Objective To explore the diagnostic value of deep learning image reconstruction(DLIR)com-bined with low-dose chest computed tomography(CT)with axial scan in the diagnosis of mycoplasma pneumoniae pneumonia(MPP)in children,and to provide reference for clinical practice.Methods 160 cases MPP children from February 2024 to June 2025 were selected as study subjects,and low-dose chest CT with axial scan was performed on all patients.DLIR and conventional adaptive iterative reconstruction-V(ASIR-V)were used for image reconstruction.The objective image quality[background noise(SD),signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR)],subjective image quality,and CT sign detection rate were compared,and the consistency of DLIR and ASIR-V in the diagnosis of MPP severity and clinical diagnosis was compared.Results As the intensity of DLIR and the weight of ASIR increasd,SD gradually decreased,while SNR and CNR gradually increased.The high-strength DLIR(DLIR-H)SD was lower than that of ASIR with a blending level of 80%(ASIR-V80%).The SNR and CNR were higher than those of ASIR-V80%,showing statistical significance(P<0.05).Ridit test showed that DLIR-H had the best subjective image quality score under different DLIR intensities,and ASIR-V80%had the best subjective image quality score under different ASIR weights.Furthermore,the subjective image quality score of DLIR-H was higher that of ASIR-V80%,and the differences were statistically significant(P<0.05).Using DLIR-H,the detection rates of air bronchogram,pulmonary consolidation,and interstitial infiltration(69.38%,86.88%,20.63%,respectively)were higher than those using ASIR-V80%(50.00%,71.88%,7.50%,respectively),and the differences were statistically significant(P<0.05).Consistency analysis showed that the Kappa value between the diagnostic results of MPP severity using DLIR-H and clinical diagnosis was 0.856(95%CI:0.771～0.996),while that between the diagnostic results of MPP severity using ASIR-V80%and clinical diagnosis was 0.498(95%CI:0.346～0.650).ROC analysis showed that the area under the curve(AUC)for diagnosing MPP severity was 0.925(95%CI:0.872～0.960)for DLIR-H and 0.729(95%CI:0.653～0.796)for ASIR-V80%,and the diagnostic value of DLIR-H was superior to that of ASIR-V80%(Z=3.952,P<0.001).Conclusion DLIR can effectively improve image quality.DLIR-H combined with low-dose chest CT with axial scan has high diagnostic value for the severity of MPP,and can serve as a feasible solution for clinical diagnosis of MPP severity and reducing radiation dose.

Objective To explore the diagnostic value of deep learning image reconstruction(DLIR)com-bined with low-dose chest computed tomography(CT)with axial scan in the diagnosis of mycoplasma pneumoniae pneumonia(MPP)in children,and to provide reference for clinical practice.Methods 160 cases MPP children from February 2024 to June 2025 were selected as study subjects,and low-dose chest CT with axial scan was performed on all patients.DLIR and conventional adaptive iterative reconstruction-V(ASIR-V)were used for image reconstruction.The objective image quality[background noise(SD),signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR)],subjective image quality,and CT sign detection rate were compared,and the consistency of DLIR and ASIR-V in the diagnosis of MPP severity and clinical diagnosis was compared.Results As the intensity of DLIR and the weight of ASIR increasd,SD gradually decreased,while SNR and CNR gradually increased.The high-strength DLIR(DLIR-H)SD was lower than that of ASIR with a blending level of 80%(ASIR-V80%).The SNR and CNR were higher than those of ASIR-V80%,showing statistical significance(P<0.05).Ridit test showed that DLIR-H had the best subjective image quality score under different DLIR intensities,and ASIR-V80%had the best subjective image quality score under different ASIR weights.Furthermore,the subjective image quality score of DLIR-H was higher that of ASIR-V80%,and the differences were statistically significant(P<0.05).Using DLIR-H,the detection rates of air bronchogram,pulmonary consolidation,and interstitial infiltration(69.38%,86.88%,20.63%,respectively)were higher than those using ASIR-V80%(50.00%,71.88%,7.50%,respectively),and the differences were statistically significant(P<0.05).Consistency analysis showed that the Kappa value between the diagnostic results of MPP severity using DLIR-H and clinical diagnosis was 0.856(95%CI:0.771～0.996),while that between the diagnostic results of MPP severity using ASIR-V80%and clinical diagnosis was 0.498(95%CI:0.346～0.650).ROC analysis showed that the area under the curve(AUC)for diagnosing MPP severity was 0.925(95%CI:0.872～0.960)for DLIR-H and 0.729(95%CI:0.653～0.796)for ASIR-V80%,and the diagnostic value of DLIR-H was superior to that of ASIR-V80%(Z=3.952,P<0.001).Conclusion DLIR can effectively improve image quality.DLIR-H combined with low-dose chest CT with axial scan has high diagnostic value for the severity of MPP,and can serve as a feasible solution for clinical diagnosis of MPP severity and reducing radiation dose.

Objective: To explore the application value of array spatial sensitivity encoding technique (ASSET) in 3D TOF MRA of head with 8-receiving channels phased-array coil in 1.5T MR system and the methods to eliminate image artifact, in order to optimize the scanning protocol of 3D TOF MRA. Methods Using the standard model of head for MRI, scanning of 3D TOF MRA was performed combining with different ASSET reduce factor (R value), phase FOV (FOVp), scanning FOV (FOVs) and calibration FOV (FOVc; 30 cm×30 cm in group A, 35 cm×35 cm in group B and 40 cm×40 cm in group C). Then the shortest distance between bilateral wrapped artifacts (D) and SNR was measured, and the acquisition time (TA) of each group and resolution on phase direction (Rp) were recorded. Then the optimized parameters were estimated. Results ASSET artifact became serious as FOVc increased, the distance between wrapped artifact broadened to disappear as FOVs and FOVp increased and R value decreased. TA unchanged as R value and FOVp unchanged. When R value fixed, SNR increased as FOVs and FOVp increased. SNR decreased as R value increased when FOVs and FOVp fixed. Rp decreased as FOVs and FOVp increased. Conclusion ASSET scanning needs right R value matching with corresponding FOVc, FOVs and FOVp. The optimization parameters under the above mentioned condition for 3D TOF MRA of head are FOVc with 35 cm×35 cm, FOVs with 28 cm×28 cm, FOVp with 0.75 and R value with 1.25.