Pairs Of Scans Research Articles

Accuracy and Reproducibility of Myocardial Blood Flow Quantification by Single Photon Emission Computed Tomography Imaging in Patients With Known or Suspected Coronary Artery Disease.

Single photon emission computed tomography (SPECT) has limited ability to identify multivessel and microvascular coronary artery disease. Gamma cameras with cadmium zinc telluride detectors allow the quantification of absolute myocardial blood flow (MBF) and myocardial flow reserve (MFR). However, evidence of its accuracy is limited, and of its reproducibility is lacking. We aimed to validate 99mTc-sestamibi SPECT MBF and MFR using standard and spline-fitted reconstruction algorithms compared with 13N-ammonia positron emission tomography in a cohort of patients with known or suspected coronary artery disease and to evaluate the reproducibility of this technique. Accuracy was assessed in 34 participants who underwent dynamic 99mTc-sestamibi SPECT and 13N-ammonia positron emission tomography and reproducibility in 14 participants who underwent 2 99mTc-sestamibi SPECT studies, all within 2 weeks. A rest/pharmacological stress single-day SPECT protocol was performed. SPECT images were reconstructed using a standard ordered subset expectation maximization (OSEM) algorithm with (N=21) and without (N=30) application of spline fitting. SPECT MBF was quantified using a net retention kinetic model' and MFR was derived as the stress/rest MBF ratio. SPECT global MBF with splines showed good correlation with 13N-ammonia positron emission tomography (r=0.81, P<0.001) and MFR estimates (r=0.74, P<0.001). Correlations were substantially weaker for standard reconstruction without splines (r=0.61, P<0.001 and r=0.34, P=0.07, for MBF and MFR, respectively). Reproducibility of global MBF estimates with splines in paired SPECT scans was good (r=0.77, P<0.001), while ordered subset expectation maximization without splines led to decreased MBF (r=0.68, P<0.001) and MFR correlations (r=0.33, P=0.3). There were no significant differences in MBF or MFR between the 2 reproducibility scans independently of the reconstruction algorithm (P>0.05 for all). MBF and MFR quantification using 99mTc-sestamibi cadmium zinc telluride SPECT with spatiotemporal spline fitting improved the correlation with 13N-ammonia positron emission tomography flow estimates and test/retest reproducibility. The use of splines may represent an important step toward the standardization of SPECT flow estimation.

Matched FES and FDG PET imaging in patients with hormone receptor-positive, HER2+ advanced breast cancer.

1042 Background: The recently FDA approved 18F-Fluoroestradiol (FES) is a PET imaging tracer for characterizing disease in patients with ER+ breast cancer. As FES PET enters clinical practice it will be important to establish its utility in the full population of hormone-receptor positive patients, including those with HER2+ tumors. Historically the consensus around ER+/HER+ disease has been that these tumors are primarily driven by HER2, with therapies focused on targeting this pathway. Emerging research suggests that ER+ and HER2+ tumors represent a distinct phenotype, with bidirectional crosstalk between ER and HER2 pathways contributing to resistance to therapies targeting these critical pathways. Methods: Our cross-sectional database of patients with one or more FES scans stretches back to 1996. We selected all patients with HER2+ advanced breast cancer to determine whether ER is functional in the ER+/HER2+ subset. We examined paired FDG and FES scans and recorded SUVmax in matched lesions between the FDG and FES scans. We also looked at a subset of patients who underwent scans at more than one time-points and examined the clinical characteristics of these cases over time. Results: 36 patients with metastatic ER+, HER2+ breast cancer underwent concurrent FDG and FES PET scans between 1996 and 2013. 34 subjects (94%) were female; 32 (89%) were Caucasian, and 4 (11%) were Asian. Eight patients underwent serial scans. A total of 200 metastatic sites were recorded with the majority (67%) being bony lesions. No difference in quantitative FES avidity was observed between soft tissue and osseous sites. Six patients (16%) had negative FES scans despite displaying FDG avid lesions; three patients had at least one negative FES scan on serial scans, and two demonstrated FES-avid lesions with no FDG activity. Average FES SUVmax for positive scans was 3.5, with a range of 0.8 to 10.7. Among eight patients with multiple scans, half had 2 scans, three had 3 scans, and one had 4 scans. In 7/8 patients (88%) FES avidity increased over time even as FDG decreased or stayed stable with treatment; in one, both FES and FDG decreased on follow up scan. Conclusions: In a cohort of ER+, HER2+ patients undergoing FDG and FES PET scans, robust concordance between FDG and FES uptake was observed. FES avidity increased in patients with multiple scans, suggesting that the ER pathway remained active during treatment. The strong FES positivity in many HER2+ patients in this cohort suggests that FES PET could be used to guide patient selection for trials examining deescalated regimens employing a non-chemotherapy partner for HER2-directed therapy or emphasizing more ER-directed therapies such as CDK4/6 inhibitors, which are not currently approved in this population. With the ongoing development of HER2- PET imaging, combination scans could carry the potential for discrimination between sites, possibly serving as a tool to guide biopsy.

