Performance evaluation of a 4D similarity filter for dynamic CT angiography imaging of the liver.

BACKGROUND: Dynamic computed tomography (CT) angiography of the abdomen provides perfusion information and characteristics of the tissues present in the abdomen. This information could potentially help characterize liver metastases. However, radiation dose has to be relatively low for the patient, causing the images to have very high noise content. Denoising methods are needed to increase image quality. PURPOSE: The purpose of this study was to investigate the performance, limitations, and behavior of a new 4D filtering method, called the 4D Similarity Filter (4DSF), to reduce image noise in temporal CT data. METHODS: The 4DSF averages voxels with similar time-intensity curves (TICs). Each phase is filtered individually using the information of all phases except for the one being filtered. This approach minimizes the bias toward the noise initially present in this phase. Since the 4DSF does not base similarity on spatial proximity, loss of spatial resolution is avoided. The 4DSF was evaluated on a 12-phase liver dynamic CT angiography acquisition of 52 digital anthropomorphic phantoms, each containing one hypervascular 1 cm lesion with a small necrotic core. The metrics used for evaluation were noise reduction, lesion contrast-to-noise ratio (CNR), CT number accuracy using peak-time and peak-intensity of the TICs, and resolution loss. The results were compared to those obtained by the time-intensity profile similarity (TIPS) filter, which uses the whole TIC for determining similarity, and the combination 4DSF followed by TIPS filter (4DSF + TIPS). RESULTS: The 4DSF alone resulted in a median noise reduction by a factor of 6.8, which is lower than that obtained by the TIPS filter at 8.1, and 4DSF + TIPS at 12.2. The 4DSF increased the median CNR from 0. 44 to 1.85, which is less than the TIPS filter at 2.59 and 4DSF + TIPS at 3.12. However, the peak-intensity accuracy in the TICs was superior for the 4DSF, with a median intensity decrease of -34 HU compared to -88 and -50 HU for the hepatic artery when using the TIPS filter and 4DSF + TIPS, respectively. The median peak-time accuracy was inferior for the 4DSF filter and 4DSF + TIPS, with a time shift of -1 phases for the portal vein TIC compared to no shift in time when using the TIPS. The analysis of the full-width-at-half-maximum (FWHM) of a small artery showed significantly less spatial resolution loss for the 4DSF at 3.2 pixels, compared to the TIPS filter at 4.3 pixels, and 3.4 pixels for the 4DSF + TIPS. CONCLUSION: The 4DSF can reduce noise with almost no resolution loss, making the filter very suitable for denoising 4D CT data for detection tasks, even in very low dose, i.e., very high noise level, situations. In combination with the TIPS filter, the noise reduction can be increased even further.

Deep learning-based low-dose CT simulator for non-linear reconstruction methods.

BACKGROUND: Computer algorithms that simulate lower-doses computed tomography (CT) images from clinical-dose images are widely available. However, most operate in the projection domain and assume access to the reconstruction method. Access to commercial reconstruction methods may often not be available in medical research, making image-domain noise simulation methods useful. However, the introduction of non-linear reconstruction methods, such as iterative and deep learning-based reconstruction, makes noise insertion in the image domain intractable, as it is not possible to determine the noise textures analytically. PURPOSE: To develop a deep learning-based image-domain method to generate low-dose CT images from clinical-dose CT (CDCT) images for non-linear reconstruction methods. METHODS: We propose a fully image domain-based method, utilizing a series of three convolutional neural networks (CNNs), which, respectively, denoise CDCT images, predict the standard deviation map of the low-dose image, and generate the noise power spectra (NPS) of local patches throughout the low-dose image. All three models have U-net-based architectures and are partly or fully three-dimensional. As a use case for this study and with no loss of generality, we use paired low-dose and clinical-dose brain CT scans. A dataset of 326 $\hskip.001pt 326$ paired scans was retrospectively obtained. All images were acquired with a wide-area detector clinical system and reconstructed using its standard clinical iterative algorithm. Each pair was registered using rigid registration to correct for motion between acquisitions. The data was randomly partitioned into training ( 251 $\hskip.001pt 251$ samples), validation ( 25 $\hskip.001pt 25$ samples), and test ( 50 $\hskip.001pt 50$ samples) sets. The performance of each of these three CNNs was validated separately. For the denoising CNN, the local standard deviation decrease, and bias were determined. For the standard deviation map CNN, the real and estimated standard deviations were compared locally. Finally, for the NPS CNN, the NPS of the synthetic and real low-dose noise were compared inside and outside the skull. Two proof-of-concept denoising studies were performed to determine if the performance of a CNN- or a gradient-based denoising filter on the synthetic low-dose data versus real data differed. RESULTS: The denoising network had a median decrease in noise in the cerebrospinal fluid by a factor of 1.71 $1.71$ and introduced a median bias of + 0.7 $ + 0.7$ HU. The network for standard deviation map estimation had a median error of + 0.1 $ + 0.1$ HU. The noise power spectrum estimation network was able to capture the anisotropic and shift-variant nature of the noise structure by showing good agreement between the synthetic and real low-dose noise and their corresponding power spectra. The two proof of concept denoising studies showed only minimal difference in standard deviation improvement ratio between the synthetic and real low-dose CT images with the median difference between the two being 0.0 and +0.05 for the CNN- and gradient-based filter, respectively. CONCLUSION: The proposed method demonstrated good performance in generating synthetic low-dose brain CT scans without access to the projection data or to the reconstruction method. This method can generate multiple low-dose image realizations from one clinical-dose image, so it is useful for validation, optimization, and repeatability studies of image-processing algorithms.

