Arterial input function estimation compensating for inflow and partial voluming in dynamic contrast-enhanced MRI

Both inflow and the partial volume effect (PVE) are sources of error when measuring the arterial input function (AIF) in dynamic contrast-enhanced (DCE) MRI. This is relevant, as errors in the AIF can propagate into pharmacokinetic parameter estimations from the DCE data. A method was introduced for flow correction by estimating and compensating the number of the perceived pulse of spins during inflow. We hypothesized that the PVE has an impact on concentration–time curves similar to inflow. Therefore, we aimed to study the efficiency of this method to compensate for both effects simultaneously. We first simulated an AIF with different levels of inflow and PVE contamination. The peak, full width at half-maximum (FWHM), and area under curve (AUC) of the reconstructed AIFs were compared with the true (simulated) AIF. In clinical data, the PVE was included in AIFs artificially by averaging the signal in voxels surrounding a manually selected point in an artery. Subsequently, the artificial partial volume AIFs were corrected and compared with the AIF from the selected point. Additionally, corrected AIFs from the internal carotid artery (ICA), the middle cerebral artery (MCA), and the venous output function (VOF) estimated from the superior sagittal sinus (SSS) were compared. As such, we aimed to investigate the effectiveness of the correction method with different levels of inflow and PVE in clinical data. The simulation data demonstrated that the corrected AIFs had only marginal bias in peak value, FWHM, and AUC. Also, the algorithm yielded highly correlated reconstructed curves over increasingly larger neighbourhoods surrounding selected arterial points in clinical data. Furthermore, AIFs measured from the ICA and MCA produced similar peak height and FWHM, whereas a significantly larger peak and lower FWHM was found compared with the VOF. Our findings indicate that the proposed method has high potential to compensate for PVE and inflow simultaneously. The corrected AIFs could thereby provide a stable input source for DCE analysis.</p

Analysis of osteoarthritis in a mouse model of the progeroid human DNA repair syndrome trichothiodystrophy

The increasing average age in developed societies is paralleled by an increase in the prevalence of many age-related diseases such as osteoarthritis (OA), which is characterized by deformation of the joint due to cartilage damage and increased turnover of subchondral bone. Consequently, deficiency in DNA repair, often associated with premature aging, may lead to increased pathology of these two tissues. To examine this possibility, we analyzed the bone and cartilage phenotype of male and female knee joints derived from 52- to 104-week-old WT C57Bl/6 and trichothiodystrophy (TTD) mice, who carry a defect in the nucleotide excision repair pathway and display many features of premature aging. Using micro-CT, we found bone loss in all groups of 104-week-old compared to 52-week-old mice. Cartilage damage was mild to moderate in all mice. Surprisingly, female TTD mice had less cartilage damage, proteoglycan depletion, and osteophytosis compared to WT controls. OA severity in males did not significantly differ between genotypes, although TTD males had less osteophytosis. These results indicate that in premature aging TTD mice age-related changes in cartilage were not more severe compared to WT mice, in striking contrast with bone and many other tissues. This segmental aging character may be explained by a difference in vasculature and thereby oxygen load in cartilage and bone. Alternatively, a difference in impact of an anti-aging response, previously found to be triggered by accumulation of DNA damage, might help explain why female mice were protected from cartilage damage. These findings underline the exceptional segmental nature of progeroid conditions and provide an explanation for pro- and anti-aging features occurring in the same individual

Activation of the unfolded protein response and granulovacuolar degeneration are not common features of human prion pathology

