The present study aims to assess the performance of a CT-guided spatial normalization method (CT-method) for the anatomical region-of-interest (ROI)-based semi-quantification of dopamine transporter (DAT) single photon emission computed tomography (SPECT) images and the detection of nigrostriatal degeneration as compared to an effective SPECT template-based method (MSPECT-method) and visual analysis performed by an expert reader. Patients who underwent [123I]FP-CIT SPECT/CT in the Hospices Civils de Lyon between 2008 and 2018 for clinically uncertain parkinsonian syndromes were included. The proposed CT-method aimed to spatially normalize the jointly acquired CT scans and apply the deformation fields to the coregistered SPECT images. It was compared to an effective SPECT template-based method using multiple templates as target for the spatial normalization (MSPECT-method). The distribution of specific binding ratios (SBR) was compared between both methods and the SBR classifications were compared to an expert’s visual classification of the scans, which served as the reference. Overall, 1156 patients (mean age ± SD = 68.7 ± 11.5; 52.6

Background Accurate interpretation of dopamine transporter (DAT)-SPECT depends on clinical context, including symptoms, medication, comorbidities, and structural imaging findings. In routine practice, however, this information is often documented as heterogeneous free text, which may hinder consistent consideration of clinically relevant parameters and limit guideline-compliant reporting. We therefore developed and evaluated a locally deployable small language model (SLM)-based pipeline to structure and standardize the clinical information section for DAT-SPECT reporting. Two retrospective datasets were used. The in-house dataset included clinical information sections from 2,990 DAT-SPECT examinations and was split into training (n = 2,700) and internal test data (n = 290). The external validation dataset comprised 595 clinical information sections from a second institution. A structured dictionary (466 entries across 12 categories) was created by two experienced DAT-SPECT readers and used to manually annotate the training and internal test samples. A Qwen3 1.7B model was fine-tuned by supervised learning to reproduce expert annotations. A rule-based postprocessing pipeline converted model outputs into report-ready structured text, including removal of redundant entries, automated suggestion of scan indication, consolidation of symptom lateralization and time course, reminders for missing guideline-relevant elements, and medication interaction checks. Internal test performance was assessed by expert comparison of SLM output with manual annotation. External performance was assessed by counting the manual corrections required in the postprocessed output under strict criteria. Results In the internal test dataset, the postprocessed SLM output provided the same information as the expert annotation in 196/290 cases (67.6%), including exact matches in 147/290 (50.7%). Interpretation differences due to free-text ambiguity occurred in 13/290 cases (4.5%), and SLM errors in 41/290 cases (14.1%). In 40/290 cases (13.8%), the SLM output was judged superior to the manual annotation. Among the error cases, 36/41 (87.8%) involved only a single erroneous or missing item. In the external dataset, 446/595 postprocessed outputs (75.0%) required no manual correction before inclusion in the report. Among the remaining 149/595 cases (25.0%), correction of only a single item was required in 111 cases (18.7% of all cases). The mean number of corrections was 0.34 items per case, corresponding to 6.0% of all structured items. Conclusions A locally deployable SLM combined with rule-based postprocessing can transform free-text clinical information for DAT-SPECT into standardized, report-ready documentation with high clinical usability and robust external performance. By improving consistency, highlighting missing guideline-relevant information, and enabling real-time support during clinical assessment, the pipeline may facilitate structured reporting without reliance on cloud-based infrastructure.

Aim:This study evaluated ChatGPT (GPT-5.2) for drafting a review paper on deep learning in dopamine transporter (DAT)-SPECT with [¹²³I]ioflupane. Methods:The review workflow consisted of 3 steps: (i) literature search, (ii) generation of structured summaries with 24 predefined fields for each publication, and (iii) drafting a review paper based on the structured summaries across all publications. A detailed prompt for ChatGPT was iteratively designed for each step with ChatGPT support. A manual literature search was independently performed by an expert in DAT-SPECT and deep learning. ChatGPT-generated structured summaries were manually fact-checked against the full publications and corrected where necessary. The review draft generated by ChatGPT was checked against the corrected summaries. Results:When prompted to compile an exhaustive list of publications, ChatGPT cited 13 papers, whereas the manual search identified 70 relevant publications, 67 of which were included. Corrections to ChatGPT-generated structured summaries were required in 27 cases (40.3%), affecting one or two of the 24 predefined fields, while no changes were necessary in 40 publications (59.7%). Most corrections could likely have been avoided by more precise prompting. All numerical information (dataset sizes, train-test splits, performance metrics) was correct. The review draft (~950 words) generated by ChatGPT was content-wise meaningful and accurate, but contained referencing errors, including incorrect citations, missing references, and citations of non-existent publications. Conclusions:ChatGPT is a highly effective tool for drafting review manuscripts in nuclear medicine imaging, but its limitations in literature retrieval and referencing require careful expert supervision.

