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.

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.

Denoising Tc-99m DMSA images using Denoising Convolutional Neural Network with comparison to a Block Matching Filter.

INTRODUCTION: A DnCNN for image denoising trained with natural images is available in MATLAB. For Tc-99m DMSA images, any loss of clinical details during the denoising process will have serious consequences since denoised image is to be used for diagnosis. The objective of the study was to find whether this pre-trained DnCNN can be used for denoising Tc-99m DMSA images and compare its performance with block matching 3D (BM3D) filter. MATERIALS AND METHODS: Two hundred forty-two Tc-99m DMSA images were denoised using BM3D filter (at sigma = 5, 10, 15, 20, and 25) and DnCNN. The original and denoised images were reviewed by two nuclear medicine physicians and also assessed objectively using the image quality metrics: SSIM, FSIM, MultiSSIM, PIQE, Blur, GCF, and Brightness. Wilcoxon signed-rank test was applied to find the statistically significant difference between the value of image quality metrics of the denoised images and the corresponding original images. RESULTS: Nuclear medicine physicians observed no loss of clinical information in DnCNN denoised image and superior image quality compared to its original and BM3D denoised images. Edges/boundaries of the scar were found to be well preserved, and doubtful scar became obvious in the denoised image. Objective assessment also showed that the quality of DnCNN denoised images was significantly better than that of original images at P -value <0.0001. CONCLUSION: The pre-trained DnCNN available with MATLAB Deep Learning Toolbox can be used for denoising Tc-99m DMSA images, and the performance of DnCNN was found to be superior in comparison with BM3D filter.

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.

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.

Vertebral and ribs enhancement in Tc-99m methylene diphosphonate bone scan image by blind deconvolution algorithm.

AIMS AND OBJECTIVES: The objective of the study was to restore Tc-99m methylene diphosphonate (MDP) bone scan image using blind deconvolution (BD) algorithm so that ribs, vertebrae, and lesions present in them become prominent. MATERIALS AND METHODS: Our study consists of retrospective data in which 356 Tc-99m MDP bone scan images (178 anterior and 178 posterior) were processed using dynamic stochastic resonance algorithm, block-matching 3D filter, and then restored using BD algorithm. Two nuclear medicine (NM) physicians compared restored image with its input image; they especially lookedfor: (a) improvement in lesions detectability, (b) artifacts if any, (c) deterioration in ribs and vertebra, and (d) contrast enhancement in adjacent vertebra and adjacent ribs. They selected one out of two (restored and input) images, which had better quality. The overall image quality was also assessed using the following image quality metrics: brightness, blur, global contrast factor, and contrast per pixel. The Wilcoxon signed-rank test was applied for finding significant difference between the value of image quality metrics of restored image and input image at level of significance alpha = 0.05. RESULTS: According to NM physicians, 80.3% (286 out of 356) of restored images were acceptable, whereas 19.6% (70 out of 356) were unacceptable. Ribs and vertebrae were prominent in 161 out of 178 posterior restored images. Lumbar vertebrae were enhanced and well differentiated from adjacent vertebrae in 125 out of 178 anterior restored images. The value of image quality metrics of restored and input images were found to be significantly different ( P -value < 0.0001). CONCLUSION: Ribs, vertebrae, and lesions present in them become prominent in the most of Tc-99m MDP bone scan images (80.3%) restored using BD algorithm.

Optimizing input parameter of the randomized singular value decomposition algorithm for compressing technetium-99m L,L, ethylenedicysteine renal dynamic study in minimum time preserving clinical information.

