Recent Developments in Speeding up Prostate MRI.

The increasing incidence of prostate cancer cases worldwide has led to a tremendous demand for multiparametric MRI (mpMRI). In order to relieve the pressure on healthcare, reducing mpMRI scan time is necessary. This review focuses on recent techniques proposed for faster mpMRI acquisition, specifically shortening T2W and DWI sequences while adhering to the PI-RADS (Prostate Imaging Reporting and Data System) guidelines. Speeding up techniques in the reviewed studies rely on more efficient sampling of data, ranging from the acquisition of fewer averages or b-values to adjustment of the pulse sequence. Novel acquisition methods based on undersampling techniques are often followed by suitable reconstruction methods typically incorporating synthetic priori information. These reconstruction methods often use artificial intelligence for various tasks such as denoising, artifact correction, improvement of image quality, and in the case of DWI, for the generation of synthetic high b-value images or apparent diffusion coefficient maps. Reduction of mpMRI scan time is possible, but it is crucial to maintain diagnostic quality, confirmed through radiological evaluation, to integrate the proposed methods into the standard mpMRI protocol. Additionally, before clinical integration, prospective studies are recommended to validate undersampling techniques to avoid potentially inaccurate results demonstrated by retrospective analysis. This review provides an overview of recently proposed techniques, discussing their implementation, advantages, disadvantages, and diagnostic performance according to PI-RADS guidelines compared to conventional methods. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Technology and Tool Development for BACPAC: Qualitative and Quantitative Analysis of Accelerated Lumbar Spine MRI with Deep-Learning Based Image Reconstruction at 3T.

OBJECTIVES: To evaluate whether combining fast acquisitions with deep-learning reconstruction can provide diagnostically useful images and quantitative assessment comparable to standard-of-care acquisitions for lumbar spine magnetic resonance imaging (MRI). METHODS: Eighteen patients were imaged with both standard protocol and fast protocol using reduced signal averages, each protocol including sagittal fat-suppressed T2-weighted, sagittal T1-weighted, and axial T2-weighted 2D fast spin-echo sequences. Fast-acquisition data was additionally reconstructed using vendor-supplied deep-learning reconstruction with three different noise reduction factors. For qualitative analysis, standard images as well as fast images with and without deep-learning reconstruction were graded by three radiologists on five different categories. For quantitative analysis, convolutional neural networks were applied to sagittal T1-weighted images to segment intervertebral discs and vertebral bodies, and disc heights and vertebral body volumes were derived. RESULTS: Based on noninferiority testing on qualitative scores, fast images without deep-learning reconstruction were inferior to standard images for most categories. However, deep-learning reconstruction improved the average scores, and noninferiority was observed over 24 out of 45 comparisons (all with sagittal T2-weighted images while 4/5 comparisons with sagittal T1-weighted and axial T2-weighted images). Interobserver variability increased with 50 and 75% noise reduction factors. Deep-learning reconstructed fast images with 50% and 75% noise reduction factors had comparable disc heights and vertebral body volumes to standard images (r2≥ 0.86 for disc heights and r2≥ 0.98 for vertebral body volumes). CONCLUSIONS: This study demonstrated that deep-learning-reconstructed fast-acquisition images have the potential to provide noninferior image quality and comparable quantitative assessment to standard clinical images.

Diagnostic performance of total-body 18F-FDG PET/CT with fast 2-min acquisition for liver tumours: comparison with conventional PET/CT.

