arXiv subjects

Dirk H. J. Poot

Publications and source records attributed to Dirk H. J. Poot.

10 recordsLinked to original sources

arXiv2026

Quiet, rapid 3D multiparametric mapping using magnetization-prepared zero echo time MRI

Purpose: To introduce and evaluate MuPa-ZTE, a quiet, rapid 3D framework combining native and magnetization-prepared zero echo time (ZTE) acquisitions for multiparametric mapping. Methods: MuPa-ZTE combines steady-state native ZTE with transient-state magnetization-prepared ZTE. Two implementations were evaluated: T2T1-ZTE for apparent proton density, T1, and T2 mapping, and T1-ZTE for apparent proton density and T1 mapping. Both were assessed in an ISMRM/NIST system phantom and two healthy volunteers; T2T1-ZTE was also demonstrated in a patient with brain metastases. The 10-minute phantom and 4.5-minute in vivo acquisitions were retrospectively truncated to 3, 2, and 1 minute and reconstructed with and without deep learning-based denoising. Evaluations included phantom agreement, precision, short-term repeatability, apparent SNR, edge sharpness, and consistency with full-duration in vivo maps. Results: T1 estimates remained close to nominal phantom values across implementations, durations, and reconstructions. T2 accuracy was maintained down to 2 minutes over the brain-relevant range, with limited sensitivity to longer T2 values. Denoising generally reduced variability and improved short-term repeatability. In vivo, 1.1-mm isotropic whole-brain maps were obtained in 4.5 minutes; denoising increased apparent SNR while preserving edge sharpness and yielded promising image quality after retrospective truncation to 2 minutes. Conclusion: MuPa-ZTE enables quiet, isotropic 3D multiparametric mapping within 4.5 minutes, supporting robust T1 mapping and T2 mapping over a brain-relevant range, with promising acceleration toward 2 minutes using deep learning-based denoising.

physics.med-ph↗

arXiv2026

CUPA-T2: Covariance-Aware Uncertainty Propagation and Alignment for T2 Mapping in Accelerated MRI

Quantitative T2* maps have strong potential for biomarker discovery but are limited by long scan times, rendering them impractical in clinical settings. Significant acceleration can be achieved through undersampling in k-space combined with learning-based reconstruction. However, reconstruction artifacts and noise can propagate into downstream T2* fitting, degrading its accuracy. We introduce CUPA-T2*, a framework that explicitly propagates voxel-wise inter-echo uncertainty from stochastic Monte Carlo dropout reconstructions to downstream T2* fitting via covariance-aware sampling. T2* fitting is performed with a heteroscedastic MLP and a correlation-based regularizer that encourages alignment between predicted variance and reconstruction uncertainty. Experiments on accelerated brain MRI data show tissue-dependent behavior: CUPA-T2* achieves competitive overall T2* fitting performance and improves white-matter performance at higher accelerations. Compared with a heteroscedastic baseline, the proposed framework substantially increases alignment between reconstruction uncertainty and predicted T2* variance, while also revealing a trade-off with calibration (ECE) and selective prediction performance (AURC). CUPA-T2* enables reconstruction uncertainty-aware T2* fitting and delivers voxel-wise uncertainty maps to support the interpretation of quantitative T2* estimates.

cs.CV↗

arXiv2026

Self-Supervised Weighted Image Guided Quantitative MRI Super-Resolution

Object: To present and evaluate Self-supervised Weighted Image Guided quantitative MRI Super-Resolution (SWIG qMRI SR), a physics-informed framework recovering high-resolution (HR) qMRI from a rapid low-resolution (LR) acquisition guided by routine weighted images (wMRI), without HR training targets. Materials and Methods: A CNN matches acquired wMRI to images synthesized from predicted maps through forward signal models, while anchoring those maps to the acquired LR qMRI. Training and ablation used synthetic data (n = 27); cross-sequence generalizability was tested in three volunteers scanned with silent 3D MuPa-ZTE at 1 and 5min, with clinical T1w/T2w guides. Results: In synthetic data, dual-guide SR reduced the T1 high-frequency error norm 45.4% below baseline; removing the LR qMRI anchor raised gray-matter T1 error from 63 to 110ms. In vivo, super-resolved 1-min maps synthesized T1w images (SSIM 0.93, PSNR 27.3dB) surpassing synthesis from 5-min maps (SSIM 0.83), and improved synthesis of T2-FLAIR, never used as a guide (SSIM 0.69 to 0.75). Discussion: HR detail was recovered without HR supervision, and the network transferred across a different qMRI sequence. Because the guides are already acquired, adding a 1-min qMRI scan to a standard exam offers a practical route to routine clinical qMRI integration.