Bi-exponential fitting excluding b=0 data improves the scan-rescan stability of liver IVIM parameter measures and particularly so for the perfusion fraction.

A prerequisite to translating intravoxel incoherent motion (IVIM) imaging into meaningful clinical applications is sufficient scan-rescan reproducibility. This study aims to confirm the hypothesis that IVIM data fitting by not using b=0 images will improve the stability of liver IVIM measurement. Healthy volunteers' liver IVIM images were prospectively acquired using a 1.5-T magnet or a 3.0 T with 16 b-values. Repeatability study subjects were scanned twice during the same session, resulted in 35 paired scans for 35 subjects (11 men, mean age: 41.82 years, range: 32-60 years; 24 women, mean age: 42.67 years, range: 20-71 years). IVIM analysis was performed with full-fitting and segmented-fitting with a threshold b-value of 60 s/mm2, and fitting started from b=0 s/mm2 or from b=2 s/mm2. Reproducibility study subjects were scanned and then rescanned with an interval of 5-18 days, resulted in 20 paired scans for 11 subjects (4 men, mean age: 26.25 years, range: 25-27 years; 7 women, mean age: 25.57 years, range: 24-27 years). IVIM analysis was performed with segmented-fitting with a threshold b-value of 50 s/mm2, and fitting started from b=0 s/mm2 or from b=3 s/mm2. Fitting without b=0 data generally improved the repeatability and reproducibility for both PF and Dslow, and particularly so for PF. For with b=0 data segmented fitting repeatability, PF had within-subject standard deviation of 0.019, bland-Atman 75% agreement limit of -31.52% to 28.35%, and ICC of 0.647, while these values were 0.009, -20.78% to 16.86%, and 0.837 for without b=0 analysis. Though the repeatability and reproducibility for Dfast generally also improved, they remained suboptimal. Measurement stability was better for repeatability than for reproducibility. Scan-rescan repeatability and reproducibility of liver IVIM parameters can be improved by fitting without b=0 data, which is particularly so for PF.

FAST CONVERGENCE METHOD FOR GLOBAL OPTIMAL 4DOF REGISTRATION

Abstract. Four degrees of freedom (4DoF) registration is a class of point cloud registration problems for finding a rigid transformation to align two point clouds under the constraint that the rigid transformation is composed of a three-dimensional (3D) translation and 1D rotation. This constraint is suitable to align scan pairs acquired using modern terrestrial Light Detection and Ranging (LiDAR) scanners, the scans of which can share the direction of gravity as the Z-axis due to such scanners using tripods or internal inclinometers. We propose a fast convergence method for global optimal 4DoF registration. The proposed method consists of (i) our newly developed 4DoF registration model formulated as an optimization problem involving the cylindrical norm to measure the distance between two points, and (ii) a fast convergence algorithm to find a global optimal solution of the model. We experimentally demonstrated that the proposed method reduced the number of iterations to convergence and computation time compared with a current 4DoF registration method, especially when the given scan pairs are similar but cannot be aligned, which often appears in registration of multiple point clouds.

Automatic coronary artery calcium scoring on routine chest computed tomography (CT): comparison of a deep learning algorithm and a dedicated calcium scoring CT.

The aim of this study was to investigate the reliability and accuracy of automatic coronary artery calcium (CAC) scoring and risk classification in non-gated, non-contrast chest computed tomography (CT) of different slice thicknesses using a deep learning algorithm. This retrospective study was performed at 2 tertiary hospitals. Paired, dedicated calcium-scoring CT scans and non-gated, non-contrast chest CT scans taken within a month from the same patients were included. Chest CT images were grouped according to the slice thickness (group A: 1 mm; group B: 3 mm). For internal scans, the CAC score manually measured on dedicated calcium scoring CT images was used as the gold standard. The deep learning algorithm for group A was trained using 150 chest CT scans and tested using 144 scans, and that for group B was trained using 170 chest CT scans and tested using 144 scans. The intraclass correlation coefficient (ICC) was used to evaluate the correlation between the algorithm and the gold standard. Agreement between the deep learning algorithm, the manual results on chest CT, and the gold standard was determined by Bland-Altman analysis. Cardiac risk categories were compared. External validation was performed on 334 paired scans from a different organization. A total of 608 internal paired scans (1 mm: 294; 3 mm: 314) of 406 individuals and 334 external paired scans (1 mm: 117; 3 mm: 117) of 117 individuals were included in the analysis. The ICCs between the deep learning algorithm and the gold standard were excellent in both group A (0.90; 95% CI: 0.85-0.93) and group B (0.94; 95% CI: 0.92-0.96). The Bland-Altman plots showed good agreement in both groups. For the cardiovascular risk category, the deep learning algorithm accurately classified 71% of cases in group A and 81% of cases in group B. The Kappa values for risk classification were 0.72 in group A and 0.82 in group B. External validation yielded equally good results. The automatic calculation of CAC score and cardiovascular risk stratification on non-gated chest CT using a deep learning algorithm was reliable and accurate on both 1 and 3 mm scans. Chest CT with a slice thickness of 3 mm was slightly more accurate in CAC detection and risk classification.

Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment

PurposeParametric response mapping (PRM) of high-resolution, paired inspiration and expiration computed tomography (CT) scans is a promising analytical imaging technique that is currently used in diagnostic applications and offers the ability to characterize and quantify certain pulmonary pathologies on a patient-specific basis. As one of the first studies to implement such a technique in the radiation oncology clinic, the goal of this work was to assess the feasibility for PRM analysis to identify pulmonary abnormalities in patients with lung cancer before radiation therapy (RT). Methods and MaterialsHigh-resolution, paired inspiration and expiration CT scans were acquired from 23 patients with lung cancer as part of routine treatment planning CT acquisition. When applied to the paired CT scans, PRM analysis classifies lung parenchyma, on a voxel-wise basis, as normal, small airways disease (SAD), emphysema, or parenchymal disease (PD). PRM classifications were quantified as a percent of total lung volume and were evaluated globally and regionally within the lung. ResultsPRM analysis of pre-RT CT scans was successfully implemented using a workflow that produced patient-specific maps and quantified specific phenotypes of pulmonary abnormalities. Through this study, a large prevalence of SAD and PD was demonstrated in this lung cancer patient population, with global averages of 10% and 17%, respectively. Moreover, PRM-classified normal and SAD in the region with primary tumor involvement were found to be significantly different from global lung values. When present, elevated levels of PD and SAD abnormalities tended to be pervasive in multiple regions of the lung, indicating a large burden of underlying disease. ConclusionsPulmonary abnormalities, as detected by PRM, were characterized in patients with lung cancer scheduled for RT. Although further study is needed, PRM is a highly accessible CT-based imaging technique that has the potential to identify local lung abnormalities associated with chronic obstructive pulmonary disease and interstitial lung disease. Further investigation in the radiation oncology setting may provide strategies for tailoring RT planning and risk assessment based on pre-existing PRM-based pathology.

Synthetic contrast-enhanced computed tomography generation using a deep convolutional neural network for cardiac substructure delineation in breast cancer radiation therapy: a feasibility study

BackgroundAdjuvant radiation therapy improves the overall survival and loco-regional control in patients with breast cancer. However, radiation-induced heart disease, which occurs after treatment from incidental radiation exposure to the cardiac organ, is an emerging challenge. This study aimed to generate synthetic contrast-enhanced computed tomography (SCECT) from non-contrast CT (NCT) using deep learning (DL) and investigate its role in contouring cardiac substructures. We also aimed to determine its applicability for a retrospective study on the substructure volume-dose relationship for predicting radiation-induced heart disease.MethodsWe prepared NCT-CECT cardiac scan pairs of 59 patients. Of these, 35, 4, and 20 pairs were used for training, validation, and testing, respectively. We adopted conditional generative adversarial network as a framework to generate SCECT. SCECT was validated in the following three stages: (1) The similarity between SCECT and CECT was evaluated; (2) Manual contouring was performed on SCECT and CECT with sufficient intervals and based on this, the geometric similarity of cardiac substructures was measured between them; (3) The treatment plan was quantitatively analyzed based on the contours of SCECT and CECT.ResultsWhile the mean values (± standard deviation) of the mean absolute error, peak signal-to-noise ratio, and structural similarity index measure between SCECT and CECT were 20.66 ± 5.29, 21.57 ± 1.85, and 0.77 ± 0.06, those were 23.95 ± 6.98, 20.67 ± 2.34, and 0.76 ± 0.07 between NCT and CECT, respectively. The Dice similarity coefficients and mean surface distance between the contours of SCECT and CECT were 0.81 ± 0.06 and 2.44 ± 0.72, respectively. The dosimetry analysis displayed error rates of 0.13 ± 0.27 Gy and 0.71 ± 1.34% for the mean heart dose and V5Gy, respectively.ConclusionOur findings displayed the feasibility of SCECT generation from NCT and its potential for cardiac substructure delineation in patients who underwent breast radiation therapy.