PURPOSE: To comparatively evaluate the diagnostic performances of total-body 18F-fluorodeoxyglucose positron-emission tomography/computed tomography (18F-FDG PET/CT) with fast 2-min acquisition and conventional PET/CT in liver cancer patients. METHODS: This study included 156 patients with liver tumours. Seventy-eight patients underwent total-body PET/CT. PET raw data were reconstructed using acquisition durations of 2 min (G2) and 15 min (G15). Another 78 patients with liver lesions (control patients) underwent conventional uMI780 PET/CT (G780). All patients were evaluated based on TNM staging. The maximum tumour standardized uptake value (tumour SUVmax), mean normal liver SUV (SUVmean), and tumour SUVmax-to-liver SUVmean ratio (TLR) were determined for all patients. G15 data were used as the reference in the lesion detectability analysis. The diagnostic performances of PET/CT in terms of visual parameters and of PET in terms of semi-quantitative parameters such as SUVmax and TLR were evaluated. Receiver operating characteristics (ROC) curve analysis of SUVmax and TLR at G2 was performed. Pathologic findings of surgical specimens served as the gold standard for all patients. RESULTS: The lesions found in G15 were also noted in G2; three lymph nodes were missed in G2. However, no significant difference was found in the TNM stage among G2, G15, and G780. For benign and malignant lesions, the liver SUVmean in G2 and G15 was higher than that in G780 (all P < 0.05). The tumour SUVmax and TLR in G2 were equivalent to those in G15 and G780 regardless of whether the lesions were benign or malignant. ROC curve analysis (SUVmax cutoff: 4.34, TLR cutoff: 1.34) demonstrated that G2 also had good sensitivity in detecting liver cancer. CONCLUSION: The diagnostic performance of total-body PET/CT in G2 was comparable to that in G15 among liver cancer patients. Further, the diagnostic efficiency of total-body PET/CT imaging with fast 2-min acquisition and conventional PET/CT was similar.

Total-body 18F-FDG PET/CT scan in oncology patients: how fast could it be?

PURPOSE: The aim of the study was to determine a faster PET acquisition protocol for a total-body PET/CT scanner by assessing the image quality that is equivalent to a conventional digital PET/CT scanner from both a phantom and a clinical perspective. METHODS: A phantom study using a NEMA/IEC NU-2 body phantom was first performed in both a total-body PET/CT (uEXPLORER) and a routine digital PET/CT (uMI 780), with a hot sphere to background activity concentration ratio of 4:1. The contrast recovery coefficient (CRC), background variability (BV), and recovery coefficient (RC: RCmax and RCmean) were assessed in the uEXPLORER with different scanning durations and reconstruction protocols, which were compared to those acquired from the uMI 780 with clinical acquisition settings. The coefficient of variation (COV) of the uMI 780 with clinical settings was calculated and used as a threshold reference to determine the optimized scanning duration and reconstruction protocol for the uEXPLORER. The obtained protocol from the phantom study was subsequently tested and validated in 30 oncology patients. Images acquired from the uMI 780 with 2-3 min per bed position were referred as G780 and served as the reference for comparison. All PET raw data from the uEXPLORER were reconstructed using the data-cutting technique to simulate a 30-s, 45-s, or 60-s acquisition duration, respectively. The iterations were 2 and 3 for the uEXPLORER, referred as G30s_3i, G45s_2i, G45s_3i, G60s_2i, and G60s_3i, respectively. A 5-point Likert scale was used in the qualitative analysis to assess the image quality. The image quality was also evaluated by the liver COV, the lesion target-to-background ratio (TBR), and the lesion signal-to-noise ratio (SNR). RESULTS: In the phantom study, CRC, BV, RCmax, and RCmean in the uEXPLORER with different scanning durations and reconstruction iterations were compared with those in the uMI 780 with clinical settings. A minor fluctuation was found among different scanning durations. COV of the uMI 780 with clinical settings was 11.6%, and a protocol with a 30-45-s scanning duration and 2 or 3 iterations for the uEXPLORER was found to provide an equivalent image quality as the uMI 780. An almost perfect agreement was shown with a kappa value of 0.875. The qualitative score of the G30s_3i in the uEXPLORER was inferior to the G780 reference (p = 0.001); however, the scores of other groups in the uEXPLORER with a 45-s and above acquisition time were higher than the G780 in the uMI 780. In quantitative analysis, the delay time between the two scans in the two orders was not significantly different. There was no significant difference of the liver COV between the G780 and G30s_3i (p = 0.162). A total of 33 lesions were analyzed in the clinical patient study. There was no significant difference in lesion TBR between the reference G780 and the G45s_2i obtained from the uEXPLORER (p = 0.072), while the latter showed a higher lesion SNR value compared to that in uMI 780 with clinical settings (p < 0.001). CONCLUSIONS: This study showed that a fast PET protocol with a 30-45-s acquisition time in the total-body uEXPLORER PET/CT can provide an equivalent image quality as the conventional digital uMI 780 PET/CT with longer clinical acquisition settings.

