recommended by nuclear medicine physicians (NMPs) were used to enhance 87 poor-quality bone scan images. Processed images and their corresponding input images were compared visually by two NMPs with scoring and also quantitatively using entropy, Structural similarity index measure, edge-based contrast measure, and absolute brightness mean error. Barnard's unconditional test was applied with a null hypothesis that the histogram of both IA and IB produces similar output image at α =0.05. The mean values of quantitative metrices of the processed images obtained using IA and IB were compared using Kolmogorov-Smirnov test. Histogram of a good-quality bone scan image can enhance a poor-quality bone scan image. Visually, histogram of IB improved statistically significantly higher proportion (P < 0.0001) of images (86/87) as compared to histogram of IA (51/87). Quantitatively, IB performed better than IA, and the Chi-square distance of input and IB was smaller than that of IA. In addition, a statistically significant (P < 0.05) difference in all the quantitative metrics among the outputs obtained using IA and IB was observed. In our study, reference histogram of good-quality bone scan images transformed the majority of poor-quality bone scan images (98.85%) into visually good-quality images acceptable by NMPs.

Role of an Intensity-Transformation Function in Enhancement of Bone Scintigraphy Images.

Bone scintigraphy images might exceed the dynamic range (the ratio between the highest and the lowest displayable brightness) of the monitor. In such a case, a high-intensity area accompanied by loss of detail in other structures in the displayed image make the clinical interpretation challenging. We have investigated the role of an intensity-transformation (IT) function in enhancement of these types of images. Methods: Forty high-dynamic-range bone scintigraphy images were processed using an IT function. The IT function has 2 parameters: threshold and slope. With the threshold kept equal to the mean count of the image, the slope was varied from 1 to 20. A software program developed in-house was used to process the images. Twenty output images corresponding to one input image were visually inspected by 2 experienced nuclear medicine physicians to select images of diagnostic quality, and from their selection was determined the standardized slope that produced the maximum number of diagnostic images. The 2 physicians also scored the quality of the input and output images (at the standardized slope) on a scale of 1-5. The Student t test was used to determine the significance of differences in mean score between the input and output images at an α significance level of 0.05. Results: Application of the IT function with standardized parameters significantly improved the quality of high-dynamic-range bone scintigraphy images (P < 0.001, with α = 0.05). A slope of 8 maximized the number of diagnostic images. Conclusion: The IT function has a significant role in enhancing high-dynamic-range bone scintigraphy images.

Calculating Gamma Camera Uniformity Parameters: Beyond the Vendor-specific Protocol.

OBJECTIVES: The aim of this study was to develop and verify a personal computer-based software tool for calculating uniformity indices of gamma camera. MATERIALS AND METHODS: The program was developed in MATLAB R2013b under Microsoft Windows operating system. Noise-less digital phantoms with known uniformity parameters were used to verify the accuracy of the program. Two hundred and forty-four Co-57 flood source images were acquired on Symbia T6 and Discovery nuclear medicine/computed tomography 670. The uniformity indices of these images were determined with their respective vendor's software and also by the tool developed. Bland-Altman plots were used for measuring the agreements between the developed program and the vendor's program for the calculation of uniformity indices. RESULTS: The tool for calculating uniformity indices was found to be accurate. Uniformity indices measured with the tool revealed a very good correlation with vendor's software based on Bland-Altman analysis, as almost all measurements were within the ±2 standard deviation range. CONCLUSION: The software tool for calculation of uniformity indices is accurate, and the uniformity indices calculated by it are in agreement with uniformity indices calculated by the vendor's software.

Development of a Simple Image Processing Application that Makes Abdominopelvic Tumor Visible on Positron Emission Tomography/Computed Tomography Image.

INTRODUCTION: In this study, we have developed a simple image processing application in MATLAB that uses suprathreshold stochastic resonance (SSR) and helps the user to visualize abdominopelvic tumor on the exported prediuretic positron emission tomography/computed tomography (PET/CT) images. METHODS: A brainstorming session was conducted for requirement analysis for the program. It was decided that program should load the screen captured PET/CT images and then produces output images in a window with a slider control that should enable the user to view the best image that visualizes the tumor, if present. The program was implemented on personal computer using Microsoft Windows and MATLAB R2013b. RESULTS: The program has option for the user to select the input image. For the selected image, it displays output images generated using SSR in a separate window having a slider control. The slider control enables the user to view images and select one which seems to provide the best visualization of the area(s) of interest. CONCLUSION: The developed application enables the user to select, process, and view output images in the process of utilizing SSR to detect the presence of abdominopelvic tumor on prediuretic PET/CT image.

