A systematic review of (semi-)automatic quality control of T1-weighted MRI scans.

PURPOSE: Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks. METHODS: We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool). RESULTS: A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures. CONCLUSION: Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches.

Parametric imaging of dual-time window [18F]flutemetamol and [18F]florbetaben studies.

Optimal pharmacokinetic models for quantifying amyloid beta (Aß) burden using both [18F]flutemetamol and [18F]florbetaben scans have previously been identified at a region of interest (ROI) level. The purpose of this study was to determine optimal quantitative methods for parametric analyses of [18F]flutemetamol and [18F]florbetaben scans. Forty-six participants were scanned on a PET/MR scanner using a dual-time window protocol and either [18F]flutemetamol (N=24) or [18F]florbetaben (N=22). The following parametric approaches were used to derive DVR estimates: reference Logan (RLogan), receptor parametric mapping (RPM), two-step simplified reference tissue model (SRTM2) and multilinear reference tissue models (MRTM0, MRTM1, MRTM2), all with cerebellar grey matter as reference tissue. In addition, a standardized uptake value ratio (SUVR) was calculated for the 90-110 min post injection interval. All parametric images were assessed visually. Regional outcome measures were compared with those from a validated ROI method, i.e. DVR derived using RLogan. Visually, RPM, and SRTM2 performed best across tracers and, in addition to SUVR, provided highest AUC values for differentiating between Aß-positive vs Aß-negative scans ([18F]flutemetamol: range AUC=0.96-0.97 [18F]florbetaben: range AUC=0.83-0.85). Outcome parameters of most methods were highly correlated with the reference method (R2≥0.87), while lowest correlation were observed for MRTM2 (R2=0.71-0.80). Furthermore, bias was low (≤5%) and independent of underlying amyloid burden for MRTM0 and MRTM1. The optimal parametric method differed per evaluated aspect; however, the best compromise across aspects was found for MRTM0 followed by SRTM2, for both tracers. SRTM2 is the preferred method for parametric imaging because, in addition to its good performance, it has the advantage of providing a measure of relative perfusion (R1), which is useful for measuring disease progression.

Impact of cerebral blood flow and amyloid load on SUVR bias.

BACKGROUND: Despite its widespread use, the semi-quantitative standardized uptake value ratio (SUVR) may be biased compared with the distribution volume ratio (DVR). This bias may be partially explained by changes in cerebral blood flow (CBF) and is likely to be also dependent on the extent of the underlying amyloid-ß (Aß) burden. This study aimed to compare SUVR with DVR and to evaluate the effects of underlying Aß burden and CBF on bias in SUVR in mainly cognitively unimpaired participants. Participants were scanned according to a dual-time window protocol, with either [18F]flutemetamol (N = 90) or [18F]florbetaben (N = 31). The validated basisfunction-based implementation of the two-step simplified reference tissue model was used to derive DVR and R1 parametric images, and SUVR was calculated from 90 to 110 min post-injection, all with the cerebellar grey matter as reference tissue. First, linear regression and Bland-Altman analyses were used to compare (regional) SUVR with DVR. Then, generalized linear models were applied to evaluate whether (bias in) SUVR relative to DVR could be explained by R1 for the global cortical average (GCA), precuneus, posterior cingulate, and orbitofrontal region. RESULTS: Despite high correlations (GCA: R2 ≥ 0.85), large overestimation and proportional bias of SUVR relative to DVR was observed. Negative associations were observed between both SUVR or SUVRbias and R1, albeit non-significant. CONCLUSION: The present findings demonstrate that bias in SUVR relative to DVR is strongly related to underlying Aß burden. Furthermore, in a cohort consisting mainly of cognitively unimpaired individuals, the effect of relative CBF on bias in SUVR appears limited. EudraCT Number: 2018-002277-22, registered on: 25-06-2018.

Test-Retest Variability of Relative Tracer Delivery Rate as Measured by [11C]PiB.

PURPOSE: Moderate-to-high correlations have been reported between the [11C]PiB PET-derived relative tracer delivery rate R1 and relative CBF as measured using [15O]H2O PET, supporting its use as a proxy of relative CBF. As longitudinal PET studies become more common for measuring treatment efficacy or disease progression, it is important to know the intrinsic variability of R1. The purpose of the present study was to determine this through a retrospective data analysis. PROCEDURES: Test-retest data belonging to twelve participants, who underwent two 90 min [11C]PiB PET scans, were retrospectively included. The voxel-based implementation of the two-step simplified reference tissue model with cerebellar grey matter as reference tissue was used to compute R1 images. Next, test-retest variability was calculated, and test and retest R1 measures were compared using linear mixed effect models and a Bland-Altman analysis. RESULTS: Test-retest variability was low across regions (max. 5.8 %), and test and retest measures showed high, significant correlations (R2=0.92, slope=0.98) and a negligible bias (0.69±3.07 %). CONCLUSIONS: In conclusion, the high precision of [11C]PiB R1 suggests suitable applicability for cross-sectional and longitudinal studies.

[11C]PIB amyloid quantification: effect of reference region selection.

BACKGROUND: The standard reference region (RR) for amyloid-beta (Aß) PET studies is the cerebellar grey matter (GMCB), while alternative RRs have mostly been utilized without prior validation against the gold standard. This study compared five commonly used RRs to gold standard plasma input-based quantification using the GMCB. METHODS: Thirteen subjects from a test-retest (TRT) study and 30 from a longitudinal study were retrospectively included (total: 17 Alzheimer's disease, 13 mild cognitive impairment, 13 controls). Dynamic [11C]PiB PET (90 min) and T1-weighted MR scans were co-registered and time-activity curves were extracted for cortical target regions and the following RRs: GMCB, whole cerebellum (WCB), white matter brainstem/pons (WMBS), whole brainstem (WBS) and eroded subcortical white matter (WMES). A two-tissue reversible plasma input model (2T4k_Vb) with GMCB as RR, reference Logan and the simplified reference tissue model were used to derive distribution volume ratios (DVRs), and standardized uptake value (SUV) ratios were calculated for 40-60 min and 60-90 min intervals. Parameter variability was evaluated using TRT scans, and correlations and agreements with the gold standard (DVR from 2T4k_Vb with GMCB RR) were also assessed. Next, longitudinal changes in SUVs (both intervals) were assessed for each RR. Finally, the ability to discriminate between visually Aß positive and Aß negative scans was assessed. RESULTS: All RRs yielded stable TRT performance (max 5.1% variability), with WCB consistently showing lower variability. All approaches were able to discriminate between Aß positive and Aß negative scans, with highest effect sizes obtained for GMCB (range - 0.9 to - 0.7), followed by WCB (range - 0.8 to - 0.6). Furthermore, all approaches provided good correlations with the gold standard (r ≥ 0.78), while the highest bias (as assessed by the regression slope) was observed using WMES (range slope 0.52-0.67), followed by WBS (range slope 0.58-0.92) and WMBS (range slope 0.62-0.91). Finally, RR SUVs were stable across a period of 2.6 years for all except WBS and WMBS RRs (60-90 min interval). CONCLUSIONS: GMCB and WCB are considered the best RRs for quantifying amyloid burden using [11C]PiB PET.