Whole brain measurements of the positive BOLD response variability during a finger tapping task at 7 T show regional differences in its profiles.

PURPOSE: The positive BOLD response can vary across brain regions. Here, the positive BOLD responses of motor regions, including the cerebellum, were investigated by fast fMRI acquisition. METHODS: The participants were asked to perform an event-related finger-tapping task in a 7T MRI scanner during a fast 3D-EPI controlled aliasing in parallel imaging acquisition protocol (CAIPI; TR = 399 ms). The positive BOLD responses of 6 motor regions were extracted and their timings and shapes measured. RESULTS: Compared with other brain regions, the positive BOLD responses in the cerebellum and secondary somatosensory cortex showed delayed onsets, but no differences were observed for the time to-peak. Additionally, variations of the undershoot and main peak amplitudes were also observed, and undershoot was quasi-absent in the cerebellum. CONCLUSION: This study confirms that care should be taken when drawing conclusions about neuronal activity from the BOLD signal, particularly for the cerebellum.

Low SAR 31 P (multi-echo) spectroscopic imaging using an integrated whole-body transmit coil at 7T.

Phosphorus (31 P) MRSI provides opportunities to monitor potential biomarkers. However, current applications of 31 P MRS are generally restricted to relatively small volumes as small coils are used. Conventional surface coils require high energy adiabatic RF pulses to achieve flip angle homogeneity, leading to high specific absorption rates (SARs), and occupy space within the MRI bore. A birdcage coil behind the bore cover can potentially reduce the SAR constraints massively by use of conventional amplitude modulated pulses without sacrificing patient space. Here, we demonstrate that the integrated 31 P birdcage coil setup with a high power RF amplifier at 7 T allows for low flip angle excitations with short repetition time (TR ) for fast 3D chemical shift imaging (CSI) and 3D T1 -weighted CSI as well as high flip angle multi-refocusing pulses, enabling multi-echo CSI that can measure metabolite T2 , over a large field of view in the body. B1+ calibration showed a variation of only 30% in maximum B1 in four volunteers. High signal-to-noise ratio (SNR) MRSI was obtained in the gluteal muscle using two fast in vivo 3D spectroscopic imaging protocols, with low and high flip angles, and with multi-echo MRSI without exceeding SAR levels. In addition, full liver MRSI was achieved within SAR constraints. The integrated 31 P body coil allowed for fast spectroscopic imaging and successful implementation of the multi-echo method in the body at 7 T. Moreover, no additional enclosing hardware was needed for 31 P excitation, paving the way to include larger subjects and more space for receiver arrays. The increase in possible number of RF excitations per scan time, due to the improved B1+ homogeneity and low SAR, allows SNR to be exchanged for spatial resolution in CSI and/or T1 weighting by simply manipulating TR and/or flip angle to detect and quantify ratios from different molecular species.

Inner-volume echo volumar imaging (IVEVI) for robust fetal brain imaging.

PURPOSE: Fetal functional MRI studies using conventional 2-dimensional single-shot echo-planar imaging sequences may require discarding a large data fraction as a result of fetal and maternal motion. Increasing the temporal resolution using echo volumar imaging (EVI) could provide an effective alternative strategy. Echo volumar imaging was combined with inner volume (IV) imaging (IVEVI) to locally excite the fetal brain and acquire full 3-dimensional images, fast enough to freeze most fetal head motion. METHODS: IVEVI was implemented by modifying a standard multi-echo echo-planar imaging sequence. A spin echo with orthogonal excitation and refocusing ensured localized excitation. To introduce T2* weighting and to save time, the k-space center was shifted relative to the spin echo. Both single and multi-shot variants were tested. Acoustic noise was controlled by adjusting the amplitude and switching frequency of the readout gradient. Image-based shimming was used to minimize B0 inhomogeneities within the fetal brain. RESULTS: The sequence was first validated in an adult. Eight fetuses were scanned using single-shot IVEVI at a 3.5 × 3.5 × 5.0 mm3 resolution with a readout duration of 383 ms. Multishot IVEVI showed reduced geometric distortions along the second phase-encode direction. CONCLUSIONS: Fetal EVI remains challenging. Although effective echo times comparable to the T2* values of fetal cortical gray matter at 3 T could be achieved, controlling acoustic noise required longer readouts, leading to substantial distortions in single-shot images. Although multishot variants enabled us to reduce susceptibility-induced geometric distortions, sensitivity to motion was increased. Future studies should therefore focus on improvements to multishot variants. Magn Reson Med 80:279-285, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Spin-Noise-Detected Two-Dimensional Nuclear Magnetic Resonance at Triple Sensitivity.