Free-breathing high-resolution respiratory-gated radial stack-of-stars magnetic resonance imaging of the upper abdomen at 7 T.

Ultrahigh field magnetic resonance imaging (MRI) (≥ 7 T) has the potential to provide superior spatial resolution and unique image contrast. Apart from radiofrequency transmit inhomogeneities in the body at this field strength, imaging of the upper abdomen faces additional challenges associated with motion-induced ghosting artifacts. To address these challenges, the goal of this work was to develop a technique for high-resolution free-breathing upper abdominal MRI at 7 T with a large field of view. Free-breathing 3D gradient-recalled echo (GRE) water-excited radial stack-of-stars data were acquired in seven healthy volunteers (five males/two females, body mass index: 19.6-24.8 kg/m2) at 7 T using an eight-channel transceive array coil. Two volunteers were also examined at 3 T. In each volunteer, the liver and kidney regions were scanned in two separate acquisitions. To homogenize signal excitation, the time-interleaved acquisition of modes (TIAMO) method was used with personalized pairs of B1 shims, based on a 23-s Cartesian fast low angle shot (FLASH) acquisition. Utilizing free-induction decay navigator signals, respiratory-gated images were reconstructed at a spatial resolution of 0.8 × 0.8 × 1.0 mm3. Two experienced radiologists rated the image quality and the impact of B1 inhomogeneity and motion-related artifacts on multipoint scales. The images of all volunteers showcased effective water excitation and were accurately corrected for respiratory motion. The impact of B1 inhomogeneity on image quality was minimal, underscoring the efficacy of the multitransmit TIAMO shim. The high spatial resolution allowed excellent depiction of small structures such as the adrenal glands, the proximal ureter, the diaphragm, and small blood vessels, although some streaking artifacts persisted in liver image data. In direct comparisons with 3 T performed for two volunteers, 7-T acquisitions demonstrated increases in signal-to-noise ratio of 77% and 58%. Overall, this work demonstrates the feasibility of free-breathing MRI in the upper abdomen at submillimeter spatial resolution at a magnetic field strength of 7 T.

Development, validation, and simplification of a scanner-specific CT simulator.