Human prion diseases are fatal neurodegenerative disorders with a genetic, sporadic or infectiously acquired aetiology. Neuropathologically, human prion diseases are characterized by deposition of misfolded prion protein and neuronal loss. In post-mortem brain tissue from patients with other neurodegenerative diseases characterized by protein misfolding, including Alzheimer’s disease (AD) and frontotemporal lobar degeneration with tau pathology (FTLD-tau), increased activation of the unfolded protein response (UPR) has been observed. The UPR is a cellular stress response that copes with the presence of misfolded proteins. Recent studies have indicated that UPR activation is also involved in experimental models of prion disease and have suggested intervention in the UPR as a therapeutic strategy. On the other hand, it was previously shown that the active form of the UPR stress sensor dsRNA-activated protein kinase-like ER kinase (PERK) is not increased in post-mortem brain tissue samples from human prion disease cases. In the present study, we assessed the active form of another UPR stress sensor, inositol-requiring enzyme 1α (IRE1α), in human post-mortem frontal cortex of a large cohort of sporadic, inherited and acquired prion disease patients (n = 47) and non-neurological controls. Immunoreactivity for phosphorylated IRE1α was not increased in prion disease cases compared with non-neurological controls. In addition, immunoreactivity for phosphorylated PERK was unaltered in human prion disease cases included in the current cohort. Moreover, no difference in the extent of granulovacuolar degeneration, a pathological feature associated with the presence of UPR activation markers, was detected. Our data indicate that, in contrast to AD and primary tauopathies, activation of the UPR is not a common feature of human prion pathology

CT radiation dose reduction in patients with total hip arthroplasties using model-based iterative reconstruction and orthopaedic metal artefact reduction

Objective: To evaluate the impact of radiation dose reduction on image quality in patients with metal-on-metal total hip arthroplasties (THAs) using model-based iterative reconstruction (MBIR) combined with orthopaedic metal artefact reduction (O-MAR). Materials and methods: Patients with metal-on-metal THAs received a pelvic CT with a full (FD) and a reduced radiation dose (RD) with −20%, −40%, −57%, or −80% CT radiation dose respectively, when assigned to group 1, 2, 3, or 4 respectively. FD acquisitions were reconstructed with iterative reconstruction, iDose 4 . RD acquisitions were additionally reconstructed with iterative model-based reconstruction (IMR) levels 1–3 with different levels of noise suppression. CT numbers, noise and contrast-to-noise ratios were measured in muscle, fat and bladder. Subjective image quality was evaluated on seven aspects including artefacts, osseous structures, prosthetic components and soft tissues. Results: Seventy-six patients were randomly assigned to one of the four groups. While reducing radiation dose by 20%, 40%, 57%, or 80% in combination with IMR, CT numbers remained constant. Compared with iDose 4 , the noise decreased (p < 0.001) and contrast-to-noise ratios increased (p < 0.001) with IMR. O-MAR improved CT number accuracy in the bladder and reduced noise in the bladder, muscle and fat (p < 0.01). Subjective image quality was rated lower on RD IMR images than FD iDose 4 images on all seven aspects (p < 0.05) and was not related to the applied radiation dose reduction. Conclusion: In RD IMR with O-MAR images, CT numbers remained constant, noise decreased and contrast-to-noise ratios between muscle and fat increased compared with FD iDose 4 with O-MAR images in patients with metal-on-metal THAs. Subjective image quality reduced, regardless of the degree of radiation dose reduction

Is AI the way forward for reducing metal artifacts in CT? development of a generic deep learning-based method and initial evaluation in patients with sacroiliac joint implants