Summary:This procedure guideline for SPECT examinations of striatal dopamine transporter availability is intended to support the planning, execution, quality control, interpretation and reporting of cerebral SPECT scans with [123I]ioflupane. It is an update and expansion built on the 2019 version of the procedure guideline. It was developed through an informal process as a consensus of the Neuroimaging Working Group of the German Society of Nuclear Medicine and in consultation with the German Neurological Society. It is intended for use by physicians and technical staff working in the field of nuclear medicine.

Preamble:This represents a substantial development of the guideline on the topic first published in 2016. The following notable points have been updated: the sections on background information; the clinical benefit of the method; the resulting differential diagnostic considerations; the outlook for possible future extensions of the indication spectrum; the quantitative analysis of the PET images; the embedding of the method in diagnostic pathways; and the relation to alternative biomarker methods such as amyloid measurement in CSF/blood.

Abstract:This procedure guideline for brain perfusion SPECT is intended to support the planning, execution, quality control, evaluation and reporting of brain perfusion SPECT studies using the 99 mTc-labelled radiopharmaceuticals [99 mTc]Tc-HMPAO and [99 mTc]Tc-ECD. It is an update and expansion built on the 2019 version of the procedure guideline. It was developed through an informal process as a consensus of the Neuroimaging Working Group of the German Society of Nuclear Medicine. It is intended for use by physicians and physical-technical staff (technologists, physicists) working in nuclear medicine.

Summary This procedure guideline for SPECT examinations of striatal dopamine transporter availability is intended to support the planning, execution, quality control, interpretation and reporting of cerebral SPECT scans with [123I]ioflupane. It is an update and expansion built on the 2019 version of the procedure guideline. It was developed through an informal process as a consensus of the Neuroimaging Working Group of the German Society of Nuclear Medicine and in consultation with the German Neurological Society. It is intended for use by physicians and technical staff working in the field of nuclear medicine.

To estimate (i) the rate of difficult-to-interpret cases in dopamine transporter (DAT)-SPECT with [123I]ioflupane and (ii) the diagnostic accuracy of binary visual categorization in difficult-to-interpret DAT-SPECT.The study included 178 control subjects (49% females, 63.7±11.7y) and 178 sex- and age-matched patients with pre-screening clinical diagnosis of Parkinson's disease (PD) from the Parkinson's Progression Markers Initiative (PPMI). SPECT images reconstructed by the PPMI and a local method were visually interpreted twice by 2 independent readers with respect to Parkinson-like reduction of the striatal signal using the following 6-score: -3 = clearly reduced, -2 = probably reduced, -1 = more likely reduced than normal, 1 = more likely normal than reduced, 2 = probably normal, 3 = clearly normal. Cases with 6-score of -1 or 1 were considered "difficult-to-interpret", all other cases were considered "conclusive". To assess diagnostic accuracy relative to the clinical group label (control, PD), the 6-score was binarized using 0 as cutoff.The proportion of difficult-to-interpret cases ranged between 3.4% (95%-CI 1.5-5.2%) and 7.6% (4.8-10.3%) across readers and reconstruction methods. The proportion of cases misclassified by the binarized score ranged between 17.4% (1.9-32.9%) and 50.0% (25.5-74.5%) among the difficult-to-interpret cases, and between 5.1% (2.7-7.4%) and 6.4% (3.8-9.0%) among the conclusive cases.(i) the proportion of difficult-to-interpret cases in DAT-SPECT for the diagnosis of parkinsonism is not larger than 10%, (ii) the diagnostic accuracy of binary decisions is severely reduced in these cases. We therefore recommend reporting difficult-to-interpret cases as "inconclusive", unless in special situations.