BACKGROUND: Simulated computed tomography (CT) images allow for knowledge of the underlying ground truth and for easy variation of imaging conditions, making them ideal for testing and optimization of new applications or algorithms. However, simulating all processes that affect CT images can result in simulations that are demanding in terms of processing time and computer memory. Therefore, it is of interest to determine how much the simulation can be simplified while still achieving realistic results. PURPOSE: To develop a scanner-specific CT simulation using physics-based simulations for the position-dependent effects and shift-invariant image corruption methods for the detector effects. And to investigate the impact on image realism of introducing simplifications in the simulation process that lead to faster and less memory-demanding simulations. METHODS: To make the simulator realistic and scanner-specific, the spatial resolution and noise characteristics, and the exposure-to-detector output relationship of a clinical CT system were determined. The simulator includes a finite focal spot size, raytracing of the digital phantom, gantry rotation during projection acquisition, and finite detector element size. Previously published spectral models were used to model the spectrum for the given tube voltage. The integrated energy at each element of the detector was calculated using the Beer-Lambert law. The resulting angular projections were subsequently corrupted by the detector modulation transfer function (MTF), and by addition of noise according to the noise power spectrum (NPS) and signal mean-variance relationship, which were measured for different scanner settings. The simulated sinograms were reconstructed on the clinical CT system and compared to real CT images in terms of CT numbers, noise magnitude using the standard deviation, noise frequency content using the NPS, and spatial resolution using the MTF throughout the field of view (FOV). The CT numbers were validated using a multi-energy CT phantom, the noise magnitude and frequency were validated with a water phantom, and the spatial resolution was validated with a tungsten wire. These metrics were compared at multiple scanner settings, and locations in the FOV. Once validated, the simulation was simplified by reducing the level of subsampling of the focal spot area, rotation and of detector pixel size, and the changes in MTFs were analyzed. RESULTS: The average relative errors for spatial resolution within and across image slices, noise magnitude, and noise frequency content within and across slices were 3.4%, 3.3%, 4.9%, 3.9%, and 6.2%, respectively. The average absolute difference in CT numbers was 10.2 HU and the maximum was 22.5 HU. The simulation simplification showed that all subsampling can be avoided, except for angular, while the error in frequency at 10% MTF would be maximum 16.3%. CONCLUSION: The simulation of a scanner-specific CT allows for the generation of realistic CT images by combining physics-based simulations for the position-dependent effects and image-corruption methods for the shift-invariant ones. Together with the available ground truth of the digital phantom, it results in a useful tool to perform quantitative analysis of reconstruction or post-processing algorithms. Some simulation simplifications allow for reduced time and computer power requirements with minimal loss of realism.

A Bayesian estimation method for cerebral blood flow measurement by area-detector CT perfusion imaging.

PURPOSE: Bayesian estimation with advanced noise reduction (BEANR) in CT perfusion (CTP) could deliver more reliable cerebral blood flow (CBF) measurements than the commonly used reformulated singular value decomposition (rSVD). We compared the efficacy of CBF measurement by CTP using BEANR and rSVD, evaluating both relative to N-isopropyl-p-[(123) I]- iodoamphetamine (123I-IMP) single-photon emission computed tomography (SPECT) as a reference standard, in patients with cerebrovascular disease. METHODS: Thirty-one patients with suspected cerebrovascular disease underwent both CTP on a 320 detector-row CT system and SPECT. We applied rSVD and BEANR in the ischemic and contralateral regions to create CBF maps and calculate CBF ratios from the ischemic side to the healthy contralateral side (CBF index). The analysis involved comparing the CBF index between CTP methods and SPECT using Pearson's correlation and limits of agreement determined with Bland-Altman analyses, before comparing the mean difference in the CBF index between each CTP method and SPECT using the Wilcoxon matched pairs signed-rank test. RESULTS: The CBF indices of BEANR and 123I-IMP SPECT were significantly and positively correlated (r = 0.55, p < 0.0001), but there was no significant correlation between the rSVD method and SPECT (r = 0.15, p > 0.05). BEANR produced smaller limits of agreement for CBF than rSVD. The mean difference in the CBF index between BEANR and SPECT differed significantly from that between rSVD and SPECT (p < 0.001). CONCLUSIONS: BEANR has a better potential utility for CBF measurement in CTP than rSVD compared to SPECT in patients with cerebrovascular disease.

Abdominopelvic CT Image Quality: Evaluation of Thin (0.5-mm) Slices Using Deep Learning Reconstruction.

