Subject-specific timing adaption in time-encoded arterial spin labeling imaging.

OBJECTIVES: One challenge in arterial spin labeling (ASL) is the high variability of arterial transit times (ATT), which causes associated arterial transit delay (ATD) artifacts. In patients with pathological changes, these artifacts occur when post-labeling delay (PLD) and bolus durations are not optimally matched to the subject, resulting in difficult quantification of cerebral blood flow (CBF) and ATT. This is also true for the free lunch approach in Hadamard-encoded pseudocontinuous ASL (H-pCASL). MATERIAL AND METHODS: Five healthy volunteers were scanned with a 3 T MR-system. pCASL-subbolus timing was adjusted individually by the developed adaptive Walsh-ordered pCASL sequence and an automatic feedback algorithm. The quantification results for CBF and ATT and the respective standard deviations were compared with results obtained using recommended timings and intentionally suboptimal timings. RESULTS: The algorithm individually adjusted the pCASL-subbolus PLD for each subject within the range of recommended timing for healthy subjects, with a mean intra-subject adjustment deviation of 47.15 ms for single-shot and 44.5 ms for segmented acquisition in three repetitions. DISCUSSION: A first positive assessment of the results was performed on healthy volunteers. The extent to which the results can be transferred to patients and are of benefit must be investigated in follow-up studies.

Joint estimation and correction of motion and geometric distortion in segmented arterial spin labeling.

PURPOSE: Arterial spin labeling allows noninvasive measurement of cerebral blood flow by magnetically labeling inflowing blood, using it as endogenous tracer. Unfortunately, sensitivity to subject motion is high due to the subtractive nature of arterial spin labeling, which is especially problematic if Cartesian segmented 3D gradient and spin echo (GRASE) is applied. Using a 3D GRASE PROPELLER (3DGP) segmentation, retrospective correction of in-plane rigid body motion is possible before final combination of different segments. However, the standard 3DGP reconstruction is affected by off-resonance effects and has not yet been validated with different motion patterns and levels of background suppression. METHODS: The standard algorithm (1) and a Cartesian segmented 3D GRASE (2), as well as a new 3DGP reconstruction algorithm, which allows joint estimation of motion and geometric distortion (called 3DGP-JET), are validated in 5 healthy volunteers. Image quality of perfusion-weighted images was investigated for background suppression levels of 0%, 5%, and 10% in combination with no motion, as well as slow and fast intentional motion patterns during the scan. RESULTS: The proposed 3DGP-JET algorithm allowed robust estimation of field maps and motion for all scenarios, and greatly reduced motion-related artifacts in perfusion-weighted images when compared with Cartesian segmented 3D GRASE. CONCLUSION: Further improvements of the presented 3DGP-JET routine and a combination with prospective motion correction are recommended to compensate for through-plane motion, making the presented technique a good candidate for dealing with motion-related artifacts in arterial spin labeling images in clinical reality.

Towards free breathing 3D ASL imaging of the human liver using prospective motion correction.

PURPOSE: Measurements of liver perfusion yield valuable information about certain diseases like carcinomas and cirrhosis. To assess perfusion, noninvasive arterial spin labeling (ASL) MRI has the potential to become an important alternative to contrast agent-based methods. Unfortunately, ASL perfusion-weighted images are highly susceptible to breathing motion. This work presents a prospective motion-compensation technique, tackling breathing motion during ASL experiments of the human liver. METHODS: Feasibility of 3D pseudo-continuous ASL imaging under free breathing is investigated by using a prospective motion-compensation technique with fat signal in additional 2D-EPI navigators. The proposed technique is compatible with strong signal suppression, occurring in background-suppressed pseudo-continuous ASL imaging. An additional retrospective 2D elastic registration algorithm is proposed to correct for residual elastic deformations. The technique is validated in 8 healthy volunteers. RESULTS: The prospective technique allowed a significant reduction of the cranial-caudal liver shifts during free breathing pseudo-continuous ASL experiments. Additional 2D elastic image registration allowed reduction of cranial-caudal liver motion to levels of timed breathing protocols. Artifacts in perfusion-weighted images could be reduced when compared with free-breathing ASL images without motion correction. CONCLUSION: The proposed technique is suitable for prospective compensation of liver motion in background-suppressed 3D pseudo-continuous ASL imaging. Future work focuses on further improving the quality of perfusion-weighted images.