The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system

SummaryThe respective value of clinical data and CT examinations in predicting COVID-19 progression is unclear, because the CT scans and clinical data previously used are not synchronized in time. To address this issue, we collected 119 COVID-19 patients with 341 longitudinal CT scans and paired clinical data, and we developed an AI system for the prediction of COVID-19 deterioration. By combining features extracted from CT and clinical data with our system, we can predict whether a patient will develop severe symptoms during hospitalization. Complementary to clinical data, CT examinations show significant add-on values for the prediction of COVID-19 progression in the early stage of COVID-19, especially in the 6th to 8th day after the symptom onset, indicating that this is the ideal time window for the introduction of CT examinations. We release our AI system to provide clinicians with additional assistance to optimize CT usage in the clinical workflow.

Ultra-high-resolution Mapping of Cortical Layers 3T-Guided 7T MRI.

7T MRI provides unprecedented resolution for examining human brain anatomy in vivo. For example, 7T MRI enables deep thickness measurement of laminar subdivisions in the right fusiform area. Existing laminar thickness measurement on 7T is labor intensive, and error prone since the visual inspection of the image is typically along one of the three orthogonal planes (axial, coronal, or sagittal view). To overcome this, we propose a new analytics tool that allows flexible quantification of cortical thickness on a 2D plane that is orthogonal to the cortical surface (beyond axial, coronal, and sagittal views) based on the 3D computational surface reconstruction. The proposed method further distinguishes high quality 2D planes and the low-quality ones by automatically inspecting the angles between the surface normals and slice direction. In our approach, we acquired a pair of 3T and 7T scans (same subject). We extracted the brain surfaces from the 3T scan using MaCRUISE and projected the surface to the 7T scan's space. After computing the angles between the surface normals and axial direction vector, we found that 18.58% of surface points were angled at more than 80° with the axial direction vector and had 2D axial planes with visually distinguishable cortical layers. 15.12% of the surface points with normal vectors angled at 30° or lesser with the axial direction, had poor 2D axial slices for visual inspection of the cortical layers. This effort promises to dramatically extend the area of cortex that can be quantified with ultra-high resolution in-plane imaging methods.

Normal Lung Tissue CT Density Changes after Volumetric-Arc Radiotherapy (VMAT) for Lung Cancer.

Radiation-induced lung injury remains a significant toxicity in thoracic radiotherapy. Because a precise diagnosis is difficult and commonly used assessment scales are unclear and subjective, there is a need to establish quantitative and sensitive grading methods. The lung tissue density change expressed in Hounsfield units (HUs) derived from CT scans seems a useful numeric surrogate. The study aimed to confirm a dose-response effect on HU value changes (ΔHU), their evolution in time, and the impact of selected clinical and demographic factors. We used dedicated, self-developed software to register and analyze 120 pairs of initial and follow-up CT scans of 47 lung cancer patients treated with dynamic arc radiotherapy. The differences in HU values between CT scans were calculated within discretized dose-bins limited by isodose lines. We have proved the dose-effect relationship, which is well described with a sigmoid model. We found the time evolution of HU changes to suit a typical clinical presentation of radiation-induced toxicity. Some clinical factors were found to correlate with ΔHU degree: planning target volume (PTV), V35 in the lung, patient’s age and a history of arterial hypertension, and initial lung ventilation intensity. Lung density change assessment turned out to be a sensitive and valuable method of grading post-RT lung toxicity.

Air trapping in COVID-19 patients following hospital discharge: retrospective evaluation with paired inspiratory/expiratory thin-section CT

ObjectivesThe study reports our experience with paired inspiration/expiration thin-section computed tomographic (CT) scans in the follow-up of COVID-19 patients with persistent respiratory symptoms.MethodsFrom August 13, 2020, to May 31, 2021, 48 long-COVID patients with respiratory symptoms (27 men and 21 women; median age, 62.0 years; interquartile range: 54.0–69.0 years) underwent follow-up paired inspiration-expiration thin-section CT scans. Patient demographics, length of hospital stay, intensive care unit admission rate, and clinical and laboratory features of acute infection were also included. The scans were obtained on a median of 72.5 days after onset of symptoms (interquartile range: 58.5–86.5) and at least 30 days after hospital discharge. Thin-section CT findings included ground-glass opacity, mosaic attenuation pattern, consolidation, traction bronchiectasis, reticulation, parenchymal bands, bronchial wall thickening, and air trapping. We used a quantitative score to determine the degree of air trapping in the expiratory scans.ResultsParenchymal abnormality was found in 50% (24/48) of patients and included air trapping (37/48, 77%), ground-glass opacities (19/48, 40%), reticulation (18/48, 38%), parenchymal bands (15/48, 31%), traction bronchiectasis (9/48, 19%), mosaic attenuation pattern (9/48, 19%), bronchial wall thickening (6/48, 13%), and consolidation (2/48, 4%). The absence of air trapping was observed in 11/48 (23%), mild air trapping in 20/48 (42%), moderate in 13/48 (27%), and severe in 4/48 (8%). Independent predictors of air trapping were, in decreasing order of importance, gender (p = 0.0085), and age (p = 0.0182).ConclusionsOur results, in a limited number of patients, suggest that follow-up with paired inspiratory/expiratory CT in long-COVID patients with persistent respiratory symptoms commonly displays air trapping.Key Points• Our experience indicates that paired inspiratory/expiratory CT in long-COVID patients with persistent respiratory symptoms commonly displays air trapping.• Iterative reconstruction and dose-reduction options are recommended for demonstrating air trapping in long-COVID patients.