BACKGROUND:Multiple-pinhole (MPH) collimators for brain SPECT improve the resolution-sensitivity tradeoff compared with conventional parallel-hole and fan-beam collimators. The system count sensitivity profile with MPH collimators exhibits a peak at the center of the field-of-view and significant decline in count sensitivity toward the periphery. In MPH SPECT with [123I]FP-CIT, this makes the difference in relative statistical noise between the striatum and extrastriatal (reference) regions even bigger. This could slow down the convergence of iterative reconstruction compared with [123I]FP-CIT using conventional collimators. This study evaluated the impact of the effective number of reconstruction iterations in [123I]FP-CIT SPECT with MPH collimators. METHODS:671 patients (43.8% females, 67.3±11.3y) were included retrospectively. Projection data were acquired with a triple-head camera (Mediso AnyScan Trio) equipped with 2nd generation general-purpose brain MPH collimators. MPH projections from 12 min total scan duration were reconstructed with the iterative 3d Tera-Tomo Monte Carlo algorithm of the system software. The effective number of iterations was varied between 12 and 300. To evaluate the impact of the iteration number on diagnostic performance, a data-driven Gaussian mixture model approach was used to assess the power of the putaminal specific binding ratio (SBR) for differentiation between reduced and normal scans. In a subset of 72 patients, reconstructed images were visually evaluated twice by 7 independent readers with respect to parkinson-like reduction of striatal [123I]FP-CIT uptake using a Likert 6-score (-3=clearly reduced, …, 3=clearly normal). The impact of the iteration number on the Likert 6-score was tested using repeated measure analysis of variance (ANOVA) with iteration number, reader and session as within-subject factors. The majority vote binary classification (reduced, normal) was taken into account as between-subject factor. Within- and between-readers agreement of visual scoring were characterized by Cohen's and Fleiss' kappa. RESULTS:Iterative reconstruction converged much slower in the extrastriatal reference region than in the striatum and its subregions. Considerable improvement of spatial resolution with increasing iteration number came with an acceptable increase of statistical noise. The discriminative power of the putaminal SBR was highest with 300 effective iterations. There was a small but significant reduction of reader confidence and between-readers agreement with 300 compared to 24 iterations, particularly in normal cases (Likert 6-score 2.325 versus 2.468, p < 0.001). CONCLUSIONS:300 iterations provide a good compromise between spatial resolution, robustness and diagnostic power of semi-quantitative SBR analyses on one side versus statistical noise and between-readers variability on the other side. In order to avoid relevant loss of between-readers stability, readers should be specifically trained for reading [123I]FP-CIT MPH SPECT images reconstructed with a high number of iterations.

The advent of disease-modifying therapies for neurodegenerative diseases may result in a growing demand for nuclear neuroimaging procedures presenting opportunities but also challenges to the nuclear medicine community. Whether capacity and expertise in Germany are sufficient to meet an increasing demand for nuclear neuroimaging is under discussion. Against this background, the Neuroimaging Working Group of the German Society of Nuclear Medicine initiated the first survey on the status of nuclear neuroimaging in Germany in 2023. 82 institutions participated in the survey: 33 practices, 15 community hospitals, 34 university hospitals. Primary findings were the following. In practices, brain scans are less frequently performed than in hospitals and are often limited to dopamine transporter SPECT. Brain PET is mainly performed in hospitals, and in community hospitals it is often restricted to FDG PET. Nevertheless, availability of amyloid PET with well-certified quality can be taken for granted. Thus, access to amyloid PET will not be a major bottleneck for new treatments of Alzheimer's disease. Adequate reimbursement and clear anchoring in clinical guidelines have the greatest potential to advance nuclear neuroimaging in Germany. Clinical dopamine transporter SPECT is largely in agreement with procedure guidelines. An area for improvement is the limited availability of MR images to avoid misinterpretation of structural/vascular lesions as nigrostriatal degeneration. The survey provides the first systematic assessment of the status of nuclear neuroimaging in Germany. It underscores the capacity of the German nuclear medicine community to meet an increasing demand for neuroimaging procedures, its adherence to procedure guidelines and identifies topics for improvement.