cs.CV↗

arXiv2026

Evaluating and Calibrating Diffusion Model-derived Uncertainty for Quantitative MRI Mapping

Quantitative MRI (qMRI) provides standardised tissue parameter maps, but the reliability of deep learning-based qMRI mapping methods is often not explicitly characterised. In this work we systematically evaluate uncertainty maps for quantitative MRI derived from multiple inferences of a data-consistent diffusion model-based qMRI framework. Evaluation on synthetic test data assessed error-awareness, high-error detection, selective prediction, and Gaussian interval calibration. Diffusion model-derived uncertainty was positively associated with the mapping error, while risk-coverage analysis showed that excluding high-uncertainty voxels reduced the retained error. However, the raw uncertainty was poorly calibrated for quantitative interval interpretation. Calibration was substantially improved using a post-hoc procedure combining prediction-value-dependent bias correction with scalar uncertainty scaling. Qualitative evaluation on a healthy volunteer showed spatially meaningful uncertainty patterns. These results indicate that diffusion model-derived uncertainty is informative for reliability assessment and selective prediction, but requires calibration for quantitative interval interpretation.

cs.CV↗

arXiv2026

q3-MuPa: Quick, Quiet, Quantitative Multi-Parametric MRI using Physics-Informed Diffusion Models

The 3D fast silent multi-parametric mapping sequence with zero echo time (MuPa-ZTE) is a novel quantitative MRI (qMRI) acquisition that enables nearly silent scanning by using a 3D phyllotaxis sampling scheme. MuPa-ZTE improves patient comfort and motion robustness, and generates quantitative maps of T1, T2, and proton density using the acquired weighted image series. In this work, we propose a diffusion model-based qMRI mapping method that leverages both a deep generative model and physics-based data consistency to further improve the mapping performance. Furthermore, our method enables additional acquisition acceleration, allowing high-quality qMRI mapping from a fourfold-accelerated MuPa-ZTE scan (approximately 1 minute). Specifically, we trained a denoising diffusion probabilistic model (DDPM) to map MuPa-ZTE image series to qMRI maps, and we incorporated the MuPa-ZTE forward signal model as an explicit data consistency (DC) constraint during inference. We compared our mapping method against a baseline dictionary matching approach and a purely data-driven diffusion model. The diffusion models were trained entirely on synthetic data generated from digital brain phantoms, eliminating the need for large real-scan datasets. We evaluated on synthetic data, a NISM/ISMRM phantom, healthy volunteers, and a patient with brain metastases. The results demonstrated that our method produces 3D qMRI maps with high accuracy, reduced noise and better preservation of structural details. Notably, it generalised well to real scans despite training on synthetic data alone. The combination of the MuPa-ZTE acquisition and our physics-informed diffusion model is termed q3-MuPa, a quick, quiet, and quantitative multi-parametric mapping framework, and our findings highlight its strong clinical potential.

physics.med-ph↗

arXiv2024

qMRI Diffuser: Quantitative T1 Mapping of the Brain using a Denoising Diffusion Probabilistic Model

Quantitative MRI (qMRI) offers significant advantages over weighted images by providing objective parameters related to tissue properties. Deep learning-based methods have demonstrated effectiveness in estimating quantitative maps from series of weighted images. In this study, we present qMRI Diffuser, a novel approach to qMRI utilising deep generative models. Specifically, we implemented denoising diffusion probabilistic models (DDPM) for T1 quantification in the brain, framing the estimation of quantitative maps as a conditional generation task. The proposed method is compared with the residual neural network (ResNet) and the recurrent inference machine (RIM) on both phantom and in vivo data. The results indicate that our method achieves improved accuracy and precision in parameter estimation, along with superior visual performance. Moreover, our method inherently incorporates stochasticity, enabling straightforward quantification of uncertainty. Hence, the proposed method holds significant promise for quantitative MR mapping.

