Estimation of brain amyloid accumulation using deep learning in clinical [11C]PiB PET imaging

INTRODUCTION

Estimation of brain amyloid accumulation is valuable for evaluation of patients with cognitive impairment in both research and clinical routine. The development of high throughput and accurate strategies for the determination of amyloid status could be an important tool in patient selection for clinical trials and amyloid directed treatment. Here, we propose the use of deep learning to quantify amyloid accumulation using standardized uptake value ratio (SUVR) and classify amyloid status based on their PET images.

METHODS

A total of 1309 patients with cognitive impairment scanned with [¹¹C]PIB PET/CT or PET/MRI were included. Two convolutional neural networks (CNNs) for reading-based amyloid status and SUVR prediction were trained using 75% of the PET/CT data. The remaining PET/CT (n = 300) and all PET/MRI (n = 100) data was used for evaluation.

RESULTS

The prevalence of amyloid positive patients was 61%. The amyloid status classification model reproduced the expert reader's classification with 99% accuracy. There was a high correlation between reference and predicted SUVR (R² = 0.96). Both reference and predicted SUVR had an accuracy of 97% compared to expert classification when applying a predetermined SUVR threshold of 1.35 for binary classification of amyloid status.

CONCLUSION

The proposed CNN models reproduced both the expert classification and quantitative measure of amyloid accumulation in a large local dataset. This method has the potential to replace or simplify existing clinical routines and can facilitate fast and accurate classification well-suited for a high throughput pipeline.

DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants

Purpose

Conventional magnetic resonance imaging (MRI) can for glioma assessment be supplemented by positron emission tomography (PET) imaging with radiolabeled amino acids such as O-(2-[¹⁸F]fluoroethyl)-L-tyrosine ([¹⁸F]FET), which provides additional information on metabolic properties. In neuro-oncology, patients often undergo brain and skull altering treatment, which is known to challenge MRI-based attenuation correction (MR-AC) methods and thereby impact the simplified semi-quantitative measures such as tumor-to-brain ratio (TBR) used in clinical routine. The aim of the present study was to examine the applicability of our deep learning method, DeepDixon, for MR-AC in [¹⁸F]FET PET/MRI scans of a post-surgery glioma cohort with metal implants.

Methods

The MR-AC maps were assessed for all 194 included post-surgery glioma patients (318 studies). The subgroup of 147 patients (222 studies, 200 MBq [¹⁸F]FET PET/MRI) with tracer uptake above 1 ml were subsequently reconstructed with DeepDixon, vendor-default atlas-based method, and a low-dose computed tomography (CT) used as reference. The biological tumor volume (BTV) was delineated on each patient by isocontouring tracer uptake above a TBR threshold of 1.6. We evaluated the MR-AC methods using the recommended clinical metrics BTV and mean and maximum TBR on a patient-by-patient basis against the reference with CT-AC.

Results

Ninety-seven percent of the studies (310/318) did not have any major artifacts using DeepDixon, which resulted in a Dice coefficient of 0.89/0.83 for tissue/bone, respectively, compared to 0.84/0.57 when using atlas. The average difference between DeepDixon and CT-AC was within 0.2% across all clinical metrics, and no statistically significant difference was found. When using DeepDixon, only 3 out of 222 studies (1%) exceeded our acceptance criteria compared to 72 of the 222 studies (32%) with the atlas method.

Conclusion

We evaluated the performance of a state-of-the-art MR-AC method on the largest post-surgical glioma patient cohort to date. We found that DeepDixon could overcome most of the issues arising from irregular anatomy and metal artifacts present in the cohort resulting in clinical metrics within acceptable limits of the reference CT-AC in almost all cases. This is a significant improvement over the vendor-provided atlas method and of particular importance in response assessment.

[18F]FE-PE2I PET is a feasible alternative to [123I]FP-CIT SPECT for dopamine transporter imaging in clinically uncertain parkinsonism

BACKGROUND

Dopamine transporter (DAT) imaging of striatum is clinically used in Parkinson's disease (PD) and neurodegenerative parkinsonian syndromes (PS) especially in the early disease stages. The aim of the present study was to evaluate the diagnostic performance of the recently developed tracer for DAT imaging [¹⁸F]FE-PE2I PET/CT to the reference standard [¹²³I]FP-CIT SPECT.

