Optimizing background suppression for dual-module velocity-selective arterial spin labeling: Without using additional background-suppression pulses.

PURPOSE: Background suppression (BS) is recommended in arterial spin labeling (ASL) for improved SNR but is difficult to optimize in existing velocity-selective ASL (VSASL) methods. Dual-module VSASL (dm-VSASL) enables delay-insensitive, robust, and SNR-efficient perfusion imaging, while allowing efficient BS, but its optimization has yet to be thoroughly investigated. METHODS: The inversion effects of the velocity-selective labeling pulses, such as velocity-selective inversion (VSI), can be used for BS, and were modeled for optimizing BS in dm-VSASL. In vivo experiments using dual-module VSI (dm-VSI) were performed to compare two BS strategies: a conventional one with additional BS pulses and a new one without any BS pulse. Their BS performance, temporal noise, and temporal SNR were examined and compared, with pulsed and pseudo-continuous ASL (PASL and PCASL) as the reference. RESULTS: The in vivo experiments validated the BS modeling. Strong positive linear correlations (r > 0.82, p < 0.0001) between the temporal noise and the tissue signal were found in PASL/PCASL and dm-VSI. Optimal BS can be achieved with and without additional BS pulses in dm-VSI; the latter improved the ASL signals by 8.5% in gray matter (p = 0.006) and 12.2% in white matter (p = 0.014) and tended to provide better temporal SNR. The dm-VSI measured significantly higher ASL signal (p < 0.016) and temporal SNR (p < 0.018) than PASL and PCASL. Complex reconstruction was found necessary with aggressive BS. CONCLUSION: Guided by modeling, optimal BS can be achieved without any BS pulse in dm-VSASL, further improving the ASL signal and the SNR performance.

Whole-Cerebrum distortion-free three-dimensional pseudo-continuous arterial spin labeling at 7T.

Fulfilling potentials of ultrahigh field for pseudo-Continuous Arterial Spin Labeling (pCASL) has been hampered by B1/B0 inhomogeneities that affect pCASL labeling, background suppression (BS), and the readout sequence. This study aimed to present a whole-cerebrum distortion-free three-dimensional (3D) pCASL sequence at 7T by optimizing pCASL labeling parameters, BS pulses, and an accelerated Turbo-FLASH (TFL) readout. A new set of pCASL labeling parameters (Gave = 0.4 mT/m, Gratio = 14.67) was proposed to avoid interferences in bottom slices while achieving robust labeling efficiency (LE). An OPTIM BS pulse was designed based on the range of B1/B0 inhomogeneities at 7T. A 3D TFL readout with 2D-CAIPIRINHA undersampling (R = 2 × 2) and centric ordering was developed, and the number of segments (Nseg) and flip angle (FA) were varied in simulation to achieve the optimal trade-off between SNR and spatial blurring. In-vivo experiments were performed on 19 subjects. The results showed that the new set of labeling parameters effectively achieved whole-cerebrum coverage by eliminating interferences in bottom slices while maintaining a high LE. The OPTIM BS pulse achieved 33.3% higher perfusion signal in gray matter (GM) than the original BS pulse with a cost of 4.8-fold SAR. Incorporating a moderate FA (8°) and Nseg (2), whole-cerebrum 3D TFL-pCASL imaging was achieved with a 2 × 2 × 4 mm3 resolution without distortion and susceptibility artifacts compared to 3D GRASE-pCASL. In addition, 3D TFL-pCASL showed a good to excellent test-retest repeatability and potential of higher resolution (2 mm isotropic). The proposed technique also significantly improved SNR when compared to the same sequence at 3T and simultaneous multislice TFL-pCASL at 7T. By combining a new set of labeling parameters, OPTIM BS pulse, and accelerated 3D TFL readout, we achieved high resolution pCASL at 7T with whole-cerebrum coverage, detailed perfusion and anatomical information without distortion, and sufficient SNR.

A source for awareness-dependent figure-ground segregation in human prefrontal cortex.

