With the expanding applications of magnetic resonance-guided high-intensity focused ultrasound (MR-HIFU), there is an urgent need for a convenient, reliable, and fast acoustic pressure field measurement method to aid treatment protocol design, ensure consistent and safe operation of the transducer, and facilitate regulatory approval of new techniques. Herein, we report a method for acoustic pressure field characterization of MR-HIFU systems with multi-element phased array transducers. This method integrates fiber-optic hydrophone measurements and electronic steering of the ultrasound beam with MRI-assisted HIFU focus alignment to the fiber tip. A clinical MR-HIFU system (Sonalleve V2, Profound Medical Inc., Mississauga, Canada) was used to assess the proposed method. A fiber-optic hydrophone was submerged in a degassed water bath, and the fiber tip location was traced using MRI. Subsequently, the nominal transducer focal point indicated on the MR-HIFU therapy planning software was positioned at the fiber tip, and the HIFU focus was electronically steered around the fiber tip within a 3D volume for 3D pressure field mapping, eliminating the need for an additional, expensive, and MRI-compatible 3D positioning stage. The peak positive and negative pressures were measured at the focus and validated using a standard hydrophone measurement setup outside the MRI magnet room. We found that the initial MRI-assisted HIFU focus alignment had an average offset of 2.23 ± 1.33 mm from the fiber tip as identified by the 3D pressure field mapping. MRI guidance and electronic beam steering allowed 3D focus localization within ~ 1 h, i.e., faster than the typical time required using the standard laboratory setup (~ 3–4 h). Acoustic pressures measured using the proposed method were not significantly different from those obtained with the standard laboratory hydrophone measurements. In conclusion, our method offers a convenient, reliable, and fast acoustic pressure field characterization tool for MR-HIFU systems with phased array transducers.

Background: The 18F‐AV‐1451 radioligand enables in‐vivo identification of tau neurofibrillary tangles that are considered as biomarkers of neurodegeneration in Alzheimer Disease (AD). However, off‐target radioligand binding is also observed in basal ganglia, known as an iron‐rich region. Hence, it is important to distinguish between radioligand‐identified tissue neurodegeneration and iron‐related radioligand binding effects. Our aim is to answer this question by using previously developed quantitative Gradient Recalled Echo (qGRE) MRI technique sensitive to morphological and microstructural tissue neurodegeneration, as well as to iron deposition.Methods: Sixty‐eight participants (ages: 72.1y ± 8.2y; 15 with amyloid status (Aβ) positive and cognitive dementia rating (CDR®)>0; 16 with Aβ‐positive and CDR®=0; and 37 with Aβ‐negative and CDR®=0) recruited by Knight ADRC underwent 18F‐AV‐1451 PET imaging and qGRE MRI imaging (3T Siemens MRI scanners). As previously demonstrated, the R2t* metric of qGRE signal is sensitive to both, pre‐atrophic neuronal loss (low R2t*, a.k.a. Dark Matter (DM), and nonheme iron accumulation (High R2t*). The histogram characterizing distribution of R2t* values across the brain (Figure 1) allows the quantification of DM (R2t*28s^‐1). 18F‐AV‐1451 binding was characterized in terms of Standardized Uptake Value Ratio (SUVR). All results were summarized in brain regions segmented using FreeSurfer.Results: (Figures 2 and 3): Tissue volumes exhibited moderate negative associations with ¹⁸F‐AV‐1451 SUVRs in MTL (R²=0.21), temporal lobe (R²=0.17), and parietal lobe (R²=0.11). The linear model that includes IDS and DM exhibited positive association with ¹⁸F‐AV‐1451 SUVRs only in MTL (R²=0.18). The linear mixed model that includes volume, IDS and DM metrics showed the improved association between predicted and actual 18F‐AV‐1451 tracer retention in the MTL (R²=0.34), the temporal lobe (R²=0.23), and the parietal lobe (R²=0.12).Conclusion: Our results suggest that the ¹⁸F‐AV‐1451 binding is modulated not only by neurodegeneration but also by elevated levels of iron deposition. More detail studies are required to answer the question whether the iron effect is concomitant or contributes to neurodegeneration through ferroptosis. The qGRE‐based multiparametric approach allowing simultaneous identification of pre‐atrophic neurodegeneration and iron deposition has a potential to contribute to understanding of this intricate relationship.

