Reduced acquisition times in whole body bone scintigraphy using a noise-reducing Pixon®-algorithm-a qualitative evaluation study

Verification of image quality improvement of low-count bone scintigraphy using deep learning

To improve image quality for low-count bone scintigraphy using deep learning and evaluate their clinical applicability. Six hundred patients (training, 500; validation, 50; evaluation, 50) were included in this study. Low-count original images (75%, 50%, 25%, 10%, and 5% counts) were generated from reference images (100% counts) using Poisson resampling. Output (DL-filtered) images were obtained after training with U-Net using reference images as teacher data. Gaussian-filtered images were generated for comparison. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) to the reference image were calculated to determine image quality. Artificial neural network (ANN) value, bone scan index (BSI), and number of hotspots (Hs) were computed using BONENAVI analysis to assess diagnostic performance. Accuracy of bone metastasis detection and area under the curve (AUC) were calculated. PSNR and SSIM for DL-filtered images were highest in all count percentages. BONENAVI analysis values for DL-filtered images did not differ significantly, regardless of the presence or absence of bone metastases. BONENAVI analysis values for original and Gaussian-filtered images differed significantly at ≦25% counts in patients without bone metastases. In patients with bone metastases, BSI and Hs for original and Gaussian-filtered images differed significantly at ≦10% counts, whereas ANN values did not. The accuracy of bone metastasis detection was highest for DL-filtered images in all count percentages; the AUC did not differ significantly. The deep learning method improved image quality and bone metastasis detection accuracy for low-count bone scintigraphy, suggesting its clinical applicability.

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.

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.

The usefulness of SwiftScan technology for bone scintigraphy using a novel anthropomorphic phantom

The aim of this study was to demonstrate the usefulness of SwiftScan with a low-energy high-resolution and sensitivity (LEHRS) collimator for bone scintigraphy using a novel bone phantom simulating the human body. SwiftScan planar image of lateral view was acquired in clinical condition; thereafter, each planar image of different blend ratio (0–80%) of Crality 2D processing were created. SwiftScan planar images with reduced acquisition time by 25–75% were created by Poisson’s resampling processing. SwiftScan single photon emission computed tomography (SPECT) was acquired with step-and-shoot and continuous mode, and SPECT images were reconstructed using a three-dimensional ordered subset expectation maximization incorporating attenuation, scatter and spatial resolution corrections. SwiftScan planar image showed a high contrast to noise ratio (CNR) and low percent of the coefficient of variance (%CV) compared with conventional planar image. The CNR of the tumor parts in SwiftScan SPECT was higher than that of the conventional SPECT image of step and shoot acquisition, while the %CV showed the lowest value in all systems. In conclusion, SwiftScan planar and SPECT images were able to reduce the image noise compared with planar and SPECT image with a low-energy high-resolution collimator, so that SwiftScan planar and SPECT images could be obtained a high CNR. Furthermore, the SwiftScan planar image was able to reduce the acquisition time by 25% when the blend ratio of Clarity 2D processing set to more than 40%.

A novel tool for improving the interpretation of isotope bone scans in metastatic prostate cancer

OBJECTIVES

The isotope bone scan (IBS) is the gold-standard imaging modality for detecting skeletal metastases as part of prostate cancer staging. However, its clinical utility for assessing skeletal metastatic burden is limited due to the need for subjective interpretation. We designed and tested a novel custom software tool, the Metastatic Bone Scan Tool (MetsBST), aimed at improving interpretation of IBSs, and compared its performance with that of an established software programme.

METHODS

We used IBS images from 62 patients diagnosed with prostate cancer and suspected bone metastases to design and implement MetsBST in MATLAB by defining thresholds used to identify the texture and size of metastatic bone lesions. The results of MetsBST were compared with those of the commercially available automated Bone Scan Index (aBSI) with regression analysis.

RESULTS

There was strong agreement between the MetsBST and aBSI results (R² = 0.9189). In a subregional analysis, MetsBST quantified the extent of metastatic disease in multiple bone sites in patients receiving multimodality therapy (radium-223 and external beam radiotherapy) to illustrate the differences in bone metastatic response to different treatments.

CONCLUSION

The results of MetsBST and the commercial software aBSI were highly consistent. MetsBST introduces novel clinical utility by its ability to differentiate between the responses of different bone metastases to multimodality therapies.

ADVANCES IN KNOWLEDGE

MetsBST reduces the variability in assessment of tumour burden caused by subjective interpretation. Therefore, it is a useful aid to physicians reporting nuclear medicine scans, and may improve decision-making in the treatment of metastatic prostate cancer.

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 I_A and I_B 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 I_A and I_B produces similar output image at α =0.05. The mean values of quantitative metrices of the processed images obtained using I_A and I_B 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 I_B improved statistically significantly higher proportion (P < 0.0001) of images (86/87) as compared to histogram of I_A (51/87). Quantitatively, I_B performed better than I_A, and the Chi-square distance of input and I_B was smaller than that of I_A. In addition, a statistically significant (P < 0.05) difference in all the quantitative metrics among the outputs obtained using I_A and I_B 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.

Methylene Diphosphonate Bone Scan Scintigraphic Image Enhancement using Gamma Correction and Optimizing the Value of Gamma

Introduction

Focal areas of high radiotracer uptake in a bone-scan image can result in dynamic range of the intensity value to exceed the dynamic range of the display, requiring multiple interactive contrast adjustments. This unnecessary burden on time of physician can be avoided using power law equation to brighten up the low-intensity areas in image. However, despite the widespread availability of this technique in commercial systems, for this clinical setting, the gamma-value needs to be standardized.

Materials and Methods

Sixty dark bone scan images were selected. Ten randomly selected images from this set were evaluated qualitatively and quantitatively (perception-based image quality evaluator, absolute mean brightness error, structural similarity, and peak signal to noise ratio) to select a range of gamma values (from 0.1 to 0.9, increment of 0.1), where the results were acceptable. This range of gamma was then applied to rest of the 50 images to find the best value. Images were evaluated by two experienced nuclear medicine physicians. Although not ideal, but for the purpose of simplicity, we tried reaching a single best value. For this, the physicians were asked to reach consensus on the acceptable images.

Results

In the first part of the study, after evaluation of 100 images (1 original and 9 processed images with 0.1-0.9 gammas), range of gamma values from 0.3 to 0.8 was found to be optimum. This range was then applied to rest of the 50 images. Evaluation of resultant 350 images (1 original and 6 processed for each input image) further narrowed this range to 0.4-0.7 (0.3 gamma selected only twice by one physician). The kappa for acceptable images was moderate at 0.482 (P <0.001). The single gamma value of 0.6 resulted in 72% of the images to be acceptable.

Conclusion

Use of power law equation to brighten up the low intensity areas of dark bone scan images, without loss of clinically significant details, is feasible with single gamma value of 0.6 and range of 0.4-0.7 giving best results.

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.

Clinical Validation of a Pixon-Based Reconstruction Method Allowing a Twofold Reduction in Planar Images Time of 111In-Pentetreotide Somatostatin Receptor Scintigraphy

Objective

Methods

Results

Conclusion