Figure-ground modulation, i.e., the enhancement of neuronal responses evoked by the figure relative to the background, has three complementary components: edge modulation (boundary detection), center modulation (region filling), and background modulation (background suppression). However, the neuronal mechanisms mediating these three modulations and how they depend on awareness remain unclear. For each modulation, we compared both the cueing effect produced in a Posner paradigm and fMRI blood oxygen-level dependent (BOLD) signal in primary visual cortex (V1) evoked by visible relative to invisible orientation-defined figures. We found that edge modulation was independent of awareness, whereas both center and background modulations were strongly modulated by awareness, with greater modulations in the visible than the invisible condition. Effective-connectivity analysis further showed that the awareness-dependent region-filling and background-suppression processes in V1 were not derived through intracortical interactions within V1, but rather by feedback from the frontal eye field (FEF) and dorsolateral prefrontal cortex (DLPFC), respectively. These results indicate a source for an awareness-dependent figure-ground segregation in human prefrontal cortex.

Echo pulses for background suppression by optimal control.

Materials used to construct magic-angle-spinning NMR probes can contain NMR active nuclei that produce a significant amount of background signal. Because these materials are located outside the sample coil, the use of spatially selective pulses to remove the background is a popular approach for background suppression. However, previously suggested spatially selective pulses suffer from limited excitation bandwidths, which may make them unsuitable for the acquisition of nuclei with a large chemical shift range. Here, a pulse (OC-BACK) is presented, which has been developed by optimal control, which has a flat profile of ~120 kHz with respect to off-resonance effects and extended pass and suppression bands with respect to the nominal nutation frequency. The presented solution is large enough to be effective for background suppression schemes in 19 F magic-angle-spinning NMR at medium and low magnetic fields.

Infrared Image-Enhancement Algorithm for Weak Targets in Complex Backgrounds.

Infrared small-target enhancement in complex contexts is one of the key technologies for infrared search and tracking systems. The effect of enhancement directly determines the reliability of the monitoring equipment. To address the problem of the low signal-to-noise ratio of small infrared moving targets in complex backgrounds and the poor effect of traditional enhancement algorithms, an accurate enhancement method for small infrared moving targets based on two-channel information is proposed. For a single frame, a modified curvature filter is used in the A channel to weaken the background while an improved PM model is used to enhance the target, and a modified band-pass filter is used in the B channel for coarse enhancement followed by a local contrast algorithm for fine enhancement, based on which a weighted superposition algorithm is used to extract a single-frame candidate target. The results of the experimental data analysis prove that the method has a good enhancement effect and robustness for small IR motion target enhancement in complex backgrounds, and it outperforms other advanced algorithms by about 43.7% in ROC.

Whole-body diffusion-weighted imaging with background body signal suppression and quantitative apparent diffusion coefficient in the detection, staging, and grading of non-Hodgkin lymphoma.

Purpose: Assess reproducibility of detection, staging, and grading of non-Hodgkin lymphoma (NHL) using whole-body diffusion-weighted imaging with background body signal suppression (WB-DWIBS). Material and methods: Thirty NHL patients underwent WB-DWIBS, divided into 2 groups according to staging and grading. Image analysis and apparent diffusion coefficient (ADC) measurement of the largest lymph node in each group were performed by 2 observers. Inter-observer agreement was performed. Results: Overall inter-observer agreement for detection of NHL was excellent (k = 0.843; 92.05%) with excellent inter-observer agreement of nodal disease (cervical, thoracic and abdominal) (k = 0.783, 0.769, and 0.856; 96.67%, 90.0%, and 93.3% respectively), extra-nodal disease (k = 1; 100%), and splenic involvement (k = 0.67; 83.3%). The overall inter-observer agreement of DWIBS in staging of NHL was excellent (k = 0.90; 94.9%) with excellent inter-observer agreement for stage I (k = 0.93; 96.4%), stage II (k = 0.90; 94.8%), stage III (k = 0.89; 94.6%), and stage IV (k = 0.88; 94.0%). There was significant difference between ADC in stage I, II (0.77 ± 0.13, 0.85 ± 0.09 × 10-3 mm2/s), and stage III, IV (0.63 ± 0.08, 0.64 ± 0.11 × 10-3 mm2/s, p < 0.002, < 0.001). Interclass correlation showed almost perfect agreement for ADC measurement in staging and grading groups (r = 0.96 and r = 0.85, respectively, p < 0.001). There was significant difference between ADC in aggressive lymphoma (0.65 ± 0.1, 0.67 ± 0.13 × 10-3 mm2/s) and indolent lymphoma (0.76 ± 0.14, 0.84 ± 0.09 × 10-3 mm2/s, p < 0.028, < 0.001). Conclusion: DWIBS is reproducible for detection and staging of nodal and extra-nodal involvement in patients with NHL. ADC can quantitatively participate in the staging and grading of NHL.

Subject-specific optimization of background suppression for arterial spin labeling magnetic resonance imaging using a feedback loop on the scanner.

