Large Language Models (LLMs) extracts structured data from unstructured Ga-68 Prostate Specific Membrane Antigen Positron Emission Tomography/Computed Tomography (PSMA PET/CT) reports by interpreting context and semantics, enabling reliable conversion of free-text into structured, queryable data. We evaluated whether locally deployed Llama 3.2:3b and Gemma 2:2b models could accurately perform zero-shot extraction of key diagnostic features. We retrospectively selected 50 de-identified Ga-68 PSMA PET/CT reports of histologically confirmed prostate cancer (01 January 2020–30 June 2024), ensuring rigorous expert annotation and inter-reader agreement. Reports were batch-processed using standardized zero-shot and few-shot prompts applied to locally deployed Llama 3.2:3b and Gemma 2:2b models, with model outputs benchmarked against an adjudicated dual-expert ground truth reference derived from independent annotations and consensus resolution of discrepancies using standard performance metrics. Both Llama 3.2:3b and Gemma 2:2b achieved rapid, reliable extraction of key diagnostic features from Ga-68 PSMA PET/CT reports, with excellent inter-reader agreement (average κ = 0.882). Llama 3.2:3b offered superior sensitivity (83.7

Introduction:68Gallium Prostate specific membrane antigen (68Ga PSMA PET/CT has significantly improved prostate cancer imaging by offering superior sensitivity and specificity over conventional modalities. However, the effectiveness of this diagnostic tool depends on the quality and consistency of reporting. This study evaluates the adequacy, consistency, and AI compatibility of semistructured 68Ga PSMA PET/CT reports. Methods: Essential reporting elements were determined through consensus among nuclear medicine physicians, urologists, and radiotherapists. Two hundred 68Ga PSMA PET/CT reports from prostate cancer patients (January 2020 to June 2024) were analysed. Reporting Adequacy Score (RAS) assessed the percentage of clinical needs met, while Variability Index (VI) quantified inconsistencies in terminology. Statistical analyses, including descriptive statistics, frequency distribution, and boxplots, were performed to evaluate reporting trends. Results: RAS ranged from 31% to 78%, with 91% of reports being partially adequate (50 to 80% RAS) and 9% inadequate (<50% RAS). Key clinical details, such as Lesion Maximum Standardized Uptake Value (SUVmax), Neurovascular bundle involvement, and Bone metastasis lesion count, were frequently missing. Inconsistent terminology was observed in lesion descriptions, lymph node involvement, and uptake patterns, with VI ranging from minimal (4%) to high (67%). Reports with lower RAS and high VI were less suitable for AI-based data extraction, posing challenges to automation. Conclusion: The majority of 68Ga PSMA PET/CT reports were partially adequate, with significant missing details and considerable variability in terminology. The variability was evenly distributed across minimal, moderate, and high levels. Training an AI model on such reports would likely result in slower learning and compromised performance. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Institutional ethics committee of All India Institute of Medical Sciences gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript

BACKGROUND:68 Ga prostate-specific membrane antigen (PSMA) PET/computed tomography (CT) plays a critical role in prostate cancer management. Most clinical reports, however, remain unstructured, increasing the risk of omitting essential diagnostic information. Although standardized frameworks such as PROMISE (Prostate Cancer Molecular Imaging Standardized Evaluation) have been proposed to improve consistency, real-world adherence remains variable. This study assessed the adequacy of 68 Ga PSMA PET/CT reports and evaluated their alignment with PROMISE recommendations. METHODS:A total of 189 68 Ga PSMA PET/CT reports were retrospectively reviewed and scored based on the presence (1) or absence (0) of six diagnostic elements: primary tumor localization, PSMA uptake intensity, lymph node involvement, bone metastases, visceral metastases, and seminal vesicle involvement. Reports were classified as clinically adequate (score ≥5), partially adequate (3-4), or inadequate (<3). A one-sample z test assessed whether the proportion of adequate reports met a 90% benchmark (α = 0.05). Chi-square tests evaluated differences in reporting frequencies across elements. RESULTS:None of the reports (0%) were inadequate. Of 189 reports, 95 (50.3%) were clinically adequate (score ≥5) and 94 (49.7%) were partially adequate (score 3-<5), with a significant overall adequacy rate ( P  < 0.0001). A notable gap existed between documentation of seminal vesicle involvement and prostate size ( P  < 0.0001). Despite consistent reporting of anatomical localization and molecular imaging tumour, node, metastases (miTNM) classification, key elements were often missing, including PSMA uptake metrics, lesion size, nodal features, CT parameters, imaging protocol, and reader confidence. CONCLUSION:In this single-center study, free-text 68 Ga PSMA PET/CT reports were adequate for clinical needs but demonstrated only partial conformity to the PROMISE framework.