BACKGROUND. Because thick-section images (typically 3-5 mm) have low image noise, radiologists typically use them to perform clinical interpretation, although they may additionally refer to thin-section images (typically 0.5-0.625 mm) for problem solving. Deep learning reconstruction (DLR) can yield thin-section images with low noise. OBJECTIVE. The purpose of this study is to compare abdominopelvic CT image quality between thin-section DLR images and thin- and thick-section hybrid iterative reconstruction (HIR) images. METHODS. This retrospective study included 50 patients (31 men and 19 women; median age, 64 years) who underwent abdominopelvic CT between June 15, 2020, and July 29, 2020. Images were reconstructed at 0.5-mm section using DLR and at 0.5-mm and 3.0-mm sections using HIR. Five radiologists independently performed pairwise comparisons (0.5-mm DLR and either 0.5-mm or 3.0-mm HIR) and recorded the preferred image for subjective image quality measures (scale, -2 to 2). The pooled scores of readers were compared with a score of 0 (denoting no preference). Image noise was quantified using the SD of ROIs on regions of homogeneous liver. RESULTS. For comparison of 0.5-mm DLR images and 0.5-mm HIR images, the median pooled score was 2 (indicating a definite preference for DLR) for noise and overall image quality and 1 (denoting a slight preference for DLR) for sharpness and natural appearance. For comparison of 0.5-mm DLR and 3.0-mm HIR, the median pooled score was 1 for the four previously mentioned measures. These assessments were all significantly different (p < .001) from 0. For artifacts, the median pooled score for both comparisons was 0, which was not significant for comparison with 3.0-mm HIR (p = .03) but was significant for comparison with 0.5-mm HIR (p < .001) due to imbalance in scores of 1 (n = 28) and -1 (slight preference for HIR, n = 1). Noise for 0.5-mm DLR was lower by mean differences of 12.8 HU compared with 0.5-mm HIR and 4.4 HU compared with 3.0-mm HIR (both p < .001). CONCLUSION. Thin-section DLR improves subjective image quality and reduces image noise compared with currently used thin- and thick-section HIR, without causing additional artifacts. CLINICAL IMPACT. Although further diagnostic performance studies are warranted, the findings suggest the possibility of replacing current use of both thin- and thick-section HIR with the use of thin-section DLR only during clinical interpretations.

Deep learning-based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms.

OBJECTIVES: To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT). METHODS: Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests. RESULTS: For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively. CONCLUSIONS: With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times. KEY POINTS: • Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.

Improvement of image quality on low-dose dynamic myocardial perfusion computed tomography with a novel 4-dimensional similarity filter.

The aim of this study was to evaluate the effect of a novel 4-dimensional similarity filter (4DSF) on quantitative and qualitative parameters of low-dose dynamic myocardial computed tomography perfusion (CTP) images.In this retrospective study, medical records of 32 patients with suspected or known coronary artery disease who underwent dynamic myocardial CTP at 80 kV were included. The 4DSF reduces noise by averaging voxels that have similar dynamic behavior after adaptive iterative dose reduction 3D (AIDR3D) and deformable image registration were applied. Qualitative (artefact, contour sharpness, and myocardial homogeneity [1 = poor; 2 = intermediate; 3 = good]) and quantitative measurement (standard deviation [SD] and signal-to-noise ratio [SNR]) were compared between the 4DSF and AIDR3D. Contrast-to-noise ratio (CNR) between ischemic and normal remote myocardium was also assessed using myocardial perfusion magnetic resonance imaging as the reference standard in seven patients.The 4DSF was successfully applied to all the images. Improvement in subjective image quality yielded by 4DSF was higher than that yielded by AIDR3D (homogeneity, 1.0 [3 vs 2]; artefact, 1.5 [3 vs 1.5]; P < .001) in all patients. The 4DSF significantly decreased the SD by 59% (AIDR3D vs 4DSF: 33.5 ±â€Š0.4 vs 13.8 ±â€Š0.4, P < .001), increased the SNR by 134% (AIDR3D vs 4DSF: 4.4 ±â€Š0.2 vs 10.3 ±â€Š0.2, P < .001), and increased the CNR by 131% (AIDR3D vs 4DSF: 1.6 ±â€Š0.2 vs 3.7 ±â€Š0.2, P < .001).The 4DSF improved the qualitative and quantitative parameters of low-dose dynamic myocardial CTP images.

Clinical application of four-dimensional noise reduction filtering with a similarity algorithm in dynamic myocardial computed tomography perfusion imaging.

We aimed to evaluate the effects of four-dimensional noise reduction filtering using a similarity algorithm (4D-SF) on the image quality and hemodynamic parameter of dynamic myocardial computed tomography perfusion (CTP). Sixty-eight patients who underwent dynamic myocardial CTP for the assessment of coronary artery disease were enrolled. Dynamic CTP was performed using a 320-row CT with low tube voltage scan (80 kVp). Two different datasets of dynamic CTP were reconstructed using iterative reconstruction (IR) alone and a combination of IR and 4D-SF. Qualitative (5-grade scale) and quantitative image quality scores were assessed, and the CT-derived myocardial blood flow (CT-MBF) was quantified. These results were compared between the two different CTP images. The qualitative image quality in CTP images reconstructed with IR and 4D-SF was significantly higher than that with IR alone (noise score: 4.7 vs. 3.4, p < 0.05). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in CTP images reconstructed with IR and 4D-SF were significantly higher than those with IR alone (SNR: 20.6 vs. 9.7; CNR: 7.9 vs. 3.9, respectively; p < 0.05). There was no significant difference in mean CT-MBF between the two sets of CTP images (3.01 vs. 3.03 mL/g/min, p = 0.1081). 4D-SF showed incremental value in improving image quality in combination with IR without altering CT-MBF quantification in dynamic myocardial CTP imaging with a low tube potential.