A major breakthrough in speed and sensitivity of 2 D spin-noise-detected NMR is achieved owing to a new acquisition and processing scheme called "double block usage" (DBU) that utilizes each recorded noise block in two independent cross-correlations. The mixing, evolution, and acquisition periods are repeated head-to-tail without any recovery delays and well-known building blocks of multidimensional NMR (constant-time evolution and quadrature detection in the indirect dimension as well as pulsed field gradients) provide further enhancement and artifact suppression. Modified timing of the receiver electronics eliminates spurious random excitation. We achieve a threefold sensitivity increase over the original snHMQC (spin-noise-detected heteronuclear multiple quantum correlation) experiment (K. Chandra et al., J. Phys. Chem. Lett. 2013, 4, 3853) and demonstrate the feasibility of spin-noise-detected long-range correlation.

Real-time MRI guidance of cardiac interventions.

Cardiac magnetic resonance imaging (MRI) is appealing to guide complex cardiac procedures because it is ionizing radiation-free and offers flexible soft-tissue contrast. Interventional cardiac MR promises to improve existing procedures and enable new ones for complex arrhythmias, as well as congenital and structural heart disease. Guiding invasive procedures demands faster image acquisition, reconstruction and analysis, as well as intuitive intraprocedural display of imaging data. Standard cardiac MR techniques such as 3D anatomical imaging, cardiac function and flow, parameter mapping, and late-gadolinium enhancement can be used to gather valuable clinical data at various procedural stages. Rapid intraprocedural image analysis can extract and highlight critical information about interventional targets and outcomes. In some cases, real-time interactive imaging is used to provide a continuous stream of images displayed to interventionalists for dynamic device navigation. Alternatively, devices are navigated relative to a roadmap of major cardiac structures generated through fast segmentation and registration. Interventional devices can be visualized and tracked throughout a procedure with specialized imaging methods. In a clinical setting, advanced imaging must be integrated with other clinical tools and patient data. In order to perform these complex procedures, interventional cardiac MR relies on customized equipment, such as interactive imaging environments, in-room image display, audio communication, hemodynamic monitoring and recording systems, and electroanatomical mapping and ablation systems. Operating in this sophisticated environment requires coordination and planning. This review provides an overview of the imaging technology used in MRI-guided cardiac interventions. Specifically, this review outlines clinical targets, standard image acquisition and analysis tools, and the integration of these tools into clinical workflow. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017;46:935-950.

Dynamic diffusion-tensor measurements in muscle tissue using the single-line multiple-echo diffusion-tensor acquisition technique at 3T.

When diffusion biomarkers display transient changes, i.e. in muscle following exercise, traditional diffusion-tensor imaging (DTI) methods lack the temporal resolution to resolve the dynamics. This article presents an MRI method for dynamic diffusion-tensor acquisitions on a clinical 3T scanner. This method, the Single-Line Multiple-Echo Diffusion-Tensor Acquisition Technique (SL-MEDITATE), achieves a high temporal resolution (4 s) by rapid diffusion encoding through the acquisition of multiple echoes with unique diffusion sensitization and limiting the readout to a single line volume. The method is demonstrated in a rotating anisotropic phantom, a flow phantom with adjustable flow speed and in vivo skeletal calf muscle of healthy volunteers following a plantar flexion exercise. The rotating and flow-varying phantom experiments show that SL-MEDITATE correctly identifies the rotation of the first diffusion eigenvector and the changes in diffusion-tensor parameter magnitudes, respectively. Immediately following exercise, the in vivo mean diffusivity (MD) time courses show, before the well-known increase, an initial decrease that is not typically observed in traditional DTI. In conclusion, SL-MEDITATE can be used to capture transient changes in tissue anisotropy in a single line. Future progress might allow for dynamic DTI when combined with appropriate k-space trajectories and compressed sensing reconstruction.