Therapeutic Response Evaluation in Advanced Melanoma Patients Incorporating Plasma cfDNA, LDH, VEGF, PD-L1, and IFN-γ Measurements.

Current treatment strategies for advanced melanoma require serial assessment of disease status in affected patients. In this study, we sought to examine the relationship between radiographic tumour burden and blood borne biomarkers including plasma cfDNA, serum LDH, plasma VEGF, PD-L1 and IFN-γ in advanced melanoma patients receiving immunotherapy. We hypothesized that a combination of these explanatory variables in a suitable regression analysis model may predict changes in tumour burden during patient treatment. We extracted and quantified circulating cfDNA, LDH, VEGF, PD-L1, and IFN-γ from thirty patients with stage IV melanoma at baseline and at six months. All participating patients were evaluated with paired blood sample collection and CT scan assessments during treatment. Changes in radiographic tumour burden correlated with changes in levels of cfDNA (p≤0.001), LDH (p≤0.001), VEGF (p≤0.001), and PD-L1 (p<0.05) during treatment. Multiple regression analysis consisting of the follow-up to baseline assessment ratios of cfDNA, LDH, VEGF and PD-L1 explained changes in tumour burden (F (4, 23)=32.05, p<0.001); with an R2 of 0.8479 (Y=β0+β1*B+β2*C+β3*D+β4*E). A quantitative measure of cfDNA, LDH, VEGF and PD-L1 may complement current methods of assessing tumour burden in advanced melanoma patients.

Progression of cRORA (Complete RPE and Outer Retinal Atrophy) in Dry Age-Related Macular Degeneration Measured Using SD-OCT.

PurposeThe purpose of this study was to evaluate the long-term rate of progression and baseline predictors of geographic atrophy (GA) using complete retinal pigment epithelium and outer retinal atrophy (cRORA) annotation criteria.MethodsThis is a retrospective study. Columns of GA were manually annotated by two graders using a self-developed software on optical coherence tomography (OCT) B-scans and projected onto the infrared images. The primary outcomes were: (1) rate of area progression, (2) rate of square root area progression, and (3) rate of radial progression towards the fovea. The effects of 11 additional baseline predictors on the primary outcomes were analyzed: total area, focality (defined as the number of lesions whose area is >0.05 mm2), circularity, total lesion perimeter, minimum diameter, maximum diameter, minimum distance from the center, sex, age, presence/absence of hypertension, and lens status.ResultsGA was annotated in 33 pairs of baseline and follow-up OCT scans from 33 eyes of 18 patients with dry age-related macular degeneration (AMD) followed for at least 6 months. The mean rate of area progression was 1.49 ± 0.86 mm2/year (P < 0.0001 vs. baseline), and the mean rate of square root area progression was 0.33 ± 0.15 mm/year (P < 0.0001 vs. baseline). The mean rate of radial progression toward the fovea was 0.07 ± 0.11 mm/year. A multiple variable linear regression model (adjusted r2 = 0.522) revealed that baseline focality and female sex were significantly correlated with the rate of GA area progression.ConclusionsGA area progression was quantified using OCT as an alternative to conventional measurements performed on fundus autofluorescence images. Baseline focality correlated with GA area progression rate and lesion's minimal distance from the center correlated with GA radial progression rate toward the center. These may be important markers for the assessment of GA activity.Translational RelevanceAdvanced method linking specific retinal micro-anatomy to GA area progression analysis.