Purpose: To develop a deep learning-based metal artifact reduction technique (DL-MAR) and quantitatively compare metal artifacts on DL-MAR-corrected CT-images, orthopedic metal artifact reduction (O-MAR)-corrected CT-images and uncorrected CT-images after sacroiliac (SI) joint fusion. Methods: DL-MAR was trained on CT-images with simulated metal artifacts. Pre-surgery CT-images and uncorrected, O-MAR-corrected and DL-MAR-corrected post-surgery CT-images of twenty-five patients undergoing SI joint fusion were retrospectively obtained. Image registration was applied to align pre-surgery with post-surgery CT-images within each patient, allowing placement of regions of interest (ROIs) on the same anatomical locations. Six ROIs were placed on the metal implant and the contralateral side in bone lateral of the SI joint, the gluteus medius muscle and the iliacus muscle. Metal artifacts were quantified as the difference in Hounsfield units (HU) between pre- and post-surgery CT-values within the ROIs on the uncorrected, O-MAR-corrected and DL-MAR-corrected images. Noise was quantified as standard deviation in HU within the ROIs. Metal artifacts and noise in the post-surgery CT-images were compared using linear multilevel regression models. Results: Metal artifacts were significantly reduced by O-MAR and DL-MAR in bone (p < 0.001), contralateral bone (O-MAR: p = 0.009; DL-MAR: p < 0.001), gluteus medius (p < 0.001), contralateral gluteus medius (p < 0.001), iliacus (p < 0.001) and contralateral iliacus (O-MAR: p = 0.024; DL-MAR: p < 0.001) compared to uncorrected images. Images corrected with DL-MAR resulted in stronger artifact reduction than images corrected with O-MAR in contralateral bone (p < 0.001), gluteus medius (p = 0.006), contralateral gluteus medius (p < 0.001), iliacus (p = 0.017), and contralateral iliacus (p < 0.001). Noise was reduced by O-MAR in bone (p = 0.009) and gluteus medius (p < 0.001) while noise was reduced by DL-MAR in all ROIs (p < 0.001) in comparison to uncorrected images. Conclusion: DL-MAR showed superior metal artifact reduction compared to O-MAR in CT-images with SI joint fusion implants

Computed Tomography Imaging of a Hip Prosthesis Using Iterative Model-Based Reconstruction and Orthopaedic Metal Artefact Reduction: A Quantitative Analysis

To quantify the combined use of iterative model-based reconstruction (IMR) and orthopaedic metal artefact reduction (O-MAR) in reducing metal artefacts and improving image quality in a total hip arthroplasty phantom. Scans acquired at several dose levels and kVps were reconstructed with filtered back-projection (FBP), iterative reconstruction (iDose) and IMR, with and without O-MAR. Computed tomography (CT) numbers, noise levels, signal-to-noise-ratios and contrast-to-noise-ratios were analysed. Iterative model-based reconstruction results in overall improved image quality compared to iDose and FBP (P < 0.001). Orthopaedic metal artefact reduction is most effective in reducing severe metal artefacts improving CT number accuracy by 50%, 60%, and 63% (P < 0.05) and reducing noise by 1%, 62%, and 85% (P < 0.001) whereas improving signal-to-noise-ratios by 27%, 47%, and 46% (P < 0.001) and contrast-to-noise-ratios by 16%, 25%, and 19% (P < 0.001) with FBP, iDose, and IMR, respectively. The combined use of IMR and O-MAR strongly improves overall image quality and strongly reduces metal artefacts in the CT imaging of a total hip arthroplasty phanto

Diffusion-weighted MRI with deep learning for visualizing treatment results of MR-guided HIFU ablation of uterine fibroids

OBJECTIVES: No method is available to determine the non-perfused volume (NPV) repeatedly during magnetic resonance-guided high-intensity focused ultrasound (MR-HIFU) ablations of uterine fibroids, as repeated acquisition of contrast-enhanced T1-weighted (CE-T1w) scans is inhibited by safety concerns. The objective of this study was to develop and test a deep learning-based method for translation of diffusion-weighted imaging (DWI) into synthetic CE-T1w scans, for monitoring MR-HIFU treatment progression. METHODS: The algorithm was retrospectively trained and validated on data from 33 and 20 patients respectively who underwent an MR-HIFU treatment of uterine fibroids between June 2017 and January 2019. Postablation synthetic CE-T1w images were generated by a deep learning network trained on paired DWI and reference CE-T1w scans acquired during the treatment procedure. Quantitative analysis included calculation of the Dice coefficient of NPVs delineated on synthetic and reference CE-T1w scans. Four MR-HIFU radiologists assessed the outcome of MR-HIFU treatments and NPV ratio based on the synthetic and reference CE-T1w scans. RESULTS: Dice coefficient of NPVs was 71% (± 22%). The mean difference in NPV ratio was 1.4% (± 22%) and not statistically significant (p = 0.79). Absolute agreement of the radiologists on technical treatment success on synthetic and reference CE-T1w scans was 83%. NPV ratio estimations on synthetic and reference CE-T1w scans were not significantly different (p = 0.27). CONCLUSIONS: Deep learning-based synthetic CE-T1w scans derived from intraprocedural DWI allow gadolinium-free visualization of the predicted NPV, and can potentially be used for repeated gadolinium-free monitoring of treatment progression during MR-HIFU therapy for uterine fibroids. KEY POINTS: • Synthetic CE-T1w scans can be derived from diffusion-weighted imaging using deep learning. • Synthetic CE-T1w scans may be used for visualization of the NPV without using a contrast agent directly after MR-HIFU ablations of uterine fibroids

