ABSTRACT
OBJECTIVE: To investigate feasibility of applying deep learning image reconstruction (DLIR) algorithm in a low-kilovolt enhanced scan of the upper abdomen. METHODS: A total of 64 patients (BMI<28) are selected for the enhanced upper abdomen scan and divided evenly into two groups. The tube voltages in Group A are 100kV in arterial phase and 80kV in venous phase, while tube voltages are 120kV during two phases in Group B. Image reconstruction algorithms used in Group A include the filtered back projection (FBP) algorithm, the adaptive statistical iterative reconstruction-Veo (ASIR-V 40% and 80%) algorithm, and the DLIR algorithm (DL-L, DL-M, DL-H). Image reconstruction algorithm used in Group B is ASIR-V40%. The different reconstruction algorithm images are used to measure the common hepatic artery, liver, renal cortex, erector spinae, and subcutaneous adipose in the arterial phase and the average CT value and standard deviation of the portal vein, liver, spleen, erector spinae, and subcutaneous adipose in the portal phase. The signal-to-noise ratio (SNR) is calculated, and the images are also scored subjectively. RESULTS: In Group A, noise in the aorta, liver, portal vein (the portal phase), spleen (the portal phase), renal cortex, retroperitoneal adipose, and muscle is significantly lower in both the DL-H and ASIR-V80% images, and the SNR is significantly higher than those in the remaining groups (P<0.05). The SNR of each tissue and organ in Group B is not significantly different from that in DL-M, DL-L, and ASIR-V40% in Group A (P>0.05). The subjective image quality scores in the DL-H and B groups are higher than those in the other groups, and the FBP group has significantly lower image quality than the remaining groups (P<0.05). CONCLUSION: For upper abdominal low-kilovolt enhanced scan data, the DLIR-H gear yields a more satisfactory image quality than the FBP and ASIR-V.
Subject(s)
Deep Learning , Abdomen/diagnostic imaging , Algorithms , Humans , Image Processing, Computer-Assisted , Radiation Dosage , Radiographic Image Interpretation, Computer-Assisted/methodsABSTRACT
Objective To study the effects on tissue CT number caused by scan protocols.Methods The phantom was repeatedly scanned in different protocols by changing only one of parameters,such as X-ray tube voltage,mAs and recon kernel,while other parameters were ketp unchanged.The CT number of different materials in phantom were measured and analyzed.Results The CT numbers of tissues changed remarkably with the tube voltage and had different relativity for different tissues.The CT numbers had positive correlation with kV for such maierials as polyethyle,lexan,perspex,but for teflon the correlation was negative.The mAs and recon kernel had no effects on CT number.Conclusions The CT number of tissue changes with scanning X-ray tube voltage,so the setting of scan parameters should be taken into account in image diagnosis and radiotherapy.
ABSTRACT
This paper introduces the testing methods with respect to the major parameters (X-ray tube voltage & current,exposure time,exposure volume)of X-ray machine as well as the testing results evaluation and application. It is proved that quality control of x-ray machine is an important content and technical means as to guarantee quality for radiology department of hospital.