To provide fully automatic scanner-independent 5-level categorization of the [123I]FP-CIT uptake in striatal subregions in dopamine transporter SPECT. A total of 3500 [123I]FP-CIT SPECT scans from two in house (n = 1740, n = 640) and two external (n = 645, n = 475) datasets were used for this study. A convolutional neural network (CNN) was trained for the categorization of the [123I]FP-CIT uptake in unilateral caudate and putamen in both hemispheres according to 5 levels: normal, borderline, moderate reduction, strong reduction, almost missing. Reference standard labels for the network training were created automatically by fitting a Gaussian mixture model to histograms of the specific [123I]FP-CIT binding ratio, separately for caudate and putamen and separately for each dataset. The CNN was trained on a mixed-scanner subsample (n = 1957) and tested on one independent identically distributed (IID, n = 1068) and one out-of-distribution (OOD, n = 475) test dataset. The accuracy of the CNN for the 5-level prediction of the [123I]FP-CIT uptake in caudate/putamen was 80.1/78.0

Positron emission tomography (PET) with 18F-Fluorodeoxyglucose (FDG) is an established tool in the diagnostic workup of patients with suspected dementing disorders. However, compared to the routinely available magnetic resonance imaging (MRI), FDG-PET remains significantly less accessible and substantially more expensive. Here, we present SiM2P, a 3D diffusion bridge-based framework that learns a probabilistic mapping from MRI and auxiliary patient information to simulate FDG-PET images of diagnostic quality. In a blinded clinical reader study, two neuroradiologists and two nuclear medicine physicians rated the original MRI and SiM2P-simulated PET images of patients with Alzheimer's disease, behavioral-variant frontotemporal dementia, and cognitively healthy controls. SiM2P significantly improved the overall diagnostic accuracy of differentiating between three groups from 75.0

BackgroundMRI-based hippocampus volume (HV) is widely used as neurodegeneration marker in Alzheimer's disease.ObjectiveAn easy-to-use and easy-to-interpret method to categorize T1-weighted MR sequences with respect to test-retest stability of hippocampus volumetry based on general image quality metrics (IQM).MethodsThe study included 446 3D T1-weighted MRI scans of one healthy middle-aged man obtained during 32 months in 122 scanning sessions performed with 96 different scanners at 76 different sites. Each scanning session represented a different acquisition sequence of ≥2 back-to-back repeat scans (3.7 ± 0.7 on average). Unilateral HVs were determined with 18 different tools for automatic volumetry. An acquisition sequence was considered "poor" if the z-score of the within-session coefficient-of-variation of the HV estimates from the session, averaged across all volumetry tools and both hemispheres, exceeded one standard deviation. General IQM were computed for each scanning session using the freely available MRI Quality Control Tool. A classification-and-regression tree (CART) was trained to discriminate between good and poor acquisition sequences using the IQM as input.ResultsThe CART selected the left-right width of the acquisition field-of-view and the contrast-to-noise ratio as predictor variables. Overall accuracy of the CART was 79.5%. CART-based classification increased the ratio of good-to-poor acquisition sequences from 3.5 among all sequences to 7.4 among the sequences predicted to be good. This was at the expense of losing 15% of the good sequences.ConclusionsThe IQM-based decision tree model provides useful performance for the differentiation of T1-weighted sequences associated with good versus poor test-retest stability of hippocampus volumetry.

