PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction

Quantitative Magnetic Resonance Imaging (qMRI) enables the reproducible measurement of biophysical parameters in tissue. The challenge lies in solving a nonlinear, ill-posed inverse problem to obtain the desired tissue parameter maps from acquired raw data. While various learned and non-learned approaches have been proposed, the existing learned methods fail to fully exploit the prior knowledge about the underlying MR physics, i.e. the signal model and the acquisition model. In this paper, we propose PINQI, a novel qMRI reconstruction method that integrates the knowledge about the signal, acquisition model, and learned regularization into a single end-to-end trainable neural network. Our approach is based on unrolled alternating optimization, utilizing differentiable optimization blocks to solve inner linear and non-linear optimization tasks, as well as convolutional layers for regularization of the intermediate qualitative images and parameter maps. This design enables PINQI to leverage the advantages of both the signal model and learned regularization. We evaluate the performance of our proposed network by comparing it with recently published approaches in the context of highly undersampled $T_1$-mapping, using both a simulated brain dataset, as well as real scanner data acquired from a physical phantom and in-vivo data from healthy volunteers. The results demonstrate the superiority of our proposed solution over existing methods and highlight the effectiveness of our method in real-world scenarios.Comment: This work has been accepted for publication in IEEE Transactions on Computational Imaging. Changes were made to this version by the publisher before publication. IEEE Transactions on Computational Imaging (2024

Biomechanical comparison of fatigue and load-bearing -performance of elastic stable intramedullary nailing

Purpose: Elastic stable intramedullary nailing (ESIN) is a very common method for the treatment of pediatric long bone fractures. Because of the fact that ESIN nails offer the chance of micromotion during the healing process, this method is beneficial in comparison to rigid bone fixation and stimulates the formation of a callus [1]. The time between the incident of the fracture and complete generation of the stabilizing callus seems to be a critical phase for the implants’ load-bearing. Torsional and axial stability has to be ensured by the ESIN implant during this phase. Methods: Because of the studies aim of monitoring the period until the formation of a callus, ovine cadaver -tibiae (3–4 months old) were implanted regarding clinical standards after osteotomy at the mid diaphyseal region. Four different combinations of locking systems and ESIN implants were observed during this study. Synthes TEN -Titanium with endcaps (n = 7), Hofer Medical HSNesin Titanium unlocked (n = 8), Hofer Medical STEN Steel with eye and 3-mm screw (n = 8), and Hofer Medical HSNesin Titanium with plug and 3mm screw (n = 8) were used. All nails were 3 mm in diameter. Cyclic mechanical loading was applied using a commercial uniaxial testing device (1710DLL-5KN, