Mohd Akmal Masud

Objectives: We developed a modified gradient-different-weight threshold method, adapted from gradient-weighted thresholding, to improve robustness over fixed-percentage approaches. Material and Methods: The method computes voxel-wise gradient weights and optimises a threshold value by calibrating against a NEMA IQ phantom (six spheres) across sphere-to-background ratios (12:1, 10:1, 8:1, 6:1), linking the optimised threshold to a ratio factor (Cmax/Cmean) while comparing three background-estimation strategies (multi-circle, ULWL-guided narrow annular, and full wave with half maximum [FWHM]-based). Phantom optimisation showed the ULWL-guided narrow annular strategy produced the most consistent threshold-versus-ratio relationship. A proof-of-concept clinical evaluation on two retrospective I-131 SPECT/CT datasets used reference volumes from two nuclear medicine physicians (average contour) and reported percentage error, RMSD, and Dice similarity coefficient (DSC). Results: For a thyroid-only case, the method yielded 27.1 mL versus 25.3 mL (9.71% error; DSC = 0.9541). In a whole-body multi-lesion case, delineation became less reliable when uptake was diffuse, lesions were low-count, or lesions were closely spaced, which elevated local background estimates. Conclusion: These results suggest gradient-different-weight thresholding can improve I-131 SPECT tumour segmentation when background selection is feasible. However, larger multi-patient studies and direct side-by-side benchmarking are required to define its clinical operating range.

Functional imaging, particularly SPECT Iodine-131 ablation imaging, has gained recognition as a useful clinical tool for diagnosing, treating, assessing as well as avoiding a variety of disorders, which includes metastasis. Nonetheless, SPECT imaging is conspicuously characterized by low resolution, high sensitivity, limited specificity, and a low signal-to-noise ratio. This is caused by the imaging data's visually similar characteristics of lesions amongst diseases. Concentrating on the automated diagnosis of diseases with SPECT Iodine-131 ablation imaging, in this work, three types of segmentation layers are used. This comprises a pixel classification layer, dice classification layer, and focal loss layer that will be tested to determine which segmentation layer is high for auto-segmentation lesions on SPECT Iodine-131 ablation imaging. The data preprocessing, which mostly entails data augmentation, is initially carried out to address the issue of small SPECT image sample sizes by using the geometric transformation operation. Deep Designer Network App was used to develop a 3D U-Net Convolutional Neural Network (CNN). The dice classification layer shows the highest accuracy for the thyroid uptake data set, which is 42.34, 0.7333, and 0.5789 for RMSD, DSC, and IoU, respectively. There is significance in using the dice classification layer in data sets that have various forms of ground truth labeling. On the other hand, the pixel classification layer is promising and workable for the multi-disease, multi-lesion classification task of SPECT Iodine-131 ablation imaging with a huge data set training.

Iterative reconstruction on Single Photon Emission Computed Tomography (SPECT) imaging has recently been commercially available, resulting in reconstructed images with lower noise levels and better spatial resolution. The reconstruction variables employed and chosen under these suggestions include post-filtering, as well as several iterations and subset numbers. However, most of the number of iterations and subsets will give distortion to the segmentation image on a volume to be segmented. For this purpose, this paper will compare the effect of distortion on the dice similarity coefficient of volume segmentation for the iterative reconstruction setting by default as practice. The best outcome with the smallest root-mean-square deviation, highest percentage contrast values, and highest dice similarity coefficient) for all iterations and subsets is in iteration 6 subset 16. Here, the root-mean-square deviation is 8.06. Moreover, the four most enormous spheres can be calculated from the six spheres filled. The best contrast percentage is in subset 32, but the background variability for that subset is exceptionally high, which is 64.5. With post-filtering implementation, the scatter-corrected slices show the most enhanced image resolution and contrast when using advanced Astonish reconstruction algorithms. The user must decide if noise reduction via post-filtering or increased image resolution is more suitable for a certain imaging process.

Absolute quantification of Iodine-131 (¹³¹I) on Single Photon Emission Computed Tomography (SPECT) imaging is extremely necessary for the lesion dose per administrative differentiated thyroid cancer procedure. However, non-quantitative SPECT imaging makes determining the dose required for a tumour to be treated is challenging. This multi-vendor and multi-centre research uses phantom measures to assess the quantitative accuracy and inter-system variability of various SPECT/CT systems. Hence, this study aims to determine the calibration factor (CF) for ¹³¹I activity quantification using the National Electrical Manufacturer Association (NEMA) phantom uptake for the Philips BrightView XCT modality. The accuracy activity concentration for a non-spherical targeted volume is less than 15% compared to a spherical targeted volume (<9%). Furthermore, the CF has no significant difference in the value between the matrix sizes of 64×64, 128×128, and 256×256. Therefore, the gamma camera CF must be precisely determined to convert the reconstructed images' counts into activity values for quantitative imaging. However, the NEMA phantom with spherical geometrical is the standard tool for determining CF, the various geometrical shapes other than spherical should be considered for determining CF because the effect of photon distribution contribution is different for any different lesion geometrical.