Author Correction: White Matter and Gray Matter Segmentation in 4D Computed Tomography.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Observer variability of reference tissue selection for relativecerebral blood volume measurements in glioma patients.

OBJECTIVES: To assess observer variability of different reference tissues used for relative CBV (rCBV) measurements in DSC-MRI of glioma patients. METHODS: In this retrospective study, three observers measured rCBV in DSC-MR images of 44 glioma patients on two occasions. rCBV is calculated by the CBV in the tumour hotspot/the CBV of a reference tissue at the contralateral side for normalization. One observer annotated the tumour hotspot that was kept constant for all measurements. All observers annotated eight reference tissues of normal white and grey matter. Observer variability was evaluated using the intraclass correlation coefficient (ICC), coefficient of variation (CV) and Bland-Altman analyses. RESULTS: For intra-observer, the ICC ranged from 0.50-0.97 (fair-excellent) for all reference tissues. The CV ranged from 5.1-22.1 % for all reference tissues and observers. For inter-observer, the ICC for all pairwise observer combinations ranged from 0.44-0.92 (poor-excellent). The CV ranged from 8.1-31.1 %. Centrum semiovale was the only reference tissue that showed excellent intra- and inter-observer agreement (ICC>0.85) and lowest CVs (<12.5 %). Bland-Altman analyses showed that mean differences for centrum semiovale were close to zero. CONCLUSION: Selecting contralateral centrum semiovale as reference tissue for rCBV provides the lowest observer variability. KEY POINTS: • Reference tissue selection for rCBV measurements adds variability to rCBV measurements. • rCBV measurements vary depending on the choice of reference tissue. • Observer variability of reference tissue selection varies between poor and excellent. • Centrum semiovale as reference tissue for rCBV provides the lowest observer variability.

White Matter and Gray Matter Segmentation in 4D Computed Tomography.

Modern Computed Tomography (CT) scanners are capable of acquiring contrast dynamics of the whole brain, adding functional to anatomical information. Soft tissue segmentation is important for subsequent applications such as tissue dependent perfusion analysis and automated detection and quantification of cerebral pathology. In this work a method is presented to automatically segment white matter (WM) and gray matter (GM) in contrast- enhanced 4D CT images of the brain. The method starts with intracranial segmentation via atlas registration, followed by a refinement using a geodesic active contour with dominating advection term steered by image gradient information, from a 3D temporal average image optimally weighted according to the exposures of the individual time points of the 4D CT acquisition. Next, three groups of voxel features are extracted: intensity, contextual, and temporal. These are used to segment WM and GM with a support vector machine. Performance was assessed using cross validation in a leave-one-patient-out manner on 22 patients. Dice coefficients were 0.81 ± 0.04 and 0.79 ± 0.05, 95% Hausdorff distances were 3.86 ± 1.43 and 3.07 ± 1.72 mm, for WM and GM, respectively. Thus, WM and GM segmentation is feasible in 4D CT with good accuracy.

Interleaving cerebral CT perfusion with neck CT angiography part I. Proof of concept and accuracy of cerebral perfusion values.

OBJECTIVES: We present a novel One-Step-Stroke protocol for wide-detector CT scanners that interleaves cerebral CTP with volumetric neck CTA (vCTA). We evaluate whether the resulting time gap in CTP affects the accuracy of CTP values. METHODS: Cerebral CTP maps were retrospectively obtained from 20 patients with suspicion of acute ischemic stroke and served as the reference standard. To simulate a 4 s gap for interleaving CTP with vCTA, we eliminated one acquisition at various time points of CTP starting from the bolus-arrival-time(BAT). Optimal timing of the vCTA was evaluated. At the time point with least errors, we evaluated elimination of a second time point (6 s gap). RESULTS: Mean absolute percentage errors of all perfusion values remained below 10 % in all patients when eliminating any one time point in the CTP sequence starting from the BAT. Acquiring the vCTA 2 s after reaching a threshold of 70HU resulted in the lowest errors (mean <3.0 %). Eliminating a second time point still resulted in mean errors <3.5 %. CBF/CBV showed no significant differences in perfusion values except MTT. However, the percentage errors were always below 10 % compared to the original protocol. CONCLUSION: Interleaving cerebral CTP with neck CTA is feasible with minor effects on the perfusion values. KEY POINTS: • Removing a single CTP acquisition has minor effects on calculated perfusion values • Calculated perfusion values errors depend on timing of skipping a CTP acquisition • Qualitative evaluation of CTP was not influenced by removing two time points • Neck CTA is optimally timed in the upslope of arterial enhancement.

