Objective: To investigate the value of histogram parameters in quantifying brain development trajectory at slice of anterior and posterior horns of lateral ventricles on conventional brain MRI in normal children aged 0-5 years. Methods: Routine brain MRI data [apparent diffusion coefficient (ADC) map, T 1 -weighted imaging (T 1 WI), and T 2 -weighted imaging (T 2 WI)] were retrospectively collected from 300 children aged 0-5 years who underwent MRI at Children 's Hospital of Nanjing Medical University from April 2014 to November 2021, 154 males and 146 females, aged [ M ( Q 1 , Q 3 =60) in a ratio of 8∶2. The training set was divided into 6 groups according to age:≤0.5 years, 24 persons; >0.5-≤1 years,21 persons; >1-≤2 years,31 persons; >2-≤3 years,44 persons; >3-≤4 years,42 persons; >4-≤5 years,78 persons. MRIcron software was used to delineate the whole brain at the level of the anterior and posterior horns of the lateral ventricles of the three MRI data as the region of interest. Then gray histograms and their parameters [including mean, maximum, minimum, skewness, kurtosis, mode, variance, and percentiles at 5% intervals from 10% to 95%(10th-95th) ]were obtained. Intra-class correlation coefficients (ICC) were used to assess consistency of intra-observer and inter-observer measurement. Representative parameters were selected by Spearman correlation analysis and curve fitting. The linear regression coefficient β represented development rates at different ages. The selected curve regression models were applied to the validation set, and the reliability of the model was evaluated with accuracy. n =240) and validation set ( n =60) in a ratio of 8∶2. The training set was divided into 6 groups according to age:≤0.5 years, 24 persons; >0.5-≤1 years,21 persons; >1-≤2 years,31 persons; >2-≤3 years,44 persons; >3-≤4 years,42 persons; >4-≤5 years,78 persons. MRIcron software was used to delineate the whole brain at the level of the anterior and posterior horns of the lateral ventricles of the three MRI data as the region of interest. Then gray histograms and their parameters [including mean, maximum, minimum, skewness, kurtosis, mode, variance, and percentiles at 5% intervals from 10% to 95%(10th-95th) ]were obtained. Intra-class correlation coefficients (ICC) were used to assess consistency of intra-observer and inter-observer measurement. Representative parameters were selected by Spearman correlation analysis and curve fitting. The linear regression coefficient β represented development rates at different ages. The selected curve regression models were applied to the validation set, and the reliability of the model was evaluated with accuracy. Results: Intra-observer and inter-observer histogram measurement parameters were generally in good consistency (ICC>0.800, all P <0.001). Histogram parameters ADC 10th-65th, T 1 WI 55th-80th and T 2 WI 10th-45th were highly correlated with age (∣ r ∣≥0.700, 0.600 and 0.600 respectively; all P <0.001). ADC 30th and T 2 WI 10th had the greatest goodness of fit ( R ²=0.871, 0.873; both P <0.001). Map of brain development trends showed that ADC 30th and T 2 WI 10th decreased with age. ADC 30th changed rapidly before the age of 2 years, most significantly within 6 months, and the rate of decrease slowed down after 2 years old. T 2 Histogram parameters can quantify brain developmental trajectories at slice of anterior and posterior horns of lateral ventricles on conventional MRI in normal children aged 0-5 years, and obtain the brain development curves reflecting this slice of this age group.2 WI 10th had higher accuracy in validation set [93% (56/60) and 95% (57/60), respectively]. Conclusion: Histogram parameters can quantify brain developmental trajectories at slice of anterior and posterior horns of lateral ventricles on conventional MRI in normal children aged 0-5 years, and obtain the brain development curves reflecting this slice of this age group.