Background suppression (BGS) in arterial spin labeling (ASL) magnetic resonance imaging leads to a higher temporal signal-to-noise ratio (tSNR) of the perfusion images compared with ASL without BGS. The performance of the BGS, however, depends on the tissue relaxation times and on inhomogeneities of the scanner's magnetic fields, which differ between subjects and are unknown at the moment of scanning. Therefore, we developed a feedback loop (FBL) mechanism that optimizes the BGS for each subject in the scanner during acquisition. We implemented the FBL for 2D pseudo-continuous ASL scans with an echo-planar imaging readout. After each dynamic scan, the acquired ASL images were automatically sent to an external computer and processed with a Python processing tool. Inversion times were optimized on the fly using 80 iterations of the Nelder-Mead method, by minimizing the signal intensity in the label image while maximizing the signal intensity in the perfusion image. The performance of this method was first tested in a four-component phantom. The regularization parameter was then tuned in six healthy subjects (three males, three females, age 24-62 years) and set as λ = 4 for all other experiments. The resulting ASL images, perfusion images, and tSNR maps obtained from the last 20 iterations of the FBL scan were compared with those obtained without BGS and with standard BGS in 12 healthy volunteers (five males, seven females, age 24-62 years) (including the six volunteers used for tuning of λ). The FBL resulted in perfusion images with a statistically significantly higher tSNR (2.20) compared with standard BGS (1.96) ( p < 5 x 10 - 3 , two-sided paired t-test). Minimizing signal in the label image furthermore resulted in control images, from which approximate changes in perfusion signal can directly be appreciated. This could be relevant to ASL applications that require a high temporal resolution. Future work is needed to minimize the number of initial acquisitions during which the performance of BGS is reduced compared with standard BGS, and to extend the technique to 3D ASL.

A Combined Approach to Infrared Small-Target Detection with the Alternating Direction Method of Multipliers and an Improved Top-Hat Transformation.

In infrared small target detection, the infrared patch image (IPI)-model-based methods produce better results than other popular approaches (such as max-mean, top-hat, and human visual system) but in some extreme cases it suffers from long processing times and inconsistent performance. In order to overcome these issues, we propose a novel approach of dividing the traditional target detection process into two steps: suppression of background noise and elimination of clutter. The workflow consists of four steps: after importing the images, the second step applies the alternating direction multiplier method to preliminarily remove the background. Comparatively to the IPI model, this step does not require sliding patches, resulting in a significant reduction in processing time. To eliminate residual noise and clutter, the interim results from morphological filtering are then processed in step 3 through an improved new top-hat transformation, using a threefold structuring element. The final step is thresholding segmentation, which uses an adaptive threshold algorithm. Compared with IPI and the new top-hat methods, as well as some other widely used methods, our approach was able to detect infrared targets more efficiently (90% less computational time) and consistently (no sudden performance drop).

Third-generation in situ hybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust.

In situ hybridization based on the mechanism of the hybridization chain reaction (HCR) has addressed multi-decade challenges that impeded imaging of mRNA expression in diverse organisms, offering a unique combination of multiplexing, quantitation, sensitivity, resolution and versatility. Here, with third-generation in situ HCR, we augment these capabilities using probes and amplifiers that combine to provide automatic background suppression throughout the protocol, ensuring that reagents will not generate amplified background even if they bind non-specifically within the sample. Automatic background suppression dramatically enhances performance and robustness, combining the benefits of a higher signal-to-background ratio with the convenience of using unoptimized probe sets for new targets and organisms. In situ HCR v3.0 enables three multiplexed quantitative analysis modes: (1) qHCR imaging - analog mRNA relative quantitation with subcellular resolution in the anatomical context of whole-mount vertebrate embryos; (2) qHCR flow cytometry - analog mRNA relative quantitation for high-throughput expression profiling of mammalian and bacterial cells; and (3) dHCR imaging - digital mRNA absolute quantitation via single-molecule imaging in thick autofluorescent samples.

Subtractive NCE-MRA: Improved background suppression using robust regression-based weighted subtraction.