Interleaving cerebral CT perfusion with neck CT angiography. Part II: clinical implementation and image quality.

OBJECTIVES: Feasibility evaluation of the One-Step Stroke Protocol, which is an interleaved cerebral computed tomography perfusion (CTP) and neck volumetric computed tomography angiography (vCTA) scanning technique using wide-detector computed tomography, and to assess the image quality of vCTA. METHODS: Twenty patients with suspicion of acute ischaemic stroke were prospectively scanned and evaluated with a head and neck CTA and with the One-Step Stroke Protocol. Arterial enhancement and contrast-to-noise ratio (CNR) in the carotid arteries was assessed. Three observers scored artefacts and image quality of the cervical arteries. The total z-coverage was evaluated. RESULTS: Mean enhancement in the carotid bifurcation was rated higher in the vCTA (595 ± 164 HU) than CTA (441 ± 117 HU). CNR was rated higher in vCTA. Image quality scores showed no significant difference in the region of the carotid bifurcation between vCTA and CTA. Lower neck image quality scores were slightly lower for vCTA due to artefacts, although not rated as diagnostically relevant. In ten patients, the origin of the left common carotid artery was missed by 1.6 ± 0.8 cm. Mean patient height was 1.8 ± 0.09 m. Carotid bifurcation and origin of vertebral arteries were covered in all patients. CONCLUSIONS: The One-Step Stroke Protocol is feasible with good diagnostic image quality of vCTA, although full z-coverage is limited in tall patients. KEY POINTS: • Interleaving cerebral CTP with neck CTA (One-Step Stroke Protocol) is feasible • Diagnostic quality of One-Step Stroke Protocol neck CTA is similar to conventional CTA • One-Step Stroke Protocol neck CTA suffers from streak artefacts in the lower neck • A limitation of One-Step Stroke Protocol CTA is lack of coverage in tall patients • Precise planning of One-Step Stroke Protocol neck CTA is necessary in tall patients.

Computed tomography perfusion evaluation after extracranial-intracranial bypass surgery.

OBJECTIVE: Perfusion imaging is increasingly used for postoperative evaluation of extracranial to intracranial (EC-IC) bypass surgery. Altered hemodynamics and delayed arrival of the contrast agent in the area fed by the bypass can influence perfusion measurement. We compared perfusion asymmetry obtained with different algorithms in EC-IC bypass surgery patients. METHODS: We retrospectively identified all patients evaluated with computed tomography perfusion (CTP) between May 2007 and May 2011 after EC-IC bypass surgery at our institution. CTP images were analyzed with three perfusion algorithms that differ among their ability to anticipate for delayed arrival time of contrast material: the delay-sensitive first-moment mean transit time (fMTT), the semi-delay-sensitive standard singular value decomposition (sSVD) and the delay-insensitive block-circulant SVD (bSVD). The interhemispheric difference in bolus arrival time (ΔBAT) was determined to confirm altered hemodynamics. Interhemispheric asymmetry in perfusion values (mean transit time (MTT) difference, cerebral blood flow (CBF) ratio and cerebral blood volume (CBV) ratio) was compared between the three algorithms. Presence of a new infarct in the treated hemisphere was evaluated on follow-up imaging and perfusion asymmetry was compared between patients with and without infarction. RESULTS: Twenty-two patients were included. The median interhemispheric difference in ΔBAT was 0.98 s. The median MTT difference was significantly smaller when calculated with the delay-insensitive algorithm than with the other algorithms (0.44 s versus 0.90 s and 0.93 s, p<0.01). The CBF ratio was similar for all algorithms (111.98 versus 112.59 and 112.60). The CBV ratio was similar for all algorithms (113.20 versus 111.95 and 113.97). There was a significant difference in MTT asymmetry between patients with and without infarction with the delay-insensitive algorithm only (1.57 s versus 0.38 s, p=0.04). CONCLUSION: In patients with EC-IC bypass surgery, delay-sensitive algorithms showed larger MTT asymmetry than delay-insensitive algorithms. Furthermore, only the delay-insensitive method seems to differentiate between patients with and without infarction on follow-up.