Prospective evaluation of thoracic diseases using a compact flat-panel detector spiral computed tomographic scanner

ObjectiveTo prospectively evaluate the image quality and diagnostic performance of a compact flat-panel detector (FD) scanner for thoracic diseases compared to a clinical CT scanner. Materials and methodsThe institutional review board approved this single-center prospective study, and all participants provided informed consent. From December 2020 to May 2021, 30 patients (mean age, 67.1 ± 8.3 years) underwent two same-day low-dose chest CT scans using clinical state-of-art and compact FDCT scanners. Image quality was assessed visually and quantitatively. Two readers evaluated the diagnostic performance for nodules, parenchymal opacifications, bronchiectasis, linear opacities, and pleural abnormalities in 40 paired CT scans. The other 20 paired CT scans were used to examine the agreement of semi-quantitative CT scoring regarding bronchiectasis, bronchiolitis, nodules, airspace consolidations, and cavities. ResultsFDCT images had significantly lower visual image quality than clinical CT images (all p < 0.001). The two CT image sets showed no significant differences in signal-to-noise and contrast-to-noise ratios (56.8 ± 12.5 vs. 57.3 ± 15.2; p = 0.985 and 62.9 ± 11.7 vs. 60.7 ± 16.9; p = 0.615). The pooled sensitivity was comparable for nodules, parenchymal opacifications, linear opacities, and pleural abnormalities (p = 0.065–0.625), whereas the sensitivity was significantly lower in FDCT images than in clinical CT images for micronodules (p = 0.007) and bronchiectasis (p = 0.004). The specificity was mostly 1.0. Semi-quantitative CT scores were similar between the CT image sets (p > 0.05), and intraclass correlation coefficients were around 0.950 or higher, except for bronchiectasis (0.869). ConclusionCompact FDCT images provided lower image quality but comparable diagnostic performance to clinical CT images for nodules, parenchymal opacifications, linear opacities, and pleural abnormalities.

Sensitivity of portable low-field magnetic resonance imaging for multiple sclerosis lesions

Magnetic resonance imaging (MRI) is a fundamental tool in the diagnosis and management of neurological diseases such as multiple sclerosis (MS). New portable, low-field strength, MRI scanners could potentially lower financial and technical barriers to neuroimaging and reach underserved or disabled populations, but the sensitivity of these devices for MS lesions is unknown. We sought to determine if white matter lesions can be detected on a portable 64mT scanner, compare automated lesion segmentations and total lesion volume between paired 3T and 64mT scans, identify features that contribute to lesion detection accuracy, and explore super-resolution imaging at low-field. In this prospective, cross-sectional study, same-day brain MRI (FLAIR, T1w, and T2w) scans were collected from 36 adults (32 women; mean age, 50 ± 14 years) with known or suspected MS using Siemens 3T (FLAIR: 1 mm isotropic, T1w: 1 mm isotropic, and T2w: 0.34–0.5 × 0.34–0.5 × 3–5 mm) and Hyperfine 64mT (FLAIR: 1.6 × 1.6 × 5 mm, T1w: 1.5 × 1.5 × 5 mm, and T2w: 1.5 × 1.5 × 5 mm) scanners at two centers. Images were reviewed by neuroradiologists. MS lesions were measured manually and segmented using an automated algorithm. Statistical analyses assessed accuracy and variability of segmentations across scanners and systematic scanner biases in automated volumetric measurements. Lesions were identified on 64mT scans in 94% (31/33) of patients with confirmed MS. The average smallest lesions manually detected were 5.7 ± 1.3 mm in maximum diameter at 64mT vs 2.1 ± 0.6 mm at 3T, approaching the spatial resolution of the respective scanner sequences (3T: 1 mm, 64mT: 5 mm slice thickness). Automated lesion volume estimates were highly correlated between 3T and 64mT scans (r = 0.89, p < 0.001). Bland-Altman analysis identified bias in 64mT segmentations (mean = 1.6 ml, standard error = 5.2 ml, limits of agreement = –19.0–15.9 ml), which over-estimated low lesion volume and under-estimated high volume (r = 0.74, p < 0.001). Visual inspection revealed over-segmentation was driven venous hyperintensities on 64mT T2-FLAIR. Lesion size drove segmentation accuracy, with 93% of lesions > 1.0 ml and all lesions > 1.5 ml being detected. Using multi-acquisition volume averaging, we were able to generate 1.6 mm isotropic images on the 64mT device. Overall, our results demonstrate that in established MS, a portable 64mT MRI scanner can identify white matter lesions, and that automated estimates of total lesion volume correlate with measurements from 3T scans.

Conversion of Central Subfield Thickness Measurements of Diabetic Macular Edema Across Cirrus and Spectralis Optical Coherence Tomography Instruments.