Amyloid PET imaging is an established diagnostic tool for Alzheimer's disease, but its successful integration into clinical practice requires a comprehensive understanding of its impact on patients and the healthcare system. In 2022, the coverage with evidence development (CED) ENABLE study has been approved by the German Federal Joint Committee (trial registration: DRKS00030839). The study is scheduled to start in early 2024. Primary outcome of ENABLE is the change in patients' functional abilities at 18 months after diagnosis with amyloid PET versus without amyloid-PET. Here we describe the design of an ENABLE sub-study aimed at performing a process evaluation. We will conduct an outcome and process evaluation using a pre-post descriptive design nested in ENABLE (Figure), based on the Medical Research Council's Process Evaluation (PE) framework. The PE will focus on acceptability and reach of the target groups of patients and care providers, as well as fidelity of delivery (% of adherence to protocol, study duration), contextual changes and mechanisms (e.g., ability to mirror routine care). We will collect patients’ data independently, but in parallel with ENABLE, using interviews, surveys and checklists to assess acceptance of the intervention, fidelity of adherence to the follow-up procedures, perceived benefits and challenges. Healthcare providers and administrators will be interviewed to identify implementation hurdles and facilitators, contextual factors influencing uptake and underlying mechanisms. For data analysis we will employ a mixed-methods approach. Expected outcomes will include acceptability and adherence, barriers and facilitators to implementation, contextual factors influencing implementation, perceived utility of the intervention by patients and providers, and mechanisms by which the intervention can be delivered as part of normal care. This process evaluation can inform researchers on how to design future CED studies to be implemented into clinical care. The lessons learned during the development and approval process of the ENABLE project in Germany, as well as its process evaluation, can benefit other European CED studies and policy-makers stakeholders in making informed decisions. Likewise, these findings can be generalized beyond the context of amyloid PET to other diagnostic biomarkers, ultimately improving the diagnosis and management of Alzheimer's disease.

Background Progressive supranuclear palsy (PSP) is a rare neurodegenerative movement disorder clinically characterized by falls, axial rigidity, vertical supranuclear gaze palsy, bradykinesia, and cognitive decline. There is a relative lack of studies on the functional neuroimaging correlates of cognitive impairment in PSP. Objective This study investigated the relationship between regional cerebral glucose metabolism as assessed by static 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET) with global scaling and the profile of cognitive performance according to the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) test battery in a sample of PSP patients representative of clinical practice. Methods 22 PSP patients from three tertiary movement disorder centers with CERAD testing and FDG-PET in close proximity were included retrospectively. Neuropsychological test performance was assessed for correlation with FDG uptake on a voxel-by-voxel basis with cluster-level correction for multiple testing, separately for each subtest. Results In comparison to matched healthy controls, PSP patients showed reduced FDG uptake in the left inferior frontal gyrus and right angular gyrus. Reduced overall cognitive performance according to Montreal Cognitive Assessment was associated with reduced FDG uptake in the right frontal eye field. Word list learning correlated with FDG uptake in the left frontal eye field, while language fluency was linked to FDG uptake in the bilateral premotor and supplementary motor areas. Conclusions Reduction of FDG uptake in PSP primarily affects frontal brain regions and is linked to the performance in specific cognitive domains. These findings may have implications for the interpretation of FDG-PET to support the etiological diagnosis of PSP.

Multiple-pinhole collimators provide considerable improvement of SPECT system count sensitivity. This case report suggests that SPECT with brain-specific multiple-pinhole collimators enables cerebral perfusion imaging with diagnostic quality by an early 12 minutes scan immediately after injection of a standard dose of the dopamine transporter ligand 123 I-FP-CIT. Thus, 123 I-FP-CIT SPECT with multiple-pinhole collimators could assess nigrostriatal degeneration (late image) and extrastriatal involvement (early perfusion image) during the same imaging session. The early image may serve as an alternative to FDG PET in patients with suspicion of an atypical neurodegenerative parkinsonian syndrome. This could streamline diagnostic workflows by reducing the need for additional imaging modalities.

Striatal specific binding ratios (SBR) are widely used to support the interpretation of dopamine transporter SPECT scans. Automatic SBR computation often involves using affine transformations to map the individual SPECT images to an anatomical reference space for ROI analysis using predefined standard masks. This does not account for differences in volumetric scaling between brain structures since, by definition, affine transformations preserve volume ratios. However, striatal volume has been reported to scale proportional to (intracranial volume)0.4, indicating particularly pronounced “negative” allometric scaling. This study aimed to investigate the impact of disregarding allometric scaling on putamen SBR, and to propose an easy-to-implement method to avoid this issue. 656 [123I]FP-CIT SPECT (67.2 ± 11.4y, 44.2