cs.CV↗

arXiv2021

Time efficiency analysis for undersampled quantitative MRI acquisitions

To realize Quantitative MRI (QMRI) with clinically acceptable scan time, acceleration factors achieved by conventional parallel imaging techniques are often inadequate. Further acceleration is possible using model-based reconstruction. We propose a theoretical metric called TEUSQA: Time Efficiency for UnderSampled QMRI Acquisitions to inform sequence design and sample pattern optimisation. TEUSQA is designed for a particular class of reconstruction techniques that directly estimate tissue parameters, possibly using prior information to regularize the estimation. TEUSQA can be used to evaluate undersampling patterns for multi-contrast QMRI sequences targeting any tissue parameter. To verify the time efficiency predicted by TEUSQA, we performed Monte Carlo simulations and an accelerated parameter mapping with two sequences (Inversion prepared fast spin echo for T1 and T2 mapping and 3D GRASE for T2 and B0 inhomogeneity mapping). Using TEUSQA, we assessed several ways to generate undersampling patterns in silico, providing insight into the relation between sample distribution and time efficiency for different acceleration factors. The time efficiency predicted by TEUSQA was within 15 % of that observed in the Monte Carlo simulations and the prospective acquisition experiment. The assessment of undersampling patterns showed that a class of good patterns could be obtained by low-discrepancy sampling. We believe that TEUSQA offers a valuable instrument for developers of novel QMRI sequences pushing the boundaries of acceleration to achieve clinically feasible protocols. Finally, we applied a time-efficient undersampling pattern selected using TEUSQA for a 32-fold accelerated scan to map T1 and T2 mapping of a healthy volunteer.

eess.SP↗

arXiv2019

An Efficient Method for Multi-Parameter Mapping in Quantitative MRI using B-Spline Interpolation

Quantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equations. Repeated Bloch simulations can be avoided by matching the measured signal with a precomputed signal dictionary on a discrete parameter grid, as used in MR Fingerprinting. However, accurate estimation requires discretizing each parameter with a high resolution and consequently high computational and memory costs for dictionary generation, storage, and matching. Here, we reduce the required parameter resolution by approximating the signal between grid points through B-spline interpolation. The interpolant and its gradient are evaluated efficiently which enables a least-squares fitting method for parameter mapping. The resolution of each parameter was minimized while obtaining a user-specified interpolation accuracy. The method was evaluated by phantom and in-vivo experiments using fully-sampled and undersampled unbalanced (FISP) MR fingerprinting acquisitions. Bloch simulations incorporated relaxation effects ($T_1,T_2$), proton density ($PD$), receiver phase ($ϕ_0$), transmit field inhomogeneity ($B_1^+$), and slice profile. Parameter maps were compared with those obtained from dictionary matching, where the parameter resolution was chosen to obtain similar signal (interpolation) accuracy. For both the phantom and the in-vivo acquisition, the proposed method approximated the parameter maps obtained through dictionary matching while reducing the parameter resolution in each dimension ($T_1,T_2,B_1^+$) by, on average, an order of magnitude. In effect, the applied dictionary was reduced from 1.47 GB to 464 KB. Dictionary fitting with B-spline interpolation reduces the computational and memory costs of dictionary-based methods and is therefore a promising method for multi-parametric mapping.

eess.SP↗

arXiv2019

APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network

Deep learning has been successfully demonstrated in MRI reconstruction of accelerated acquisitions. However, its dependence on representative training data limits the application across different contrasts, anatomies, or image sizes. To address this limitation, we propose an unsupervised, auto-calibrated k-space completion method, based on a uniquely designed neural network that reconstructs the full k-space from an undersampled k-space, exploiting the redundancy among the multiple channels in the receive coil in a parallel imaging acquisition. To achieve this, contrary to common convolutional network approaches, the proposed network has a decreasing number of feature maps of constant size. In contrast to conventional parallel imaging methods such as GRAPPA that estimate the prediction kernel from the fully sampled autocalibration signals in a linear way, our method is able to learn nonlinear relations between sampled and unsampled positions in k-space. The proposed method was compared to the start-of-the-art ESPIRiT and RAKI methods in terms of noise amplification and visual image quality in both phantom and in-vivo experiments. The experiments indicate that APIR-Net provides a promising alternative to the conventional parallel imaging methods, and results in improved image quality especially for low SNR acquisitions.

eess.SP↗

arXiv2016

Diffusion MRI microstructure models with in vivo human brain Connectom data: results from a multi-group comparison

A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI) and infer properties about the white matter microstructure. However, a head-to-head comparison of DW-MRI models is critically missing in the field. To address this deficiency, we organized the "White Matter Modeling Challenge" during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed at identifying the DW-MRI models that best predict unseen DW data. in vivo DW-MRI data was acquired on the Connectom scanner at the A.A.Martinos Center (Massachusetts General Hospital) using gradients strength of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three-quarters of the whole dataset, and their models were ranked on their ability to predict the remaining unseen quarter of data. In this paper we provide both an overview and a more in-depth description of each evaluated model, report the challenge results, and infer trends about the model characteristics that were associated with high model ranking. This work provides a much needed benchmark for DW-MRI models. The acquired data and model details for signal prediction evaluation are provided online to encourage a larger scale assessment of diffusion models in the future.

physics.med-ph↗