Combined acquisition of diffusion and T2*-weighted measurements using simultaneous multi-contrast magnetic resonance imaging.

OBJECT: In this work, we present a technique called simultaneous multi-contrast imaging (SMC) to acquire multiple contrasts within a single measurement. Simultaneous multi-slice imaging (SMS) shortens scan time by allowing the repetition time (TR) to be reduced for a given number of slices. SMC imaging preserves TR, while combining different scan types into a single acquisition. This technique offers new opportunities in clinical protocols where examination time is a critical factor and multiple image contrasts must be acquired. MATERIALS AND METHODS: High-resolution, navigator-corrected, diffusion-weighted imaging was performed simultaneously with T2*-weighted acquisition at 3 T in a phantom and in five healthy subjects using an adapted readout-segmented EPI sequence (rs-EPI). RESULTS: The results demonstrated that simultaneous acquisition of two contrasts (here diffusion-weighted imaging and T2*-weighting) with SMC imaging is feasible with robust separation of contrasts and minimal effect on image quality. DISCUSSION: The simultaneous acquisition of multiple contrasts reduces the overall examination time and there is an inherent registration between contrasts. By using the results of this study to control saturation effects in SMC, the method enables rapid acquisition of distortion-matched and well-registered diffusion-weighted and T2*-weighted imaging, which could support rapid diagnosis and treatment of acute stroke.

Prospective motion correction in functional MRI using simultaneous multislice imaging and multislice-to-volume image registration.

The sensitivity to subject motion is one of the major challenges in functional MRI (fMRI) studies in which a precise alignment of images from different time points is required to allow reliable quantification of brain activation throughout the scan. Especially the long measurement times and laborious fMRI tasks add to the amount of subject motion found in typical fMRI measurements, even when head restraints are used. In case of moving subjects, prospective motion correction can maintain the relationship between spatial image information and subject anatomy by constantly adapting the image slice positioning to follow the subject in real time. Image-based prospective motion correction is well-established in fMRI studies and typically computes the motion estimates based on a volume-to-volume image registration, resulting in low temporal resolution. This study combines fMRI using simultaneous multislice imaging with multislice-to-volume-based image registration to allow sub-TR motion detection with subsequent real-time adaption of the imaging system. Simultaneous multislice imaging is widely used in fMRI studies and, together with multislice-to-volume-based image registration algorithms, enables computing suitable motion states after only a single readout by registering the simultaneously excited slices to a reference volume acquired at the start of the measurement. The technique is evaluated in three human BOLD fMRI studies (n = 1, 5, and 1) to explore different aspects of the method. It is compared to conventional, volume-to-volume-based prospective motion correction as well as retrospective motion correction methods. Results show a strong reduction in retrospectively computed residual motion parameters of up to 50% when comparing the two prospective motion correction techniques. An analysis of temporal signal-to-noise ratio as well as brain activation results shows high consistency between the results before and after additional retrospective motion correction when using the proposed technique, indicating successful prospective motion correction. The comparison of absolute tSNR values does not show an improvement compared to using retrospective motion correction alone. However, the improved temporal resolution may provide improved tSNR in the presence of more exaggerated intra-volume motion.

Prospective motion correction in diffusion-weighted imaging using intermediate pseudo-trace-weighted images.