PurposeDevelop equations to convert Cirrus central subfield thickness (CST) to Spectralis CST equivalents and vice versa in eyes with diabetic macular edema (DME).MethodsThe DRCR Retina Network Protocol O data were split randomly to train (70% sample) and validate (30% sample) conversion equations. Data from an independent study (CADME) also validated the equations. Bland–Altman 95% limits of agreement between predicted and observed values evaluated the equations.ResultsProtocol O included 374 CST scan pairs from 187 eyes (107 participants). The CADME study included 150 scan pairs of 37 eyes (37 participants). Proposed conversion equations are Spectralis = 40.78 + 0.95 × Cirrus and Cirrus = 1.82 + 0.94 × Spectralis regardless of age, sex, or CST. Predicted values were within 10% of observed values in 101 (90%) of Spectralis and 99 (88%) of Cirrus scans in the validation data; and in 136 (91%) of the Spectralis and 148 (99%) of the Cirrus scans in the CADME data. Adjusting for within-eye correlations, 95% of conversions are estimated to be within 17% (95% confidence interval, 14%–21%) of CST on Spectralis and within 22% (95% confidence interval, 18%–28%) of CST on Cirrus.ConclusionsConversion equations developed in this study allow the harmonization of CST measurements for eyes with DME using a mix of current Cirrus and Spectralis device images.Translational RelevanceThe CSTs measured on Cirrus and Spectralis devices are not directly comparable owing to outer boundary segmentation differences. Converting CST values across spectral domain optical coherence tomography instruments should benefit both clinical research and standard care efforts.

Analysis of the impact of the facial scanning method on the precision of a virtual facebow record technique: An in vivo study

Statement of problemVirtual facebow record techniques typically record the relationship of a maxillary digital scan to facial landmarks by aligning it to a 3-dimensional face scan. Three-dimensional face scans can be acquired with different facial scanning methods, but the impact of the facial scanning method on the accuracy (trueness and precision) of a virtual facebow record technique remains unclear. PurposeThe purpose of this in vivo study was to assess the impact of the facial scanning method on the precision under the repeatability conditions (repeatability) of a virtual facebow record technique. Material and methodsRepeatability of the virtual facebow record technique with the following 3 clinical-grade facial scanning methods was determined and compared: a professional handheld scanner based on structured blue light scanning technology (PHS method); an attachment-type 3-dimensional sensor camera connected to a tablet and controlled with a mobile application (3DSC-T method); and a smartphone with an integrated 3-dimensional sensor camera controlled with a mobile application (3DSC-S method). To determine the repeatability of the virtual facebow record technique with each facial scanning method, 8 virtual facebow records of a completely dentate adult with class I occlusion and mesoprosopic facial form were obtained (8×3=24 in total); with these, 8 locations of a maxillary digital scan with respect to a common 3-dimensional face scan were obtained. Repeatability was determined in terms of deviations between located maxillary digital scans, determined, in turn, by calculating the distances between corresponding vertices for each of the possible nonrepeating combinations of pairs of located maxillary digital scans (8C2=28). Finally, the repeatability of the virtual facebow record technique with the different facial scanning methods was compared by using the Welch ANOVA test and the post hoc Games-Howell test (both α=.05). ResultsThe repeatability of the virtual facebow record technique with PHS, 3DSC-T, and 3DSC-S facial scanning methods resulted in 0.243 ±0.094 mm, 0.437 ±0.171 mm, and 1.023 ±0.399 mm, respectively. Comparison of these results revealed that the facial scanning method had a statistically significant effect on the repeatability of the virtual facebow record technique (P<.001) and that its repeatability was statistically significantly greater with the PHS facial scanning method than with the 3DSC-T and 3DSC-S facial scanning methods and greater with the 3DSC-T facial scanning method than with the 3DSC-S facial scanning method (P<.001 for all pairwise comparisons). ConclusionsThis study found that the facial scanning method had a great impact on the repeatability of the virtual facebow record technique and that the virtual facebow record technique was more repeatable with more accurate facial scanning methods.

Closer correlations between florbetapir PET measurements of amyloid plaque burden using a cerebral white matter reference region and Alzheimer’s disease‐related hypometabolism