A 4D digital phantom for patient-specific simulation of brain CT perfusion protocols.

PURPOSE: Optimizing CT brain perfusion protocols is a challenge because of the complex interaction between image acquisition, calculation of perfusion data, and patient hemodynamics. Several digital phantoms have been developed to avoid unnecessary patient exposure or suboptimum choice of parameters. The authors expand this idea by using realistic noise patterns and measured tissue attenuation curves representing patient-specific hemodynamics. The purpose of this work is to validate that this approach can realistically simulate mean perfusion values and noise on perfusion data for individual patients. METHODS: The proposed 4D digital phantom consists of three major components: (1) a definition of the spatial structure of various brain tissues within the phantom, (2) measured tissue attenuation curves, and (3) measured noise patterns. Tissue attenuation curves were measured in patient data using regions of interest in gray matter and white matter. By assigning the tissue attenuation curves to the corresponding tissue curves within the phantom, patient-specific CTP acquisitions were retrospectively simulated. Noise patterns were acquired by repeatedly scanning an anthropomorphic skull phantom at various exposure settings. The authors selected 20 consecutive patients that were scanned for suspected ischemic stroke and constructed patient-specific 4D digital phantoms using the individual patients' hemodynamics. The perfusion maps of the patient data were compared with the digital phantom data. Agreement between phantom- and patient-derived data was determined for mean perfusion values and for standard deviation in de perfusion data using intraclass correlation coefficients (ICCs) and a linear fit. RESULTS: ICCs ranged between 0.92 and 0.99 for mean perfusion values. ICCs for the standard deviation in perfusion maps were between 0.86 and 0.93. Linear fitting yielded slope values between 0.90 and 1.06. CONCLUSIONS: A patient-specific 4D digital phantom allows for realistic simulation of mean values and standard deviation in perfusion data and makes it possible to retrospectively study how the interaction of patient hemodynamics and scan parameters affects CT perfusion values.

Comparison of partial volume effects in arterial and venous contrast curves in CT brain perfusion imaging.

PURPOSE: In brain CT perfusion (CTP), the arterial contrast bolus is scaled to have the same area under the curve (AUC) as the venous outflow to correct for partial volume effects (PVE). This scaling is based on the assumption that large veins are unaffected by PVE. Measurement of the internal carotid artery (ICA), usually unaffected by PVE due to its large diameter, may avoid the need for partial volume correction. The aims of this work are to examine i) the assumptions behind PVE correction and ii) the potential of selecting the ICA obviating correction for PVE. METHODS: The AUC of the ICA and sagittal sinus were measured in CTP datasets from 52 patients. The AUCs were determined by i) using commercial CTP software based on a Gaussian curve-fitting to the time attenuation curve, and ii) by simple integration of the time attenuation curve over a time interval. In addition, frames acquired up to 3 minutes after first bolus passage were used to examine the ratio of arterial and venous enhancement. The impact of selecting the ICA without PVE correction was illustrated by reporting cerebral blood volume (CBV) measurements. RESULTS: In 49 of 52 patients, the AUC of the ICA was significantly larger than that of the sagittal sinus (p = 0.017). Measured after the first pass bolus, contrast enhancement remained 50% higher in the ICA just after the first pass bolus, and 30% higher 3 minutes later. CBV measurements were significantly lowered when the ICA was used without PVE correction. CONCLUSIONS: Contradicting the assumptions underlying PVE correction, contrast in the ICA was significantly higher than in the sagittal sinus, even 3 minutes after the first pass of the contrast bolus. PVE correction might lead to overestimation of CBV if the CBV is calculated using the AUC of the time attenuation curves.

Radiation dose reduction in cerebral CT perfusion imaging using iterative reconstruction.