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.

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.

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, GlobalContrastFactor (GCF), Contrastperpixel (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,andBlur 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.

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.

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.

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.

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.

Objective  In the present study, we have used machine learning algorithm to accomplish the task of automated detection of poor-quality scintigraphic images. We have validated the accuracy of our machine learning algorithm on 99m Tc-methyl diphosphonate ( 99m Tc-MDP) bone scan images. Materials and Methods  Ninety-nine patients underwent 99mTC-MDP bone scan acquisition twice at two different acquisition speeds, one at low speed and another at double the speed of the first scan, with patient lying in the same position on the scan table. The low-speed acquisition resulted in good-quality images and the high-speed acquisition resulted in poor-quality images. The principal component analysis (PCA) of all the images was performed and the first 32 principal components (PCs) were retained as feature vectors of the image. These 32 feature vectors of each image were used for the classification of images into poor or good quality using machine learning algorithm (multivariate adaptive regression splines [MARS]). The data were split into two sets, that is, training set and test set in the ratio of 60:40. Hyperparameter tuning of the model was done in which five-fold cross-validation was performed. Receiver operator characteristic (ROC) analysis was used to select the optimal model using the largest value of area under the ROC curve. Sensitivity, specificity, and accuracy for the classification of poor- and good-quality images were taken as metrics for the performance of the algorithm. Result  Accuracy, sensitivity, and specificity of the model in classifying poor-quality and good-quality images were 93.22, 93.22, and 93.22%, respectively, for the training dataset and 86.88, 80, and 93.7%, respectively, for the test dataset. Conclusion  Machine learning algorithms can be used to classify poor- and good-quality images with good accuracy (86.88%) using 32 PCs as the feature vector and MARS as the classification model.

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.

The relative importance of amino acid sequence and native topology in the unfolding process of two SH3 domains and two circular permutants was investigated by 120 molecular dynamics runs at 375 K for a total simulation time of 0.72 micros. The alpha-spectrin (aSH3) and src SH3 (sSH3) domains, which have the same topology and a sequence identity of only 34%, show similar unfolding pathways. The disappearance of the three-stranded antiparallel beta-sheet is the last unfolding event, in agreement with a large repertoire of kinetic data derived from point mutations as well as glycine insertions and disulfide crosslinks. Two alternative routes of beta-sheet unfolding have emerged from the analysis of the trajectories. One is statistically preferred in aSH3 (n-src loop breaks before distal hair-pin) and the inverse in sSH3. An elongation of the beta2-beta3 hairpin was observed during the unfolding of sSH3 at 375 K and in 300 K simulations started from the putative transition state of sSH3 in accord with unusual kinetic data for point mutations at the n-src loop. The change of connectivity in the permutants influenced the sequence of unfolding events mainly at the permutation site. Regions where the connectivity remained unaffected showed the same chronology of contact disappearance. Taken together with previous folding simulations of two designed three-stranded antiparallel beta-sheet peptides, these results indicate that, at least for small beta-sheet proteins, the folding mechanism is primarily defined by the native state topology, whilst specific interactions determine the statistically predominant folding route.

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.

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.

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.