The efficacy of short acquisition time using 18F-FDG total-body PET/CT for the identification of pediatric epileptic foci.

BACKGROUND: 18F-FDG positron emission tomography (PET) plays a crucial part in the evaluation for pediatric epileptic patients prior to therapy. Short-term scanning holds significant importance, especially for pediatrics epileptic individuals who exhibited involuntary movements. The aim was to evaluate the effects of short acquisition time on image quality and lesion detectability in pediatric epileptic patients using total-body (TB) PET/CT. A total of 25 pediatric patients who underwent TB PET/CT using uEXPLORER scanner with an 18F-FDG administered dose of 3.7 MBq/kg and an acquisition time of 600 s were retrospectively enrolled. Short acquisition times (60 s, 150 and 300 s) were simulated by truncating PET data in list mode to reduce count density. Subjective image quality was scored on a 5-point scale. Regions of interest analysis of suspected epileptogenic zones (EZs), corresponding locations contralateral to EZs, and healthy cerebellar cortex were used to compare the semi-quantitative uptake indices of short-time images and then were compared with 600 s images. The comparison of EZs detectability based on time-dependent PET images was performed. RESULTS: Our study demonstrated that a short acquisition time of 150 s is sufficient to maintain subjective image quality and lesion significance. Statistical analysis revealed no significant difference in subjective PET image quality between imaging at 300 s and 150 s (P > 0.05). The overall impression scores of image quality and lesion conspicuity in G60s were both greater than 3 (overall quality, 3.21 ± 0.46; lesion conspicuity, 4.08 ± 0.74). As acquisition time decreased, the changes of SUVmax and SD in the cerebellar cortex gradually increased (P < 0.01). There was no significant difference in asymmetry index (AI) difference between the groups and the AIs of EZs were > 15% in all groups. In 26 EZs of 25 patients, the lesion detection rate was still 100% when the time was reduced to 60 s. CONCLUSIONS: This study proposed that TB PET/CT acquisition time could be reduced to 60 s with acceptable lesion detectability. Furthermore, it was suggested that a 150 s acquisition time would be sufficient to achieve diagnostic performance and image quality for children with epilepsy.

Sub-minute acquisition with deep learning-based image filter in the diagnosis of colorectal cancers using total-body 18F-FDG PET/CT.

BACKGROUND: This study aimed to retrospectively evaluate the feasibility of total-body 18F-FDG PET/CT ultrafast acquisition combined with a deep learning (DL) image filter in the diagnosis of colorectal cancers (CRCs). METHODS: The clinical and preoperative imaging data of patients with CRCs were collected. All patients underwent a 300-s list-mode total-body 18F-FDG PET/CT scan. The dataset was divided into groups with acquisition durations of 10, 20, 30, 60, and 120 s. PET images were reconstructed using ordered subset expectation maximisation, and post-processing filters, including a Gaussian smoothing filter with 3 mm full width at half maximum (3 mm FWHM) and a DL image filter. The effects of the Gaussian and DL image filters on image quality, detection rate, and uptake value of primary and liver metastases of CRCs at different acquisition durations were compared using a 5-point Likert scale and semi-quantitative analysis, with the 300-s image with a Gaussian filter as the standard. RESULTS: All 34 recruited patients with CRCs had single colorectal lesions, and the diagnosis was verified pathologically. Of the total patients, 11 had liver metastases, and 113 liver metastases were detected. The 10-s dataset could not be evaluated due to high noise, regardless of whether it was filtered by Gaussian or DL image filters. The signal-to-noise ratio (SNR) of the liver and mediastinal blood pool in the images acquired for 10, 20, 30, and 60 s with a Gaussian filter was lower than that of the 300-s images (P < 0.01). The DL filter significantly improved the SNR and visual image quality score compared to the Gaussian filter (P < 0.01). There was no statistical difference in the SNR of the liver and mediastinal blood pool, SUVmax and TBR of CRCs and liver metastases, and the number of detectable liver metastases between the 20- and 30-s DL image filter and 300-s images with the Gaussian filter (P > 0.05). CONCLUSIONS: The DL filter can significantly improve the image quality of total-body 18F-FDG PET/CT ultrafast acquisition. Deep learning-based image filtering methods can significantly reduce the noise of ultrafast acquisition, making them suitable for clinical diagnosis possible.

