ABSTRACT
PURPOSE: The aim of this work was to investigate the lesion detectability of Tc-99m planar scintigraphy acquired with a low-energy high-resolution and sensitivity (LEHRS) collimator and processed by Clarity 2D for patients with different body sizes through phantom study. METHODS: A NEMA IEC body phantom set was covered by two layers of 25-mm-thick bolus to construct phantom in three different sizes. All image data were performed on a Discovery NM/CT 870 DR with an LEHRS collimator and processed by Clarity 2D with blend ratio a of 0%, 20%, 40%, 60%, 80%, and 100%. The lesion detectability in gamma scintigraphy was evaluated by calculating the contrast-to-noise ratio (CNR). Multiple linear regression methods were used to analyze the impact of body size, target size, and Clarity 2D blending weight on the lesion detectability of Tc-99m planar scintigraphy. RESULTS: It was found that changing the blend ratio could improve CNR, and this phenomenon was more significant in anterior view than in posterior view. Our results also suggested that the blend ratio should be selected according to patient body size in order to maintain consistent CNR. Hence, when a blend ratio of 60% was used for a patient before cancer treatment, a lower blend ratio should be used for the same patient experiencing treatment-related weight loss to achieve consistent lesion detectability in Tc-99m planar scintigraphy acquired with LEHRS and processed by Clarity 2D. CONCLUSION: The magnitude of photon attenuation and scattering is higher in patients with larger body size, so Tc-99m planar scintigraphy usually has lower lesion detectability in obese patients. Although photon attenuation and scattering are inevitable during image formation, their impacts on image quality can be eased by employing appropriate image protocol parameters.
Subject(s)
Radionuclide Imaging , Humans , Phantoms, Imaging , Body SizeABSTRACT
INTRODUCTION: This study aimed to investigate the feasibility of generating pseudo dual-energy CT (DECT) from one 120-kVp CT by using convolutional neural network (CNN) to derive additional information for quantitative image analysis through phantom study. METHODS: Dual-energy scans (80/140 kVp) and single-energy scans (120 kVp) were performed for five calibration phantoms and two evaluation phantoms on a dual-source DECT scanner. The calibration phantoms were used to generate training dataset for CNN optimization, while the evaluation phantoms were used to generate testing dataset. A CNN model which takes 120-kVp images as input and creates 80/140-kVp images as output was built, trained, and tested by using Caffe CNN platform. An in-house software to quantify contrast enhancement and synthesize virtual monochromatic CT (VMCT) for CNN-generated pseudo DECT was implemented and evaluated. RESULTS: The CT numbers in 80-kVp pseudo images generated by CNN are differed from the truth by 11.57, 16.67, 13.92, 12.23, 10.69 HU for syringes filled with iodine concentration of 2.19, 4.38, 8.75, 17.5, 35 mg/ml, respectively. The corresponding results for 140-kVp CT are 3.09, 9.10, 7.08, 9.81, 7.59 HU. The estimates of iodine concentration calculated based on the proposed method are differed from the truth by 0.104, 0.603, 0.478, 0.698, 0.795 mg/ml for syringes filled with iodine concentration of 2.19, 4.38, 8.75, 17.5, 35 mg/ml, respectively. With regards to image quality enhancement, VMCT synthesized by using pseudo DECT shows the best contrast-to-noise ratio at 40 keV. CONCLUSION: In conclusion, the proposed method should be a practicable strategy for iodine quantification in contrast enhanced 120-kVp CT without using specific scanner or scanning procedure.
