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Compressed sensing is commonly concerned with optimizing the image quality after a partial undersampling of the measurable k -space to accelerate MRI. In this article, we propose to change the focus from the quality of the reconstructed image to the quality of the downstream image analysis outcome. Specifically, we propose to optimize the patterns according to how well a sought-after pathology could be detected or localized in the reconstructed images. We find the optimal undersampling patterns in k -space that maximize target value functions of interest in commonplace medical vision problems (reconstruction, segmentation, and classification) and propose a new iterative gradient sampling routine universally suitable for these tasks. We validate the proposed MRI acceleration paradigm on three classical medical datasets, demonstrating a noticeable improvement of the target metrics at the high acceleration factors (for the segmentation problem at ×16 acceleration, we report up to 12% improvement in Dice score over the other undersampling patterns). • New paradigm for accelerating MRI, where k-space is undersampled intelligently for the benefit of downstream image analysis. • In this paradigm, the learned pattern can ruin the look of the image, while boosting the target metrics. • Our iterative gradient sampling (IGS) algorithm optimizes undersampling patterns for specific medical vision tasks and applications (cardiac, neurological, and orthopedic utility confirmed). • Noticeable improvement to target value metrics compared to the other undersampling patterns, such as equispaced or central masks. • The method proves especially instrumental at the highest acceleration factors when good image quality is impossible to obtain. [ABSTRACT FROM AUTHOR]

To assess the possibility of reducing the image acquisition time for diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) by denoising with deep learning-based reconstruction (dDLR). Seventeen patients with prostate cancer who underwent DWIBS by 1.5 T magnetic resonance imaging with a number of excitations of 2 (NEX2) and 8 (NEX8) were prospectively enrolled. The NEX2 image data were processed by dDLR (dDLR-NEX2), and the NEX2, dDLR-NEX2, and NEX8 image data were analyzed. In qualitative analysis, two radiologists rated the perceived coarseness, conspicuity of metastatic lesions (lymph nodes and bone), and overall image quality. The contrast-to-noise ratios (CNRs), contrast ratios, and mean apparent diffusion coefficients (ADCs) of metastatic lesions were calculated in a quantitative analysis. The image acquisition time of NEX2 was 2.8 times shorter than that of NEX8 (3 min 30 s vs 9 min 48 s). The perceived coarseness and overall image quality scores reported by both readers were significantly higher for dDLR-NEX2 than for NEX2 (P = 0.005–0.040). There was no significant difference between dDLR-NEX2 and NEX8 in the qualitative analysis. The CNR of bone metastasis was significantly greater for dDLR-NEX2 than for NEX2 and NEX8 (P = 0.012 for both comparisons). The contrast ratios and mean ADCs were not significantly different among the three image types. dDLR improved the image quality of DWIBS with NEX2. In the context of lymph node and bone metastasis evaluation with DWIBS in patients with prostate cancer, dDLR-NEX2 has potential to be an alternative to NEX8 and reduce the image acquisition time. • Denoising with deep learning-based reconstruction (dDLR) is a new technique. • dDLR improved the image quality of whole-body diffusion-weighted imaging (DWI). • dDLR has potential to reduce the image acquisition time of whole-body DWI. [ABSTRACT FROM AUTHOR]

Abstract: One of the main problems with rapid magnetic resonance imaging (MRI) techniques is the artifacts that result from off-resonance effects. The proposed off-resonance frequency filtered MRI (OFF-MRI) method focuses on the elimination of off-resonance components from the image of the observed object. To maintain imaging speed and simultaneously achieve good frequency selectivity, MRI is divided into two steps: signal acquisition and post-processing. After the preliminary phase in which we determine imaging parameters, MRI takes place; the signal from the same object is successively acquired M times. As a result, we obtain M partial signals in k-space, from which we form the image of the observed object in the post-processing phase, after signal acquisition has been completed. This paper demonstrates that with proper selection of acquisition parameters and weighting coefficients in the post-processing phase, OFF-MRI is equivalent to filtering the signal by finite impulse response filter of length M. It is shown that with M successive acquisitions M−1 off-resonance components can be eliminated (filtered-out) from images, and therefore, only two acquisitions are needed to eliminate one off-resonance components. On the other hand, with OFF-MRI, it is also possible to form the image of an arbitrary off-resonance component by eliminating all other off-resonance components, including the on-resonance component. The proposed OFF-MRI method is suitable for MRI where rapid acquisition is required and elimination of off-resonance components can improve reliability of measurements. [Copyright &y& Elsevier]

Abstract: Current efficient magnetic resonance imaging (MRI) methods such as parallel-imaging and k−t methods encode MR signals using a set of effective encoding functions other than the Fourier basis. This work revisits the proposition of directly manipulating the set of effective encoding functions at the radiofrequency excitation step in order to increase MRI efficiency. This approach, often termed “broadband encoding,” enables the application of algebraic matrix factorization technologies to extract efficiency by representing and encoding MR signal content in a compacted form. Broadband imaging equivalents of fast multiecho, parallel and k−t MRI are developed and analyzed. The potential of these techniques to increase the time efficiency of data acquisition is experimentally verified on a commercial MRI scanner using simple spin-echo imaging. A three-dimensional gradient-echo dynamic imaging application that demonstrates the potential benefits of this approach compared to the present state of the art for certain applications is also presented. [Copyright &y& Elsevier]

Abstract: Sensitivity-encoded phase undersampling has been combined with simultaneous slice excitation to produce a parallel MRI method with a high volumetric acquisition acceleration factor without the need for auxiliary stepped field coils. Dual-slice excitation was produced by modulating both spin and gradient echo sequences at ±6 kHz. Frequency aliasing of simultaneously excited slices was prevented by using an additional gradient applied along the slice axis during data acquisition. Data were acquired using a four-channel receiver array and ×4 sensitivity encoding on a 1.5 T MR system. The simultaneous parallel inclined readout image technique has been successfully demonstrated in both phantoms and volunteers. A multiplicative image acquisition acceleration factor of up to ×8 was achieved. Image SNR and resolution was dependent on the ratio of the readout gradient to the additional slice gradient. A ratio of approximately 2:1 produced acceptable image quality. Use of RF pulses with additional excitation bands should enable the technique to be extended to volumetric acquisition acceleration factors in the range of ×16–24 without the SNR limitations of pure partially parallel phase reduction methods. [Copyright &y& Elsevier]

Abstract: With the proposed fast frequency selective MR imaging (FFSMRI) method, we focused on the elimination of all off-resonance components from the image of the observed object. To maintain imaging speed and simultaneously achieve good frequency selectivity, MRI was divided into two steps: signal acquisition and postprocessing. After the preliminary phase in which we determine imaging parameters, MRI takes place; the signal from the same object is successively acquired M times. As a result, we obtain M partial signals in k-space, from which we calculate the image of the observed object in postprocessing phase, after signal acquisition has been completed. With proper selection of parameters, it is possible to exclude from the image a majority of off-resonance components present in the observed object. However, we can decide to keep only a chosen off-resonance component in the image and eliminate all other components, including the on-resonance component and thus producing a different image from the same acquisition. The experiments with Fe(OH)3 and oil showed that signal-to-noise ratio (SNR) can be improved by about a factor of four. The proposed FFSMRI method is suitable for frequency selective MR imaging and quantitative measurements in dynamic MRI where exclusion of off-resonance components can improve the reliability of measurement. [Copyright &y& Elsevier]