Moment-based representation of the diffusion inside the brain from reduced DMRI acquisitions: Generalized AMURA.

AMURA (Apparent Measures Using Reduced Acquisitions) was originally proposed as a method to infer micro-structural information from single-shell acquisitions in diffusion MRI. It reduces the number of samples needed and the computational complexity of the estimation of diffusion properties of tissues by assuming the diffusion anisotropy is roughly independent on the b-value. This simplification allows the computation of simplified expressions and makes it compatible with standard acquisition protocols commonly used even in clinical practice. The present work proposes an extension of AMURA that allows the calculation of general moments of the diffusion signals that can be applied to describe the diffusion process with higher accuracy. We provide simplified expressions to analytically compute a set of scalar indices as moments of arbitrary orders over either the whole 3-D space, particular directions, or particular planes. The existing metrics previously proposed for AMURA (RTOP, RTPP and RTAP) are now special cases of this generalization. An extensive set of experiments is performed on public data and a clinical clase acquired with a standard type acquisition. The new metrics provide additional information about the diffusion processes inside the brain.

Anisotropy measure from three diffusion-encoding gradient directions.

We propose a method that can provide information about the anisotropy and orientation of diffusion in the brain from only 3 orthogonal gradient directions without imposing additional assumptions. The method is based on the Diffusion Anisotropy (DiA) that measures the distance from a diffusion signal to its isotropic equivalent. The original formulation based on a Spherical Harmonics basis allows to go down to only 3 orthogonal directions in order to estimate the measure. In addition, an alternative simplification and a color-coding representation are also proposed. Acquisitions from a publicly available database are used to test the viability of the proposal. The DiA succeeded in providing anisotropy information from the white matter using only 3 diffusion-encoding directions. The price to pay for such reduced acquisition is an increment in the variability of the data and a subestimation of the metric on those tracts not aligned with the acquired directions. Nevertheless, the calculation of anisotropy information from DMRI is feasible using fewer than 6 gradient directions by using DiA. The method is totally compatible with existing acquisition protocols, and it may provide complementary information about orientation in fast diffusion acquisitions.

Fast four-dimensional cone-beam computed tomography reconstruction using deformable convolutional networks.

BACKGROUND: Although four-dimensional cone-beam computed tomography (4D-CBCT) is valuable to provide onboard image guidance for radiotherapy of moving targets, it requires a long acquisition time to achieve sufficient image quality for target localization. To improve the utility, it is highly desirable to reduce the 4D-CBCT scanning time while maintaining high-quality images. Current motion-compensated methods are limited by slow speed and compensation errors due to the severe intraphase undersampling. PURPOSE: In this work, we aim to propose an alternative feature-compensated method to realize the fast 4D-CBCT with high-quality images. METHODS: We proposed a feature-compensated deformable convolutional network (FeaCo-DCN) to perform interphase compensation in the latent feature space, which has not been explored by previous studies. In FeaCo-DCN, encoding networks extract features from each phase, and then, features of other phases are deformed to those of the target phase via deformable convolutional networks. Finally, a decoding network combines and decodes features from all phases to yield high-quality images of the target phase. The proposed FeaCo-DCN was evaluated using lung cancer patient data. RESULTS: (1) FeaCo-DCN generated high-quality images with accurate and clear structures for a fast 4D-CBCT scan; (2) 4D-CBCT images reconstructed by FeaCo-DCN achieved 3D tumor localization accuracy within 2.5 mm; (3) image reconstruction is nearly real time; and (4) FeaCo-DCN achieved superior performance by all metrics compared to the top-ranked techniques in the AAPM SPARE Challenge. CONCLUSION: The proposed FeaCo-DCN is effective and efficient in reconstructing 4D-CBCT while reducing about 90% of the scanning time, which can be highly valuable for moving target localization in image-guided radiotherapy.