OBJECTIVE: The SVDsketch [MATLAB function which implements a randomized singular value decomposition (rSVD) algorithm] uses tolerance (tol) to adaptively determine the rank of the matrix sketch approximation. As the tol gets larger, fewer features of input image matrix are used in the matrix sketch. The objective of this study was to optimize the value of tol for compressing technetium-99m (Tc-99m) L,L, ethylenedicysteine (LLEC) renal dynamic (RD) study in minimum time preserving clinical information. MATERIALS AND METHODS: At different values of tol [0.00012(default), 0.1, 0.01, and 0.05] 50 Tc-99m LLEC RD studies were compressed. Two nuclear medicine (NM) physicians compared compressed images at tol = 0.1 with its input images. The SVD computation time and compression factor were calculated for each study. The image quality metrics: Error, structural similarity index for measuring image quality, brightness, global contrast factor (GCF), contrast per pixel (CPP), and blur were used for objective assessment of image quality. Percentage error in split function estimated from compressed and original images was calculated. Wilcoxon signed-rank test was applied to find statistically significant difference between renal split function, blur, GCF, CPP, and brightness of the compressed image and the original image at . RESULTS: As per NM physicians, compressed images estimated with tol = 0.1 were identical to the original images. Based on image quality metrics, compressed images were significantly less noisy, brighter, and have better contrast compared with its input images. There was insignificant difference in split renal function estimated from compressed RD study at tol = 0.1 and its original study. The SVD computation and percentage compression per study were found to be 0.04725 s and up to 74.53%. CONCLUSION: The optimized value of tol for compressing Tc-99m LLEC RD study preserving clinical information was found to be 0.1, and SVD computation time: 0.04725 s.

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.

Near-lossless compression of PET/CT images using singular value decomposition.

AIMS AND OBJECTIVES: The aim of the study is to compare the single matrix approach and slice-by-slice approach for computing singular value decomposition (SVD) to achieve near-lossless compression of PET/CT images. MATERIALS AND METHODS: The parameters used for comparison were SVD computation time, percentage compression and percentage difference between ROI counts on compressed and original images. SVD of 49 F-18-FDG PET/CT studies (33 370 PET/CT images) was computed using both approaches. The smaller singular values contributing insignificant information to the image were truncated, and then, the compressed image was reconstructed. A mask (101 × 101pixels) was used to extract the ROI counts from compressed and original images. Two nuclear medicine physicians compared compressed images with their corresponding original images for loss of clinical details and the presence of generated artifacts. Structural Similarity Index Measure, blur, brightness, contrast per pixel and global contrast factor were used for objective assessment of image quality. Wilcoxon test was applied to find a statistically significant difference between the parameters used for comparison at alpha = 0.05. RESULTS: Nuclear medicine physicians found compressed image identical to the corresponding original image. The values of comparation parameters were significantly larger for the single matrix approach in comparison with the slice-by-slice approach. The maximum percentage error between the compressed image and original image was less than 5%. CONCLUSIONS: Up to 64 % and 44% near-lossless compression of PET and CT images were achieved, respectively using the slice-by-slice approach, and up to 58 and 53% near-lossless compression of PET and CT images were achieved respectively using the single matrix approach.

Restoration of Tc-99m methyl diphosphonate bone scan image using Richardson-Lucy algorithm.

INTRODUCTION: In this study, the optimal input parameters point spread function (PSF) and the number of iterations of the Richardson-Lucy algorithm were experimentally determined to restore Tc-99 m methyl diphosphonate (MDP) whole-body bone scan images. MATERIALS AND METHODS: The experiment was performed on 60 anonymized Tc-99 m MDP whole-body bone scan images. Ten images were used for estimating the optimum value of PSF and the number of iterations to restore scintigraphic images. The remaining 50 images were used for validation of estimated parameters. The image quality of observed and restored images was assessed objectively using blind/referenceless image spatial quality evaluator (BRISQUE), mean brightness (MB), discrete entropy (DE), and edge-based contrast measure (EBCM) image quality metrics. Image quality was subjectively assessed by two nuclear medicine physicians (NMPs) by comparing the restored image quality with observed image quality and assigning a score to each image on the scale of 0-5. RESULTS: Based on BRISQUE, MB, DE, and EBCM scores, the restored images were significantly sharper, less bright, had more detailed information, and had less contrast around edges compared to the input images. The restored images had improved resolution based on visual assessment as well; NMPs assigned an average image quality score of 4.00 to restored images. Maximum resolution enhancement was noticed at PSF (size: 11 pixels, sigma: 1.75 pixels) and the number of iterations = 10. With the increase in the number of iterations, noise also gets amplified along with resolution enhancement and affects the detectability of small lesions; in the case of relatively low noisy input images, the number of iterations = 5 gave better results. CONCLUSION: Tc-99 m MDP bone scan images were restored to improve image quality using the Richardson-Lucy algorithm. The optimum value of the PSF parameter was found to be of size = 11 pixels and sigma = 1.75 pixels.

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.

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%).