Boris Mailhe

The challenge of mapping the human connectome based on diffusion tractography

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.Though tractography is widely used, it has not been systematically validated. Here, authors report results from 20 groups showing that many tractography algorithms produce both valid and invalid bundles.

Tractography-based connectomes are dominated by false-positive connections

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.

AIR-MRF: Accelerated iterative reconstruction for magnetic resonance fingerprinting

Existing approaches for reconstruction of multiparametric maps with magnetic resonance fingerprinting (MRF) are currently limited by their estimation accuracy and reconstruction time. We aimed to address these issues with a novel combination of iterative reconstruction, fingerprint compression, additional regularization, and accelerated dictionary search methods. The pipeline described here, accelerated iterative reconstruction for magnetic resonance fingerprinting (AIR-MRF), was evaluated with simulations as well as phantom and in vivo scans. We found that the AIR-MRF pipeline provided reduced parameter estimation errors compared to non-iterative and other iterative methods, particularly at shorter sequence lengths. Accelerated dictionary search methods incorporated into the iterative pipeline reduced the reconstruction time at little cost of quality.

Learning a multiscale patch-based representation for image denoising in X-RAY fluoroscopy

Denoising is an indispensable step in processing low-dose X-ray fluoroscopic images that requires development of specialized high-quality algorithms able to operate in near real-time. We address this problem with an efficient deep learning approach based on the process-centric view of traditional iterative thresholding methods. We develop a novel trainable patch-based multiscale framework for sparse image representation. In a computationally efficient way, it allows us to accurately reconstruct important image features on multiple levels of decomposition with patch dictionaries of reduced size and complexity. The flexibility of the chosen machine learning approach allows us to tailor the learned basis for preserving important structural information in the image and noticeably minimize the amount of artifacts. Our denoising results obtained with real clinical data demonstrate significant quality improvement and are computed much faster in comparison with the BM3D algorithm.

High-resolution dynamic CE-MRA of the thorax enabled by iterative TWIST reconstruction

To evaluate the clinical benefit of using a new iterative reconstruction technique fully integrated on a standard clinical scanner and reconstruction system using a TWIST acquisition for high‐resolution dynamic three‐dimensional contrast‐enhanced MR angiography (CE‐MRA).