A Fast Protocol for Multiparametric Characterisation of Diffusion in the Brain and Brain Tumours.

Multi-parametric tissue characterisation is demonstrated using a 4-minute protocol based on diffusion trace acquisitions. Three diffusion regimes are covered simultaneously: pseudo-perfusion, Gaussian, and non-Gaussian diffusion. The clinical utility of this method for fast multi-parametric mapping for brain tumours is explored. A cohort of 17 brain tumour patients was measured on a 3T hybrid MR-PET scanner with a standard clinical MRI protocol, to which the proposed multi-parametric diffusion protocol was subsequently added. For comparison purposes, standard perfusion and a full diffusion kurtosis protocol were acquired. Simultaneous amino-acid (18F-FET) PET enabled the identification of active tumour tissue. The metrics derived from the proposed protocol included perfusion fraction, pseudo-diffusivity, apparent diffusivity, and apparent kurtosis. These metrics were compared to the corresponding metrics from the dedicated acquisitions: cerebral blood volume and flow, mean diffusivity and mean kurtosis. Simulations were carried out to assess the influence of fitting methods and noise levels on the estimation of the parameters. The diffusion and kurtosis metrics obtained from the proposed protocol show strong to very strong correlations with those derived from the conventional protocol. However, a bias towards lower values was observed. The pseudo-perfusion parameters showed very weak to weak correlations compared to their perfusion counterparts. In conclusion, we introduce a clinically applicable protocol for measuring multiple parameters and demonstrate its relevance to pathological tissue characterisation.

Estimation of Spatiotemporal Sensitivity Using Band-limited Signals with No Additional Acquisitions for k-t Parallel Imaging.

PURPOSE: Dynamic MR techniques, such as cardiac cine imaging, benefit from shorter acquisition times. The goal of the present study was to develop a method that achieves short acquisition times, while maintaining a cost-effective reconstruction, for dynamic MRI. k - t sensitivity encoding (SENSE) was identified as the base method to be enhanced meeting these two requirements. METHODS: The proposed method achieves a reduction in acquisition time by estimating the spatiotemporal (x - f) sensitivity without requiring the acquisition of the alias-free signals, typical of the k - t SENSE technique. The cost-effective reconstruction, in turn, is achieved by a computationally efficient estimation of the x - f sensitivity from the band-limited signals of the aliased inputs. Such band-limited signals are suitable for sensitivity estimation because the strongly aliased signals have been removed. RESULTS: For the same reduction factor 4, the net reduction factor 4 for the proposed method was significantly higher than the factor 2.29 achieved by k - t SENSE. The processing time is reduced from 4.1 s for k - t SENSE to 1.7 s for the proposed method. The image quality obtained using the proposed method proved to be superior (mean squared error [MSE] ± standard deviation [SD] = 6.85 ± 2.73) compared to the k - t SENSE case (MSE ± SD = 12.73 ± 3.60) for the vertical long-axis (VLA) view, as well as other views. CONCLUSION: In the present study, k - t SENSE was identified as a suitable base method to be improved achieving both short acquisition times and a cost-effective reconstruction. To enhance these characteristics of base method, a novel implementation is proposed, estimating the x - f sensitivity without the need for an explicit scan of the reference signals. Experimental results showed that the acquisition, computational times and image quality for the proposed method were improved compared to the standard k - t SENSE method.

Evaluation of noise and blur effects with SIRT-FISTA-TV reconstruction algorithm: Application to fast environmental transmission electron tomography.

