Some regression formulae appear to be favourable within defined weight ranges. Accuracy of the formulae, however, is still unsatisfactory, and new formulae focusing on specific weight ranges (e. g., macrosomic fetuses) are needed. In addition, experience in obstetric ultrasound improves accuracy of fetal weight estimation.

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Purpose To evaluate the classifiability of small multiple sclerosis (MS)‐like lesions in simulated sodium (23Na) MRI for different 23Na MRI contrasts and reconstruction methods. Methods 23Na MRI and 23Na inversion recovery (IR) MRI of a phantom and simulated brain with and without lesions of different volumes (V = 1.3–38.2 nominal voxels) were simulated 100 times by adding Gaussian noise matching the SNR of real 3T measurements. Each simulation was reconstructed with four different reconstruction methods (Gridding without and with Hamming filter, Compressed sensing (CS) reconstruction without and with anatomical 1H prior information). Based on the mean signals within the lesion volumes of simulations with and without lesions, receiver operating characteristics (ROC) were determined and the area under the curve (AUC) was calculated to assess the classifiability for each lesion volume. Results Lesions show higher classifiability in 23Na MRI than in 23Na IR MRI. For typical parameters and SNR of a 3T scan, the voxel normed minimal classifiable lesion volume (AUC > 0.9) is 2.8 voxels for 23Na MRI and 19 voxels for 23Na IR MRI, respectively. In terms of classifiability, Gridding with Hamming filter and CS without anatomical 1H prior outperform CS reconstruction with anatomical 1H prior. Conclusion Reliability of lesion classifiability strongly depends on the lesion volume and the 23Na MRI contrast. Additional incorporation of 1H prior information in the CS reconstruction was not beneficial for the classification of small MS‐like lesions in 23Na MRI.