PURPOSE: To correct the intensity difference of static background signal between bright blood images and dark blood images in subtractive non-contrast-enhanced MR angiography using robust regression, thereby improving static background signal suppression on subtracted angiograms. METHODS: Robust regression (RR), using iteratively reweighted least squares, is used to calculate the regression coefficient of background tissues from a scatter plot showing the voxel intensity of bright blood images versus dark blood images. The weighting function is based on either the Euclidean distance from the estimated regression line or the deviation angle. Results from RR using the deviation angle (RRDA), conventional RR using the Euclidean distance, and ordinary leastsquares regression were compared with reference values determined manually by two observers. Performance was evaluated over studies using different sequences, including 36 thoracic flow-sensitive dephasing data sets, 13 iliac flow-sensitive dephasing data sets, and 26 femoral fresh blood imaging data sets. RESULTS: RR deviation angle achieved robust and accurate performance in all types of images, with small bias, small mean absolute error, and high-correlation coefficients with reference values. Background tissues, such as muscle, veins, and bladder, were suppressed while the vascular signal was preserved. Euclidean distance gave good performance for thoracic and iliac flow-sensitive dephasing, but could not suppress background tissues in femoral fresh blood imaging. Ordinary least squares regression was sensitive to outliers and overestimated regression coefficients in thoracic flow-sensitive dephasing. CONCLUSION: Weighted subtraction using RR was able to acquire the regression coefficients of background signal and improve background suppression of subtractive non-contrast-enhanced MR angiography techniques. RR deviation angle has the most robust and accurate overall performance among three regression methods.

Highly accelerated subtractive femoral non-contrast-enhanced MRA using compressed sensing with k-space subtraction, phase and intensity correction.

PURPOSE: To develop an improved reconstruction method, k-space subtraction with phase and intensity correction (KSPIC), for highly accelerated, subtractive, non-contrast-enhanced MRA. METHODS: The KSPIC method is based on k-space subtraction of complex raw data. It applies a phase-correction procedure to restore the polarity of negative signals caused by subtraction and an intensity-correction procedure to improve background suppression and thereby sparsity. Ten retrospectively undersampled data sets and 10 groups of prospectively undersampled data sets were acquired in 12 healthy volunteers. The performance of KSPIC was compared with another improved reconstruction based on combined magnitude subtraction, as well as with conventional k-space subtraction reconstruction and magnitude subtraction reconstruction, both using quantitative metrics and using subjective quality scoring. RESULTS: In the quantitative evaluation, KSPIC had the best performance in terms of peak SNR, structural similarity index measure, contrast-to-noise ratio of artery-to-background and sharpness, especially at high acceleration factors. The KSPIC method also had the highest subjective scores for all acceleration factors in terms of vessel delineation, image noise and artifact, and background contamination. The acquisition can be accelerated by a factor of 20 without significant decreases of subjective scores. The optimal size of the phase-correction region was found to be 12-20 pixels in this study. CONCLUSION: Compared with combined magnitude subtraction and conventional reconstructions, KSPIC has the best performance in all of the quantitative and qualitative measurements, permitting good image quality to be maintained up to higher accelerations. The KSPIC method has the potential to further reduce the acquisition time of subtractive MRA for clinical examinations.

Self-Erasing Network for Person Re-Identification.

Person re-identification (ReID) plays an important role in intelligent surveillance and receives widespread attention from academics and the industry. Due to extreme changes in viewing angles, some discriminative local regions are suppressed. In addition, the data with similar backgrounds collected by a fixed viewing angle camera will also affect the model's ability to distinguish a person. Therefore, we need to discover more fine-grained information to form the overall characteristics of each identity. The proposed self-erasing network structure composed of three branches benefits the extraction of global information, the suppression of background noise and the mining of local information. The two self-erasing strategies that we proposed encourage the network to focus on foreground information and strengthen the model's ability to encode weak features so as to form more effective and richer visual cues of a person. Extensive experiments show that the proposed method is competitive with the advanced methods and achieves state-of-the-art performance on DukeMTMC-ReID and CUHK-03(D) datasets. Furthermore, it can be seen from the activation map that the proposed method is beneficial to spread the attention to the whole body. Both metrics and the activation map validate the effectiveness of our proposed method.

Background suppressed magnetization transfer MRI.