METHODS

Ninety-eight unselected patients referred for DAT imaging were included prospectively and consecutively and evaluated with [¹⁸F]FE-PE2I PET/CT and [¹²³I]FP-CIT SPECT on two separate days. PET and SPECT scans were categorized independently by two blinded expert readers as either normal, vascular changes, or mixed. Semiquantitative values were obtained for each modality and compared regarding effect size using Glass' delta.

RESULTS

Fifty-six of the [¹²³I]FP-CIT SPECT scans were considered abnormal (52 caused by PS, 4 by infarctions). Using [¹⁸F]FE-PE2I PET/CT, 95 of the 98 patients were categorized identically to SPECT as PS or non-PS with a sensitivity of 0.94 [0.84-0.99] and a specificity of 1.00 [0.92-1.00]. Inter-reader agreement for [¹⁸F]FE-PE2I PET with a kappa of 0.97 [0.89-1.00] was comparable to the agreement for [¹²³I]FP-CIT SPECT of 0.96 [0.76-1.00]. Semiquantitative values for short 10-min reconstructions of [¹⁸F]FE-PE2I PET/CT were comparable to longer reconstructions. The effect size for putamen/caudate nucleus ratio was significantly increased using PET compared to SPECT.

CONCLUSIONS

The high correspondence of [¹⁸F]FE-PE2I PET compared to reference standard [¹²³I]FP-CIT SPECT establishes [¹⁸F]FE-PE2I PET as a feasible PET tracer for clinical use with favourable scan logistics.

Deep learning based low-activity PET reconstruction of [11C]PiB and [18F]FE-PE2I in neurodegenerative disorders

PURPOSE

Positron Emission Tomography (PET) can support a diagnosis of neurodegenerative disorder by identifying disease-specific pathologies. Our aim was to investigate the feasibility of using activity reduction in clinical [¹⁸F]FE-PE2I and [¹¹C]PiB PET/CT scans, simulating low injected activity or scanning time reduction, in combination with AI-assisted denoising.

METHODS

A total of 162 patients with clinically uncertain Alzheimer's disease underwent amyloid [¹¹C]PiB PET/CT and 509 patients referred for clinically uncertain Parkinson's disease underwent dopamine transporter (DAT) [¹⁸F]FE-PE2I PET/CT. Simulated low-activity data were obtained by random sampling of 5% of the events from the list-mode file and a 5% time window extraction in the middle of the scan. A three-dimensional convolutional neural network (CNN) was trained to denoise the resulting PET images for each disease cohort.

RESULTS

Noise reduction of low-activity PET images was successful for both cohorts using 5% of the original activity with improvement in visual quality and all similarity metrics with respect to the ground-truth images. Clinically relevant metrics extracted from the low-activity images deviated <2% compared to ground-truth values, which were not significantly changed when extracting the metrics from the denoised images.

CONCLUSION

The presented models were based on the same network architecture and proved to be a robust tool for denoising brain PET images with two widely different tracer distributions (delocalized, ([¹¹C]PiB, and highly localized, [18F]FE-PE2I). This broad and robust application makes the presented network a good choice for improving the quality of brain images to the level of the standard-activity images without degrading clinical metric extraction. This will allow for reduced dose or scan time in PET/CT to be implemented clinically.

Selective diagnostic cervical nerve root block--correlation with clinical symptoms and MRI-pathology

BACKGROUND

The aim of this study is to describe the method of a cervical selective diagnostic nerve root block (SNRB) technique and assess its ability to correlate clinical symptoms with MRI findings in patients with cervical radicular pain and a single level degenerative disease.

METHODS

Twenty consecutive patients with cervical radiculopathy and correlating single level MRI pathology were studied. All patients underwent clinical investigation and neck and arm pain measurement with visual analog scales (VAS). The last 10 consecutive patients also underwent provocation with active neck motion when arm and neck pain were measured. They all underwent SNRB and 1 ml local-anesthesia (Mepivacaine 10 mg/ml) was injected, with the aid of fluoroscopy, close to the nerve-root. The VAS estimation and clinical investigation including provocation were repeated 30 minutes after the block. Criteria for a positive block response are a significant subjective pain reduction and at least 50% VAS pain reduction in the arm.

FINDINGS

For the whole group mean VAS arm pain reductions were 86% and mean VAS neck pain reductions were 65%. When the results from the provocation were added all patients had a positive block. Eighteen were operated on by an anterior procedure and all 18 were free from radicular pain at follow up.

INTERPRETATION

The block procedure seems relevant for revealing a relationship between radiological pathology and clinical symptoms and signs.