Acute effects of Delta 9-tetrahydrocannabinol (THC) on resting state brain function and their modulation by COMT genotype

Cannabis produces a broad range of acute, dose-dependent psychotropic effects. Only a limited number of neuroimaging studies have mapped these effects by examining the impact of cannabis on resting state brain neurophysiology. Moreover, how genetic variation influences the acute effects of cannabis on resting state brain function is unknown. Here we investigated the acute effects of ∆9-tetrahydrocannabinol (THC), the main psychoactive constituent of cannabis, on resting state brain neurophysiology, and their modulation by catechol-methyl-transferase (COMT) Val158Met genotype. Thirty-nine healthy volunteers participated in a pharmacological MRI study, where we applied Arterial Spin Labelling (ASL) to measure perfusion and functional MRI to assess resting state connectivity. THC increased perfusion in bilateral insula, medial superior frontal cortex, and left middle orbital frontal gyrus. This latter brain area showed significantly decreased connectivity with the precuneus after THC administration. THC effects on perfusion in the left insula were significantly related to subjective changes in perception and relaxation. These findings indicate that THC enhances metabolism and thus neural activity in the salience network. Furthermore, results suggest that recruitment of brain areas within this network is involved in the acute effects of THC. Resting state perfusion was modulated by COMT genotype, indicated by a significant interaction effect between drug and genotype on perfusion in the executive network, with increased perfusion after THC in Val/Met heterozygotes only. This finding suggests that prefrontal dopamine levels are involved in the susceptibility to acute effects of cannabis

Diffusion-weighted MRI with deep learning for visualizing treatment results of MR-guided HIFU ablation of uterine fibroids

ObjectivesNo method is available to determine the non-perfused volume (NPV) repeatedly during magnetic resonance-guided high-intensity focused ultrasound (MR-HIFU) ablations of uterine fibroids, as repeated acquisition of contrast-enhanced T1-weighted (CE-T1w) scans is inhibited by safety concerns. The objective of this study was to develop and test a deep learning-based method for translation of diffusion-weighted imaging (DWI) into synthetic CE-T1w scans, for monitoring MR-HIFU treatment progression. MethodsThe algorithm was retrospectively trained and validated on data from 33 and 20 patients respectively who underwent an MR-HIFU treatment of uterine fibroids between June 2017 and January 2019. Postablation synthetic CE-T1w images were generated by a deep learning network trained on paired DWI and reference CE-T1w scans acquired during the treatment procedure. Quantitative analysis included calculation of the Dice coefficient of NPVs delineated on synthetic and reference CE-T1w scans. Four MR-HIFU radiologists assessed the outcome of MR-HIFU treatments and NPV ratio based on the synthetic and reference CE-T1w scans. ResultsDice coefficient of NPVs was 71% (& PLUSMN; 22%). The mean difference in NPV ratio was 1.4% (& PLUSMN; 22%) and not statistically significant (p = 0.79). Absolute agreement of the radiologists on technical treatment success on synthetic and reference CE-T1w scans was 83%. NPV ratio estimations on synthetic and reference CE-T1w scans were not significantly different (p = 0.27). ConclusionsDeep learning-based synthetic CE-T1w scans derived from intraprocedural DWI allow gadolinium-free visualization of the predicted NPV, and can potentially be used for repeated gadolinium-free monitoring of treatment progression during MR-HIFU therapy for uterine fibroids