PURPOSE: Up to 30% of the hydrogen atoms in brain tissue are part of molecules ("semisolids") other than water. In MRI, their magnetization is typically not observed directly, but can influence the water magnetization through magnetization transfer (MT). Comparison of MRI scans differentially sensitized to MT allows estimation of the semisolid fraction and potential changes with disease. Here, we present an approach designed to improve this estimate by measuring the size of the MT effect in a single scan. METHODS: A stimulated echo sequence was used to generate a spatial pattern in the longitudinal water magnetization, which was then given time to exchange with semisolids. After saturating the remaining water magnetization, reverse exchange was allowed to partly re-establish the original water magnetization pattern. The third excitation pulse then formed a stimulated echo out of this pattern. RESULTS: MT data were obtained on 10 human subjects at 7 T with varying exchange times. The images showed the expected time dependence of signal associated with the forward and reverse exchange processes. Excellent suppression of non-exchanging background signal was achieved. As expected, this suppression came at the price of a substantial reduction in exchange-related signal (by ~75% compared to the signal in saturation recovery MT), in part because of the reliance on a 2-step exchange process. CONCLUSION: The results demonstrate an MT signal can be observed in a single acquisition without subtraction. This may be advantageous for MT measurements when signal instabilities related to motion and physiological variations exceed thermal noise sources.

Surface plasmon-coupled emission imaging for biological applications.

Fluorescence imaging technology has been extensively applied in chemical and biological research profiting from its high sensitivity and specificity. Much attention has been devoted to breaking the light diffraction-limited spatial resolution. However, it remains a great challenge to improve the axial resolution in a way that is accessible in general laboratories. Surface plasmon-coupled emission (SPCE), generated by the interactions between surface plasmons and excited fluorophores in close vicinity of the thin metal film, offers an opportunity for optical imaging with potential application in analysis of molecular and biological systems. Benefiting from the highly directional and distance-dependent properties, SPCE imaging (SPCEi) has displayed excellent performance in bioimaging with improved sensitivity and axial confinement. Herein, we give a brief overview of the development of SPCEi. We describe the unique optical characteristics and constructions of SPCEi systems and highlight recent advances in the use of SPCEi for biological applications. We hope this review provides readers with both the insights and future prospects of SPCEi as a new promising imaging platform for potentially widespread applications in biological research and medical diagnostics. Graphical abstract.

A One-Stage Approach for Surface Anomaly Detection with Background Suppression Strategies.

We explore a one-stage method for surface anomaly detection in industrial scenarios. On one side, encoder-decoder segmentation network is constructed to capture small targets as much as possible, and then dual background suppression mechanisms are designed to reduce noise patterns in coarse and fine manners. On the other hand, a classification module without learning parameters is built to reduce information loss in small targets due to the inexistence of successive down-sampling processes. Experimental results demonstrate that our one-stage detector achieves state-of-the-art performance in terms of precision, recall and f-score.

Hierarchical Suppression Based Matched Filter for Hyperspertral Imagery Target Detection.

Target detection in hyperspectral imagery (HSI) aims at extracting target components of interest from hundreds of narrow contiguous spectral bands, where the prior target information plays a vital role. However, the limitation of the previous methods is that only single-layer detection is carried out, which is not sufficient to discriminate the target parts from complex background spectra accurately. In this paper, we introduce a hierarchical structure to the traditional algorithm matched filter (MF). Because of the advantages of MF in target separation performance, that is, the background components are suppressed while preserving the targets, the detection result of MF is used to further suppress the background components in a cyclic iterative manner. In each iteration, the average output of the previous iteration is used as a suppression criterion to distinguish these pixels judged as backgrounds in the current iteration. To better stand out the target spectra from the background clutter, HSI spectral input and the given target spectrum are whitened and then used to construct the MF in the current iteration. Finally, we provide the corresponding proofs for the convergence of the output and suppression criterion. Experimental results on three classical hyperspectral datasets confirm that the proposed method performs better than some traditional and recently proposed methods.

Feature Channel Expansion and Background Suppression as the Enhancement for Infrared Pedestrian Detection.

Pedestrian detection is an important task in many intelligent systems, particularly driver assistance systems. Recent studies on pedestrian detection in infrared (IR) imagery have employed data-driven approaches. However, two problems in deep learning-based detection are the implicit performance and time-consuming training. In this paper, a novel channel expansion technique based on feature fusion is proposed to enhance the IR imagery and accelerate the training process. Besides, a novel background suppression method is proposed to stimulate the attention principle of human vision and shrink the region of detection. A precise fusion algorithm is designed to combine the information from different visual saliency maps in order to reduce the effect of truncation and miss detection. Four different experiments are performed from various perspectives in order to gauge the efficiency of our approach. The experimental results show that the Mean Average Precisions (mAPs) of four different datasets have been increased by 5.22% on average. The results prove that background suppression and suitable feature expansion will accelerate the training process and enhance the performance of IR image-based deep learning models.