Background: Early identification of neuropathological changes in Alzheimer's disease (AD) is essential for the development and application of disease‐modifying therapies. Currently, brain accumulation of β‐amyloid (Aβ) and/or CSF amyloid level reduction are considered among the earliest AD biomarkers, and both are indirectly associated with brain neurodegeneration. Direct methods identifying neurodegeneration are usually based on MRI measurements of brain volume reduction (atrophy). Herein we use a quantitative Gradient‐Recalled‐Echo (qGRE) MRI method, providing a sensitive biomarker (Neuronal Density Index, NDI) for quantifying the neuronal cell damage (pre‐atrophic neurodegeneration) before it can be detected by atrophy measurements. While atrophy appears at late stages of amyloidosis, our results show that NDI can serve as a biomarker of pre‐atrophic neurodegeneration coincident with early amyloidosis. Methods: 143 participants, ages between 48 and 88 (69.5±7.1; 85 females; 58 males), recruited through the Knight ADRC and SEABIRD study, provided informed consent. qGRE and PET amyloid ¹¹ C‐PiB images were acquired concurrently. For each participant, amyloidosis level was identified by mean cortical standard uptake ratio (mcSUVr), while NDI was quantitated in all cortical FreeSurfer‐defined brain regions. Group differences between participants’ NDIs were established for a range of mcSUVR cutoff levels. Results: A standard mcSUVR cutoff of 1.42 is usually used to separate participants into Aβ(‐) and Aβ(+) groups. Results in Figure 1 show dramatic pre‐atrophic neurodegeneration in Aβ(+) group with NDI significantly decreased in multiple brain regions, while significant volume changes were observed only in frontal and insular regions. Furthermore, the NDI exhibited significant group differences in multiple brain regions even when mcSUVR cut‐off threshold was reduced to 1.2. Importantly, NDI exhibited significant changes in medial temporal lobe even when comparing groups with mcSUVr < 1.2 and mcSUVR between 1.2 and 1.6 (Figure 2). In contrast, volume measurements did not show significant differences until participants with mcSUVR greater than 3.4 were included in the analysis. Conclusion: qGRE‐based NDI provides in‐vivo microstructural brain neurodegeneration distribution in early amyloid pathology where atrophy is not detectable. Although the NDI parameter reflects neuronal cell density, presence of inflammation can also reduce the NDI value. Thus, further investigations are required to understand the microstructural nature of pre‐atrophic neurodegeneration.

With the expanding use and applications of MR-HIFU in both thermal-based and pressure-based therapies, there is an urgent need to develop acoustic field characterization and quality assurance (QA) tools for MR-HIFU systems. We developed a method for quick and reliable acoustic field assessment inside the magnet bore of a clinical MRI system. A fiber-optic hydrophone with a 2-m long fiber was fixed inside a water tank that was placed on the HIFU table above the acoustic window. The long fiber allowed the MRI-incompatible hydrophone control unit to be located outside the MRI suite. MR images of the fiber were used to position the HIFU focus approximately at the tip of the fiber. The HIFU focus was electronically steered within a 5×5×5mm³ volume in synchronization with hydrophone measurements. The HIFU focus location was then identified based on the 3D field scans. Peak positive and negative pressures were measured at the focus at various nominal acoustic powers. Furthermore, focus dimensions and spatial peak pulse average intensities were assessed. The results were compared to and were consistent with standard hydrophone measurements outside the MRI suite. This method provides a useful tool for field characterization and QA of MR-HIFU systems.