AbstractBackgroundAlzheimer’s disease (AD) is associated with amyloid‐β (Aß) plaque deposition and a characteristic pattern of cerebral glucose hypometabolism. While a cerebellar reference region is commonly used to characterize cerebral Aβ burden, we demonstrated improvements in the ability of florbetapir PET to track Aβ burden using a cerebral white matter reference region (Chen, et al., 2015). Here, we compare associations between cerebral‐to‐reference region florbetapir PET standard value uptake ratios (SUVRs) using cerebral white matter versus cerebellar reference regions to two FDG PET indices of AD‐related cerebral hypometabolism: “hypometabolic convergence index” (HCI) and “statistical region‐of‐interest” (sROI) (Chen et al, 2010; 2011).MethodWe analyzed 1722 florbetapir and FDG PET scan pairs from the AD Neuroimaging Initiative, including 267 with probable AD dementia (pAD), 592 with MCI and 379 cognitively unimpaired (CU). Florbetapir SUVRs were computed using measures from the cerebral regions‐of‐interest with the cerebral white matter or cerebellar reference regions. HCI and sROI were generated from the FDG PET images. Pearson’s correlations and the Steiger statistical test were used to characterize and compare the extent to which florbetapir SUVRs using cerebellar versus cerebral white matter reference regions were associated with HCI or sROI in the overall group, the pAD, MCI and CU sub‐groups.ResultCorrelations between florbetapir SUVRs using the white matter reference region and HCI (0.59, 0.41, 0.45, and 0.40 in the overall, pAD, MCI and CU groups) were significantly stronger than those using the cerebellar reference region (0.40, 0.23, 0.26, and 0.20, P=1.1e‐16, 3.6e‐06, 1.3e‐09, and 1.0e‐11, respectively). Similarly, inverse correlations between florbetapir SUVR measurements using the white matter reference region and sROI measurements (‐0.51, ‐0.32, ‐0.38, and ‐0.28 in the overall, pAD, MCI and CU groups) were significantly stronger than those using the cerebellar reference region (‐0.34, ‐0.09, ‐0.24, and ‐0.13, P=2.0e‐15, 3.2e‐07, 1.7e‐0, and 4.0e‐07, respectively).ConclusionFlorbetapir PET Aβ plaque measures are more closely associated with the AD‐related pattern of cerebral hypometabolism when SUVRs are generated using a white matter rather than cerebellar reference regions. Additional research is needed to examine if our results are generalizable for other Aβ PET tracers and longitudinal change measures.

Magnetic resonance imaging-based body composition is associated with nutritional and inflammatory status: a longitudinal study in patients with Crohn's disease

ObjectiveTo evaluate the changes in magnetic resonance imaging-based body composition parameters during follow-ups in patients with Crohn's disease (CD).MethodsBetween November 1, 2017, and June 30, 2021, patients diagnosed with CD, who underwent two or more magnetic resonance enterography (MRE) scans at our institution were retrospectively reviewed. The baseline and one subsequent follow-up scan for each patient were paired to form longitudinal comparisons. Skeletal muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) indexes were calculated from tissue areas measured at the third lumbar vertebra level per scan, standardized by dividing the height2 and lumbar height2 (heightL1–L5). We also assessed the correlation between changes in VAT to total adipose tissue ratio (VA/TA index) and CD activity scores (5-point MRE classification) using Spearman’s correlation analysis. A multivariate linear regression model was used to adjust for the follow-up duration and treatment type.ResultsOverall, 49 patients (with 49 paired scans) were enrolled. VA/TA index changes were negatively correlated with changes in skeletal muscle index (SMI; r = − 0.339, p < 0.05). The VA/TA index (52.69 ± 10.66% vs. 49.18 ± 10.80%, p < 0.001) and the total MRE score (8.0 ± 3.9 vs. 5.7 ± 3.4, p < 0.001) decreased significantly during follow-up, regardless of follow-up duration and treatment type (both p > 0.05). Changes in total MRE score were negatively correlated with SMI changes (r = − 0.408, p < 0.01) but positively correlated with VA/TA index changes (r = 0.479, p < 0.01).ConclusionAn increase in SMI and a decrease in VA/TA index could reflect improved nutritional and inflammatory status.

MRI-Derived Fetal Weight Estimation in the Midpregnancy Fetus: A Method Comparison Study

Objectives: The aim of this study was to compare the standard ultrasound (US) estimated fetal weight (EFW) and MRI volume-derived methods for the midtrimester fetus. Methods: Twenty-five paired US and MRI scans had the EFW calculated (gestational age [GA] range = 20–26 weeks). The intra- and interobserver variability of each method was assessed (2 operators/modality). A small sub-analysis was performed on 5 fetuses who were delivered preterm (mean GA 29 ⁺³ weeks) and compared to the actual birthweight. Results: Two MRI volumetry EFW formulae under-measured compared to US by −10.9% and −14.5% in the midpregnancy fetus (p < 0.001) but had excellent intra- and interobserver agreement (intraclass correlation coefficient = 0.998 and 0.993). In the preterm fetus, the mean relative difference (MRD) between the MRI volume-derived EFW (MRI-EFW) and actual expected birthweight (at the scan GA) was −13.7% (−159.0 g, 95% CI: −341.7 to 23.7 g) and −17.1% (−204.6 g, 95% CI: −380.4 to −28.8 g), for the 2 MRI formulae. The MRD was smaller for US at 5.3% (69.8 g, 95% CI: −34.3 to 173.9). Conclusions: MRI-EFW results should be interpreted with caution in midpregnancy. Despite excellent observer agreement with MRI volumetry, refinement of the EFW formula is needed in the second trimester, for the small and for the GA and preterm fetus to compensate for lower fetal densities.