Development and Validation of the Suprathreshold Stochastic Resonance-Based Image Processing Method for the Detection of Abdomino-pelvic Tumor on PET/CT Scans.

PURPOSE: The detection of abdomino-pelvic tumors embedded in or nearby radioactive urine containing 18F-FDG activity is a challenging task on PET/CT scan. In this study, we propose and validate the suprathreshold stochastic resonance-based image processing method for the detection of these tumors. METHODS: The method consists of the addition of noise to the input image, and then thresholding it that creates one frame of intermediate image. One hundred such frames were generated and averaged to get the final image. The method was implemented using MATLAB R2013b on a personal computer. Noisy image was generated using random Poisson variates corresponding to each pixel of the input image. In order to verify the method, 30 sets of pre-diuretic and its corresponding post-diuretic PET/CT scan images (25 tumor images and 5 control images with no tumor) were included. For each sets of pre-diuretic image (input image), 26 images (at threshold values equal to mean counts multiplied by a constant factor ranging from 1.0 to 2.6 with increment step of 0.1) were created and visually inspected, and the image that most closely matched with the gold standard (corresponding post-diuretic image) was selected as the final output image. These images were further evaluated by two nuclear medicine physicians. RESULTS: In 22 out of 25 images, tumor was successfully detected. In five control images, no false positives were reported. Thus, the empirical probability of detection of abdomino-pelvic tumors evaluates to 0.88. CONCLUSION: The proposed method was able to detect abdomino-pelvic tumors on pre-diuretic PET/CT scan with a high probability of success and no false positives.

Investigating the Role of Global Histogram Equalization Technique for 99mTechnetium-Methylene diphosphonate Bone Scan Image Enhancement.

PURPOSE OF THE STUDY: 99mTechnetium-methylene diphosphonate (99mTc-MDP) bone scan images have limited number of counts per pixel, and hence, they have inferior image quality compared to X-rays. Theoretically, global histogram equalization (GHE) technique can improve the contrast of a given image though practical benefits of doing so have only limited acceptance. In this study, we have investigated the effect of GHE technique for 99mTc-MDP-bone scan images. MATERIALS AND METHODS: A set of 89 low contrast 99mTc-MDP whole-body bone scan images were included in this study. These images were acquired with parallel hole collimation on Symbia E gamma camera. The images were then processed with histogram equalization technique. The image quality of input and processed images were reviewed by two nuclear medicine physicians on a 5-point scale where score of 1 is for very poor and 5 is for the best image quality. A statistical test was applied to find the significance of difference between the mean scores assigned to input and processed images. RESULTS: This technique improves the contrast of the images; however, oversaturation was noticed in the processed images. Student's t-test was applied, and a statistically significant difference in the input and processed image quality was found at P < 0.001 (with α = 0.05). However, further improvement in image quality is needed as per requirements of nuclear medicine physicians. CONCLUSION: GHE techniques can be used on low contrast bone scan images. In some of the cases, a histogram equalization technique in combination with some other postprocessing technique is useful.

A technique for automatically extracting useful field of view and central field of view images.

INTRODUCTION: It is essential to ensure the uniform response of the single photon emission computed tomography gamma camera system before using it for the clinical studies by exposing it to uniform flood source. Vendor specific acquisition and processing protocol provide for studying flood source images along with the quantitative uniformity parameters such as integral and differential uniformity. However, a significant difficulty is that the time required to acquire a flood source image varies from 10 to 35 min depending both on the activity of Cobalt-57 flood source and the pre specified counts in the vendors protocol (usually 4000K-10,000K counts). In case the acquired total counts are less than the total prespecified counts, and then the vendor's uniformity processing protocol does not precede with the computation of the quantitative uniformity parameters. In this study, we have developed and verified a technique for reading the flood source image, remove unwanted information, and automatically extract and save the useful field of view and central field of view images for the calculation of the uniformity parameters. MATERIALS AND METHODS: This was implemented using MATLAB R2013b running on Ubuntu Operating system and was verified by subjecting it to the simulated and real flood sources images. RESULTS: The accuracy of the technique was found to be encouraging, especially in view of practical difficulties with vendor-specific protocols. CONCLUSION: It may be used as a preprocessing step while calculating uniformity parameters of the gamma camera in lesser time with fewer constraints.