Background: The 18F‐AV‐1451 radioligand enables in‐vivo identification of tau neurofibrillary tangles that are considered as biomarkers of neurodegeneration in Alzheimer Disease (AD). However, off‐target radioligand binding is also observed in basal ganglia, known as an iron‐rich region. Hence, it is important to distinguish between radioligand‐identified tissue neurodegeneration and iron‐related radioligand binding effects. Our aim is to answer this question by using previously developed quantitative Gradient Recalled Echo (qGRE) MRI technique sensitive to morphological and microstructural tissue neurodegeneration, as well as to iron deposition.Methods: Sixty‐eight participants (ages: 72.1y ± 8.2y; 15 with amyloid status (Aß) positive and cognitive dementia rating (CDR®)>0; 16 with Aß‐positive and CDR® = 0; and 37 with Aß‐negative and CDR® = 0) recruited by Knight ADRC underwent 18F‐AV‐1451 PET imaging and qGRE MRI imaging (3T Siemens MRI scanners). As previously demonstrated, the R2t* metric of qGRE signal is sensitive to both, pre‐atrophic neuronal loss (low R2t*, a.k.a. Dark Matter (DM), and nonheme iron accumulation (High R2t*). The histogram characterizing distribution of R2t* values across the brain (Figure 1) allows the quantification of DM (R2t*28s‐1). 18F‐AV‐1451 binding was characterized in terms of Standardized Uptake Value Ratio (SUVR). All results were summarized in brain regions segmented using FreeSurfer.Results: (Figures 2 and 3): Tissue volumes exhibited moderate negative associations with 18F‐AV‐1451 SUVRs in MTL (R2 = 0.21), temporal lobe (R2 = 0.17), and parietal lobe (R2 = 0.11). The linear model that includes IDS and DM exhibited positive association with 18F‐AV‐1451 SUVRs only in MTL (R2 = 0.18). The linear mixed model that includes volume, IDS and DM metrics showed the improved association between predicted and actual 18F‐AV‐1451 tracer retention in the MTL (R2 = 0.34), the temporal lobe (R2 = 0.23), and the parietal lobe (R2 = 0.12).Conclusion: Our results suggest that the 18F‐AV‐1451 binding is modulated not only by neurodegeneration but also by elevated levels of iron deposition. More detail studies are required to answer the question whether the iron effect is concomitant or contributes to neurodegeneration through ferroptosis. The qGRE‐based multiparametric approach allowing simultaneous identification of pre‐atrophic neurodegeneration and iron deposition has a potential to contribute to understanding of this intricate relationship.

There is a growing interest in investigating cerebrovascular dysfunction related to early Alzheimer disease (AD) pathology. The objective of this study is to evaluate changes in brain hemodynamic properties in relation to excess of β‐amyloid (Aβ) deposition using the quantitative Gradient Recalled Echo (qGRE) MRI technique. Additionally, we aim to assess brain Aβ status by combining output metrics of qGRE and quantitative susceptibility maps (QSM). 43 amyloid positive (Aβ⁺, mean age 74.28), and 62 amyloid negative (Aβ⁻, mean age 72.02) participants were recruited by Knight ADRC. qGRE and QSM images were obtained from the same 3D multi‐gradient echo sequence using 3T Siemens MRI scanners. R2' metric of qGRE signal defines regional concentration of deoxyhemoglobin (Ulrich&Yablonskiy, MRM‐2016). Combining qGRE and QSM data allows separate evaluation of deoxyhemoglobin and tissue contributions to QSM metric (Yablonskiy, et al, Neuroimage‐2021). The principal component analysis (PCA) for the whole grey matter and lobe‐wise analysis (medial temporal (MTL), temporal, parietal, occipital, and frontal lobes) were used to present data. Statistical analysis for the whole grey matter shows increased deoxyhemoglobin concentration (p = 0.0003) in Aβ⁺ as compared to Aβ⁻ groups. This increased deoxyhemoglobin was detected in all lobes: MTL (p = 0.001), temporal (p = 6.5E‐5), parietal (p = 0.006), occipital (p = 0.02), and frontal (p = 0.008). Data also showed brain‐wide decreased in tissue susceptibility (p = 0.01) in Aβ⁺ as compared to Aβ⁻ groups, though lobe‐wise difference reached statistical significance only in temporal (p = 0.01) and frontal (p = 0.04) lobes. Our results indicate that participants with excessive amyloid deposition exhibit an increased concentration of deoxygenated blood. Importantly, the odds ratio of being amyloid positive in the presence of higher concentration of deoxyhemoglobin is increased by a factor about five. Detail analysis of qGRE data shows that increased concentration of deoxyhemoglobin is mostly caused by increased of the deoxyhemoglobin‐carrying blood vessel network (venous blood and pre‐venous part of the capillary bed). In MTL and temporal lobes qGRE also detected significant increases in oxygen extraction fraction (OEF). Additionally, discovered reduced tissue magnetic susceptibility in Aβ⁺ participants is indicative of increased Aβ deposition, thus providing new MRI‐based tool for identifying brain tissue Aβ status.