Subject head motion is a major challenge in diffusion-weighted imaging, which requires a precise alignment of images from different time points to allow a reliable quantification of diffusion parameters within each voxel. The technique requires long measurement times, making it highly sensitive to long-term subject motion, even when head restraint is used. Current methods of data analysis rely on retrospective motion correction, but there are potential benefits to using prospective motion correction, in which motion is tracked and compensated for during data acquisition. This technique is regularly used to enhance image quality in blood-oxygen-level dependent (BOLD) imaging, but its application to diffusion-weighted imaging has been limited by the contrast variation between images acquired with different diffusion-gradient directions. This paper describes a novel approach to this topic that exploits the rotational invariance of the trace of the diffusion tensor to reduce the effect of this contrast variation, making it possible to perform a fast image registration using a least-squares cost function. This results in an image-based motion detection algorithm that can be applied in real time during data acquisition to adapt the slice position and orientation in response to subject motion. The motion detection capabilities of the technique were evaluated in a study of ten subjects with b-values up to 3000s/mm². The resulting motion-parameter estimates were in close agreement with reference values provided by interleaved low-b-value images with a correlation coefficient of R=0.9634 for the voxel displacements measured across all subjects and b-values. The technique was also used to perform prospective motion correction on a standard clinical MRI system with b-values up to 2000s/mm². The correction was evaluated in 3 subjects using interleaved low-b-value images, retrospective image registration using the AFNI processing package and mean diffusivity histogram analysis. Compared to acquisitions without motion correction, prospective motion correction based on pseudo-trace-weighted images was found to provide a robust method for substantially reducing the level of misregistration between volumes. In most cases, misregistrations were reduced to less than 0.2mm of translation and 0.2° of rotation for an isotropic voxel size of 2mm, yielding high-quality diffusion parameter maps even in the absence of head restraint and post-acquisition image registration.

Prospective motion correction in 2D multishot MRI using EPI navigators and multislice-to-volume image registration.

PURPOSE: Prospective motion correction reduces artifacts in MRI by correcting for subject motion in real time, but techniques are limited for multishot 2-dimensional (2D) sequences. This study addresses this limitation by using 2D echo-planar imaging (EPI) slice navigator acquisitions together with a multislice-to-volume image registration. METHODS: The 2D-EPI navigators were integrated into 2D imaging sequences to allow a rapid, real-time motion correction based on the registration of three navigator slices to a reference volume. A dedicated slice-iteration scheme was used to limit mutual spin-saturation effects between navigator and image data. The method was evaluated using T2 -weighted spin echo and multishot rapid acquisition with relaxation enhancement (RARE) sequences, and its motion-correction capabilities were compared with those of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER). Validation was performed in vivo using a well-defined motion protocol. RESULTS: Data acquired during subject motion showed residual motion parameters within ±0.5 mm and ±0.5°, and demonstrated a substantial improvement in image quality compared with uncorrected scans. In a comparison to PROPELLER, the proposed technique preserved a higher level of anatomical detail in the presence of subject motion. CONCLUSIONS: EPI-navigator-based prospective motion correction using multislice-to-volume image registration can substantially reduce image artifacts, while minimizing spin-saturation effects. The method can be adapted for use in other 2D MRI sequences and promises to improve image quality in routine clinical examinations. Magn Reson Med 78:2127-2135, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

AI-driven and automated MRI sequence optimization in scanner-independent MRI sequences formulated by a domain-specific language.

Introduction: The complexity of Magnetic Resonance Imaging (MRI) sequences requires expert knowledge about the underlying contrast mechanisms to select from the wide range of available applications and protocols. Automation of this process using machine learning (ML) can support the radiologists and MR technicians by complementing their experience and finding the optimal MRI sequence and protocol for certain applications. Methods: We define domain-specific languages (DSL) both for describing MRI sequences and for formulating clinical demands for sequence optimization. By using various abstraction levels, we allow different key users exact definitions of MRI sequences and make them more accessible to ML. We use a vendor-independent MRI framework (gammaSTAR) to build sequences that are formulated by the DSL and export them using the generic file format introduced by the Pulseq framework, making it possible to simulate phantom data using the open-source MR simulation framework JEMRIS to build a training database that relates input MRI sequences to output sets of metrics. Utilizing ML techniques, we learn this correspondence to allow efficient optimization of MRI sequences meeting the clinical demands formulated as a starting point. Results: ML methods are capable of capturing the relation of input and simulated output parameters. Evolutionary algorithms show promising results in finding optimal MRI sequences with regards to the training data. Simulated and acquired MRI data show high correspondence to the initial set of requirements. Discussion: This work has the potential to offer optimal solutions for different clinical scenarios, potentially reducing exam times by preventing suboptimal MRI protocol settings. Future work needs to cover additional DSL layers of higher flexibility as well as an optimization of the underlying MRI simulation process together with an extension of the optimization method.