OBJECTIVES: To investigate whether iterative reconstruction (IR) in cerebral CT perfusion (CTP) allows for 50% dose reduction while maintaining image quality (IQ). METHODS: A total of 48 CTP examinations were reconstructed into a standard dose (150 mAs) with filtered back projection (FBP) and half-dose (75 mAs) with two strengths of IR (middle and high). Objective IQ (quantitative perfusion values, contrast-to-noise ratio (CNR), penumbra, infarct area and penumbra/infarct (P/I) index) and subjective IQ (diagnostic IQ on a four-point Likert scale and overall IQ binomial) were compared among the reconstructions. RESULTS: Half-dose CTP with high IR level had, compared with standard dose with FBP, similar objective (grey matter cerebral blood volume (CBV) 4.4 versus 4.3 mL/100 g, CNR 1.59 versus 1.64 and P/I index 0.74 versus 0.73, respectively) and subjective diagnostic IQ (mean Likert scale 1.42 versus 1.49, respectively). The overall IQ in half-dose with high IR level was scored lower in 26-31%. Half-dose with FBP and with the middle IR level were inferior to standard dose with FBP. CONCLUSION: With the use of IR in CTP imaging it is possible to examine patients with a half dose without significantly altering the objective and diagnostic IQ. The standard dose with FBP is still preferable in terms of subjective overall IQ in about one quarter of patients. KEY POINTS: • Computed tomography perfusion (CTP) is increasingly important in ischaemia imaging. • Radiation exposure of CTP is a drawback. • Iterative reconstruction (IR) allows reduction of radiation dose in unenhanced head CT. • CTP IR enables 50% dose reduction without altering objective and diagnostic quality.

Timing-invariant imaging of collateral vessels in acute ischemic stroke.

BACKGROUND AND PURPOSE: Although collateral vessels have been shown to be an important prognostic factor in acute ischemic stroke, patients with lack of collaterals on standard imaging techniques may still have good clinical outcome. We postulate that in these cases collateral vessels are present though not visible on standard imaging techniques that are based on a single time frame. METHODS: This study included 40 consecutive patients with acute ischemic stroke with a large-vessel occlusion. Standard computed tomography angiography (CTA, single time frame) and CT perfusion (multiple time frames) were obtained at admission and timing-invariant (TI)-CTA was created from the CT perfusion data. Clinical outcome data (modified Rankin Scale) were assessed at 3-month follow-up. Four experienced observers independently assessed collateral status twice on both standard CTA and TI-CTA in an independent, blinded, randomized manner. Collateral status was rated as good if ≥50% and poor if <50% of collaterals were present compared with the contralateral hemisphere. RESULTS: Collateral status was rated higher on TI-CTA (good in 84%) compared with standard CTA (good in 49%; P<0.001). Thirty-one percent of patients with poor collateral status on standard CTA still had good clinical outcome. All of those patients, however, showed good collaterals on TI-CTA. All cases with poor collateral status rated on TI-CTA had poor clinical outcome. CONCLUSIONS: Collateral vessels may not always be visible on standard single time-frame CTA because of delayed contrast arrival. Future prognostic studies in acute stroke should consider delay-insensitive techniques, such as TI-CTA, instead of standard single time-frame imaging, such as standard CTA.

Multi-sequence whole-brain intracranial vessel wall imaging at 7.0 tesla.

OBJECTIVES: Intracranial vessel wall magnetic resonance imaging (MRI) may improve the diagnosis of vessel wall abnormalities. Current methods are hampered by limited coverage and few contrast weightings. We present a multi-sequence protocol with whole-brain coverage for vessel wall imaging on 7.0-T MRI. METHODS: A modified magnetisation-preparation inversion recovery turbo-spin-echo (MPIR-TSE) sequence was used to obtain proton density (PD)-, T1-, and T2-weighting with 190-mm whole-brain coverage. Three observers independently scored the visibility of arterial vessel walls in five healthy volunteers, and compared the conspicuity and image contrast of all sequences. Clinical applicability was demonstrated in 17 patients with cerebrovascular disease. RESULTS: Conspicuity was good for all acquisitions, with best scores for the original limited-coverage sequence, followed by whole-brain coverage T2-, PD- and T1-weighted sequences, respectively. Mean vessel wall/background MR signal intensity ratios for all whole-brain sequences were similar, with higher scores for the limited-coverage MPIR-TSE sequence. Signal intensity ratios were highest in patients, for the whole-brain T1-weighted sequence. CONCLUSIONS: The whole-brain multi-sequence vessel wall protocol can assess intracranial arterial vessel walls with full brain coverage, for different image contrast weightings. These sequences could eventually characterise intracranial vessel wall abnormalities similar to current techniques for assessing carotid artery plaques. KEY POINTS: - Intracranial vessel wall imaging using MRI improves diagnosis of cerebrovascular diseases. - Conventional 7-T MRI sequences cannot image the whole cerebral arterial tree. - New whole-brain 7-T MRI sequences compare favourably with smaller-coverage sequences. - These whole-brain sequences can demonstrate the entire cerebral arterial tree. - These sequences should help in the diagnosis of vessel wall abnormalities.