Fast tomography in Environmental Transmission Electron Microscopy (ETEM) is of a great interest for in situ experiments where it allows to observe 3D real-time evolution of nanomaterials under operating conditions. In this context, we are working on speeding up the acquisition step to a few seconds mainly with applications on nanocatalysts. In order to accomplish such rapid acquisitions of the required tilt series of projections, a modern 4K high-speed camera is used, that can capture up to 100 images per second in a 2K binning mode. However, due to the fast rotation of the sample during the tilt procedure, noise and blur effects may occur in many projections which in turn would lead to poor quality reconstructions. Blurred projections make classical reconstruction algorithms inappropriate and require the use of prior information. In this work, a regularized algebraic reconstruction algorithm named SIRT-FISTA-TV is proposed. The performance of this algorithm using blurred data is studied by means of a numerical blur introduced into simulated images series to mimic possible mechanical instabilities/drifts during fast acquisitions. We also present reconstruction results from noisy data to show the robustness of the algorithm to noise. Finally, we show reconstructions with experimental datasets and we demonstrate the interest of fast tomography with an ultra-fast acquisition performed under environmental conditions, i.e. gas and temperature, in the ETEM. Compared to classically used SIRT and SART approaches, our proposed SIRT-FISTA-TV reconstruction algorithm provides higher quality tomograms allowing easier segmentation of the reconstructed volume for a better final processing and analysis.

Three-minute SPECT/CT is sufficient for the assessment of bone metastasis as add-on to planar bone scintigraphy: prospective head-to-head comparison to 11-min SPECT/CT.

BACKGROUND: The aim of this study is to assess whether ultra-fast acquisition SPECT/CT (UF-SPECT/CT) can replace standard SPECT/CT (std-SPECT/CT) as "add-on" to whole-body bone scintigraphy (WB-BS) for the investigation of bone metastases. Consecutive cancer patients referred for WB-BS who underwent SPECT/CT in addition to WB-BS were included. Std-SPECT, UF-SPECT, and low-dose CT were performed (std-SPECT: matrix 128 × 128, zoom factor 1, 20 s/view, 32 views; UF-SPECT: identical parameters except for 10 s/view and 16 views, reducing the acquisition time from 11 to 3 min). A consensus diagnosis was reached by two observers for each set of images (WB-BS + standard SPECT/CT or WB-BS + UF-SPECT/CT) using a three-category evaluation scale: M0: no bone metastases; M1: bone metastases; and Me: equivocal findings. RESULTS: Among the 104 included patients, most presented with prostate cancer (n = 71) or breast cancer (n = 28). Using WB-BS + std-SPECT/CT, 71 (68%) patients were classified as M0, 19 (18%) as M1, and 14 (14%) as Me. Excellent agreement was observed between WB-BS + std-SPECT/CT and WB-BS + UF-SPECT/CT using the three-category scale: kappa = 0.91 (95% CI 0.84-0.97). No difference in observer agreement between cancer types was detected. SPECT/CT provided a definitive classification in 90 of 104 cases in which WB-BS was not entirely diagnostic. CONCLUSIONS: To investigate potential bone metastases, UF-SPECT/CT can be conducted as add-on to WB-BS to notably reduce the SPECT acquisition time without compromising diagnostic confidence.

Nuisance Regression of High-Frequency Functional Magnetic Resonance Imaging Data: Denoising Can Be Noisy.

Recently, emerging studies have demonstrated the existence of brain resting-state spontaneous activity at frequencies higher than the conventional 0.1 Hz. A few groups utilizing accelerated acquisitions have reported persisting signals beyond 1 Hz, which seems too high to be accommodated by the sluggish hemodynamic process underpinning blood oxygen level-dependent contrasts (the upper limit of the canonical model is ∼0.3 Hz). It is thus questionable whether the observed high-frequency (HF) functional connectivity originates from alternative mechanisms (e.g., inflow effects, proton density changes in or near activated neural tissue) or rather is artificially introduced by improper preprocessing operations. In this study, we examined the influence of a common preprocessing step-whole-band linear nuisance regression (WB-LNR)-on resting-state functional connectivity (RSFC) and demonstrated through both simulation and analysis of real dataset that WB-LNR can introduce spurious network structures into the HF bands of functional magnetic resonance imaging (fMRI) signals. Findings of present study call into question whether published observations on HF-RSFC are partly attributable to improper data preprocessing instead of actual neural activities.