Purpose: The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully.Methods: Gradient‐echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations.Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), high‐frequency error norm (HFEN), and the error in selected white and gray matter regions.Results: Twenty‐seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill‐conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance.Conclusion: Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over‐smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria. Magn Reson Med 79:1661–1673, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

To improve susceptibility quantification, a threshold‐based k‐space/image domain iterative approach that uses geometric information from the susceptibility map itself as a constraint to overcome the ill‐posed nature of the inverse filter is introduced. Simulations were used to study the accuracy of the method and its robustness in the presence of noise. In vivo data were processed and analyzed using this method. Both simulations and in vivo results show that most streaking artifacts inside the susceptibility map caused by the ill‐defined inverse filter were suppressed by the iterative approach. In simulated data, the bias toward lower mean susceptibility values inside vessels has been shown to decrease from around 10% to 2% when choosing an appropriate threshold value for the proposed iterative method. Typically, three iterations are sufficient for this approach to converge and this process takes less than 30 s to process a 512 × 512 × 256 dataset. This iterative method improves quantification of susceptibility inside vessels and reduces streaking artifacts throughout the brain for data collected from a single‐orientation acquisition. This approach has been applied to vessels alone as well as to vessels and other structures with lower susceptibility to generate whole brain susceptibility maps with significantly reduced streaking artifacts. Magn Reson Med, 2013.

Purpose: To demonstrate the mapping of structures with high susceptibility values, such as the sinuses, bones and teeth, using short echo times.Methods: Four in vivo datasets were collected with a gradient‐echo sequence (TE₁ = 2.5 ms, TE₂ = 5 ms and TE₃ = 7.5 ms). Complex division was performed to remove the phase offset term and generate the phase at TE = 2.5 ms. Susceptibility maps were generated from unwrapped phase images, using a geometry‐constrained iterative algorithm, by preserving phase information in the extracerebral tissues. The susceptibility results were improved by updating the missing phase information inside structures with no signal using the predicted phase at each iteration step. Simulated phase images of a three‐dimensional brain model and tooth phantom were used to validate the proposed method.Results: Improved susceptibility maps were obtained once the phase information in the extracerebral tissue region was incorporated, for both the model and in vivo data. For in vivo data, the average susceptibilities of air (sphenoid sinus), bone and calcium (teeth) were found to be (in ppm): Δχ_{(sinus‐tissue)} = +9.2 ± 1.3, Δχ_{(bone‐tissue)} = −2.1 ± 0.6 and Δχ_{(teeth‐tissue)} = −3.3 ± 1.2, respectively.Conclusion: High susceptibility structures with little or no signal can be imaged using quantitative susceptibility mapping and can be used to improve background field removal. Magn Reson Med 73:2185–2194, 2015.

The fundamental motivation of the proposed work is to present a new visualization-guided computing paradigm to combine direct 3D volume processing and volume rendered clues for effective 3D exploration. For example, extracting and visualizing microstructures in-vivo have been a long-standing challenging problem. However, due to the high sparseness and noisiness in cerebrovasculature data as well as highly complex geometry and topology variations of micro vessels, it is still extremely challenging to extract the complete 3D vessel structure and visualize it in 3D with high fidelity. In this paper, we present an end-to-end deep learning method, VC-Net, for robust extraction of 3D microvascular structure through embedding the image composition, generated by maximum intensity projection (MIP), into the 3D volumetric image learning process to enhance the overall performance. The core novelty is to automatically leverage the volume visualization technique (e.g., MIP - a volume rendering scheme for 3D volume images) to enhance the 3D data exploration at the deep learning level. The MIP embedding features can enhance the local vessel signal (through canceling out the noise) and adapt to the geometric variability and scalability of vessels, which is of great importance in microvascular tracking. A multi-stream convolutional neural network (CNN) framework is proposed to effectively learn the 3D volume and 2D MIP feature vectors, respectively, and then explore their inter-dependencies in a joint volume-composition embedding space by unprojecting the 2D feature vectors into the 3D volume embedding space. It is noted that the proposed framework can better capture the small / micro vessels and improve the vessel connectivity. To our knowledge, this is the first time that a deep learning framework is proposed to construct a joint convolutional embedding space, where the computed vessel probabilities from volume rendering based 2D projection and 3D volume can be explored and integrated synergistically. Experimental results are evaluated and compared with the traditional 3D vessel segmentation methods and the state-of-the-art in deep learning, by using extensive public and real patient (micro-)cerebrovascular image datasets. The application of this accurate segmentation and visualization of sparse and complicated 3D microvascular structure facilitated by our method demonstrates the potential in a powerful MR arteriogram and venogram diagnosis of vascular disease.