BACKGROUND:Single-subject voxel-based morphometry (VBM) is a powerful technique for reader-independent detection of brain atrophy in structural magnetic resonance imaging (MRI) to support the (differential) diagnosis and staging of neurodegenerative diseases in individual patients. However, VBM is sensitive to the MRI scanner platform and details of the acquisition sequence. To mitigate this limitation, we recently proposed and technically validated a convolutional neural network (CNN)-based VBM which does not rely on a normative reference database. OBJECTIVE:Clinical validation of CNN-based VBM. METHODS:CNN-based VBM was compared with conventional VBM based on a mixed-scanner normative database in 227 consecutive patients (66.0 ± 9.6 years, 53.3% female) with suspected dementing neurodegenerative disease. VBM maps were interpreted visually by two experienced readers, first with respect to the presence of any neurodegenerative disease, then for the differentiation between Alzheimer's disease (AD)-typical and non-AD atrophy patterns. A Likert 6-score was used for both tasks. Simultaneously acquired positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) served as reference standard. RESULTS:Repeated-measures ANOVA revealed a significant impact of the VBM method on the visual detection of any neurodegenerative disease (p < 0.001). Balanced accuracy/sensitivity/specificity were 80.4/86.3/74.5% for CNN-based VBM versus 75.7/79.5/71.8% for conventional VBM. Differentiation between AD and non-AD typical atrophy patterns did not differ between both VBM methods (p = 0.871). CONCLUSIONS:CNN-based VBM provides clinically useful accuracy for the detection of neurodegeneration-suspect atrophy with higher sensitivity than conventional VBM with a mixed-scanner normative reference database and without compromising specificity.

BACKGROUND:Diagnostic criteria for progressive supranuclear palsy (PSP) include midbrain atrophy in MRI and hypometabolism in [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) as supportive features. Due to limited data regarding their relative and sequential value, there is no recommendation for an algorithm to combine both modalities to increase diagnostic accuracy. This study evaluated the added value of sequential imaging using state-of-the-art methods to analyse the images regarding PSP features. METHODS:The retrospective study included 41 PSP patients, 21 with Richardson's syndrome (PSP-RS), 20 with variant PSP phenotypes (vPSP) and 46 sex- and age-matched healthy controls. A pretrained support vector machine (SVM) for the classification of atrophy profiles from automatic MRI volumetry was used to analyse T1w-MRI (output: MRI-SVM-PSP score). Covariance pattern analysis was applied to compute the expression of a predefined PSP-related pattern in FDG-PET (output: PET-PSPRP expression score). RESULTS:The area under the receiver operating characteristic curve for the detection of PSP did not differ between MRI-SVM-PSP and PET-PSPRP expression score (p≥0.63): about 0.90, 0.95 and 0.85 for detection of all PSP, PSP-RS and vPSP. The MRI-SVM-PSP score achieved about 13% higher specificity and about 15% lower sensitivity than the PET-PSPRP expression score. Decision tree models selected the MRI-SVM-PSP score for the first branching and the PET-PSPRP expression score for a second split of the subgroup with normal MRI-SVM-PSP score, both in the whole sample and when restricted to PSP-RS or vPSP. CONCLUSIONS:FDG-PET provides added value for PSP-suspected patients with normal/inconclusive T1w-MRI, regardless of PSP phenotype and the methods to analyse the images for PSP-typical features.

Single-subject voxel-based morphometry (VBM) compares an individual T1-weighted MRI to a sample of normal MRI in a normative database (NDB) to detect regional atrophy. Outliers in the NDB might result in reduced sensitivity of VBM. The primary aim of the current study was to propose a method for outlier removal (“NDB cleaning”) and to test its impact on the performance of VBM for detection of Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD). T1-weighted MRI of 81 patients with biomarker-confirmed AD (n = 51) or FTLD (n = 30) and 37 healthy subjects with simultaneous FDG-PET/MRI were included as test dataset. Two different NDBs were used: a scanner-specific NDB (37 healthy controls from the test dataset) and a non-scanner-specific NDB comprising 164 normal T1-weighted MRI from 164 different MRI scanners. Three different quality metrics based on leave-one-out testing of the scans in the NDB were implemented. A scan was removed if it was an outlier with respect to one or more quality metrics. VBM maps generated with and without NDB cleaning were assessed visually for the presence of AD or FTLD. Specificity of visual interpretation of the VBM maps for detection of AD or FTLD was 100