Influence of image contrasts and reconstruction methods on the classification of multiple sclerosis-like lesions in simulated sodium magnetic resonance imaging

PURPOSE

To evaluate the classifiability of small multiple sclerosis (MS)-like lesions in simulated sodium (²³ Na) MRI for different ²³ Na MRI contrasts and reconstruction methods.

METHODS

²³ Na MRI and ²³ Na 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 ¹ H 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 ²³ Na MRI than in ²³ Na 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 ²³ Na MRI and 19 voxels for ²³ Na IR MRI, respectively. In terms of classifiability, Gridding with Hamming filter and CS without anatomical ¹ H prior outperform CS reconstruction with anatomical ¹ H prior.

CONCLUSION

Reliability of lesion classifiability strongly depends on the lesion volume and the ²³ Na MRI contrast. Additional incorporation of ¹ H prior information in the CS reconstruction was not beneficial for the classification of small MS-like lesions in ²³ Na MRI.

Compressed sensing and the use of phased array coils in 23Na MRI: a comparison of a SENSE-based and an individually combined multi-channel reconstruction

PURPOSE

To implement and to evaluate a compressed sensing (CS) reconstruction algorithm based on the sensitivity encoding (SENSE) combination scheme (CS-SENSE), used to reconstruct sodium magnetic resonance imaging (²³Na MRI) multi-channel breast data sets.

METHODS

In a simulation study, the CS-SENSE algorithm was tested and optimized by evaluating the structural similarity (SSIM) and the normalized root-mean-square error (NRMSE) for different regularizations and different undersampling factors (USF=1.8/3.6/7.2/14.4). Subsequently, the algorithm was applied to data from in vivo measurements of the healthy female breast (n=3) acquired at 7T. Moreover, the proposed CS-SENSE algorithm was compared to a previously published CS algorithm (CS-IND).

RESULTS

The CS-SENSE reconstruction leads to an increased image quality for all undersampling factors and employed regularizations. Especially if a simple 2^nd order total variation is chosen as sparsity transformation, the CS-SENSE reconstruction increases the image quality of highly undersampled data sets (CS-SENSE: SSIM_USF=7.2=0.234, NRMSE_USF=7.2=0.491 vs. CS-IND: SSIM_USF=7.2=0.201, NRMSE_USF=7.2=0.506).

CONCLUSION

The CS-SENSE reconstruction supersedes the need of CS weighting factors for each channel as well as a method to combine single channel data. The CS-SENSE algorithm can be used to reconstruct undersampled data sets with increased image quality. This can be exploited to reduce total acquisition times in ²³Na MRI.

Comparison of optimized intensity correction methods for 23Na MRI of the human brain using a 32-channel phased array coil at 7 Tesla

PURPOSE

To correct for the non-homogeneous receive profile of a phased array head coil in sodium magnetic resonance imaging (²³Na MRI).

METHODS

²³Na MRI of the human brain (n = 8) was conducted on a 7T MR system using a dual-tuned quadrature ¹H/²³Na transmit/receive birdcage coil, equipped with a 32-channel receive-only array. To correct the inhomogeneous receive profile four different methods were applied: (1) the uncorrected phased array image and an additionally acquired birdcage image as reference image were low-pass filtered and divided by each other. (2) The second method substituted the reference image by a support region. (3) By averaging the individually calculated receive profiles, a universal sensitivity map was obtained and applied. (4) The receive profile was determined by a pre-scanned large uniform phantom. The calculation of the sensitivity maps was optimized in a simulation study using the normalized root-mean-square error (NRMSE). All methods were evaluated in phantom measurements and finally applied to in vivo ²³Na MRI data sets. The in vivo measurements were partial volume corrected and for further evaluation the signal ratio between the outer and inner cerebrospinal fluid compartments (CSF_out:CSF_in) was calculated.

RESULTS

Phantom measurements show the correction of the intensity profile applying the given methods. Compared to the uncorrected phased array image (NRMSE = 0.46, CSF_out:CSF_in = 1.71), the quantitative evaluation of simulated and measured intensity corrected human brain data sets indicates the best performance utilizing the birdcage image (NRMSE = 0.39, CSF_out:CSF_in = 1.00). However, employing a support region (NRMSE = 0.40, CSF_out:CSF_in = 1.17), a universal sensitivity map (NRMSE = 0.41, CSF_out:CSF_in = 1.05) or a pre-scanned sensitivity map (NRMSE = 0.42, CSF_out:CSF_in = 1.07) shows only slightly worse results.

CONCLUSION

Acquiring a birdcage image as reference image to correct for the receive profile demonstrates the best performance. However, when aiming to reduce acquisition time or for measurements without existing birdcage coil, methods that use a support region as reference image, a universal or a pre-scanned sensitivity map provide good alternatives for correction of the receive profile.

Nematodes associated with damage in oilseed rape: new data on the biology and geographical distribution of Meloidogyne artiellia

For several years, patches of plants presenting deficiencies in growth have been observed in fields cultivated with oilseed rape. Over 3 years, 57 pairs of damaged and undamaged plants were sampled from cultivated fields on the Atlantic coast of France and around the Paris basin. Results show that two main species of plant-parasitic nematodes can be associated with the patches, Heterodera cruciferae and Meloidogyne artiellia, and that both species probably do not occur in the same fields and regions. This means that M. artiellia, which is considered as a Mediterranean species causing damage essentially on wheat, is also well adapted to colder climate conditions and can extensively damage oilseed rape. Monitoring the different development stages of M. artiellia in a field cultivated with oilseed rape provided more information about the life cycle and showed that only one generation can develop between the sowing in autumn and April.