Wide Field Spectral Imaging with Shifted Excitation Raman Difference Spectroscopy Using the Nod and Shuffle Technique.

Wide field Raman imaging using the integral field spectroscopy approach was used as a fast, one shot imaging method for the simultaneous collection of all spectra composing a Raman image. For the suppression of autofluorescence and background signals such as room light, shifted excitation Raman difference spectroscopy (SERDS) was applied to remove background artifacts in Raman spectra. To reduce acquisition times in wide field SERDS imaging, we adapted the nod and shuffle technique from astrophysics and implemented it into a wide field SERDS imaging setup. In our adapted version, the nod corresponds to the change in excitation wavelength, whereas the shuffle corresponds to the shifting of charges up and down on a Charge-Coupled Device (CCD) chip synchronous to the change in excitation wavelength. We coupled this improved wide field SERDS imaging setup to diode lasers with 784.4/785.5 and 457.7/458.9 nm excitation and applied it to samples such as paracetamol and aspirin tablets, polystyrene and polymethyl methacrylate beads, as well as pork meat using multiple accumulations with acquisition times in the range of 50 to 200 ms. The results tackle two main challenges of SERDS imaging: gradual photobleaching changes the autofluorescence background, and multiple readouts of CCD detector prolong the acquisition time.

Enabling free-breathing background suppressed renal pCASL using fat imaging and retrospective motion correction.

PURPOSE: For free-breathing renal perfusion imaging using arterial spin labeling (ASL), retrospective image realignment has been found essential to reduce subtraction artifacts and, independently, background suppression has been demonstrated to reduce physiologic noise. However, negative results on ASL precision and accuracy have been reported for the combination of both. In this study, the effect of background suppression -level in combination with image registration on free-breathing renal ASL signal quality, with registration either on ASL-images themselves or guided by additionally acquired fat-images, was investigated. The results from free-breathing acquisitions were compared with the reference paced-breathing motion compensation strategy. METHODS: Pseudocontinuous ASL (pCASL) data with additional fat-images were acquired from 10 subjects at 1.5T with varying background suppression levels during free-breathing and paced-breathing. Images were registered using the ASL-images themselves (ASLReg) or using their corresponding fat-images (FatReg). Temporal signal-to-noise ratio (tSNR) served to evaluate precision and perfusion weighted signal (PWS) to assess accuracy. RESULTS: In combination with image registration, background suppression significantly improved tSNR by 50% (P < .05). For heavy suppression, ASLReg and FatReg showed similar performance in terms of tSNR and PWS. Background suppression with two inversion pulses induced a small, nonsignificant (P > .05) PWS reduction, but increased PWS accuracy. When applying heavy background suppression, free-breathing acquisitions resulted in similar ASL-quality to paced-breathing acquisitions. CONCLUSION: Background suppression was found beneficial for free-breathing renal pCASL precision without compromising accuracy, despite motion challenges. In combination with ASLReg or FatReg, background suppression enabled clinically viable free-breathing renal pCASL.

A framework for motion correction of background suppressed arterial spin labeling perfusion images acquired with simultaneous multi-slice EPI.

PURPOSE: When using simultaneous multi-slice (SMS) EPI for background suppressed (BGS) arterial spin labeling (ASL), correction of through-plane motion could introduce artefacts, because the slices with most effective BGS are adjacent to slices with the least BGS. In this study, a new framework is presented to correct for such artefacts. METHODS: The proposed framework consists of 3 steps: (1) homogenization of the static tissue signal over the different slices to eliminate most inter-slice differences because of different levels of BGS, (2) application of motion correction, and (3) extraction of a perfusion-weighted signal using a general linear model. The proposed framework was evaluated by simulations and a functional ASL study with intentional head motion. RESULTS: Simulation studies demonstrated that the strong signal differences between slices with the most and least effective BGS caused sub-optimal estimation of motion parameters when through-plane motion was present. Although use of the M0 image as the reference for registration allowed 82% improvement of motion estimation for through-plane motion, it still led to residual subtraction errors caused by different static tissue signal between control and label because of different BGS levels. By using our proposed framework, those problems were minimized, and the accuracy of CBF estimation was improved. Moreover, the functional ASL study showed improved detection of visual and motor activation when applying the framework as compared to conventional motion correction, as well as when motion correction was completely omitted. CONCLUSION: When combining BGS-ASL with SMS-EPI, particular attention is needed to avoid artefacts introduced by motion correction. With the proposed framework, these issues are minimized.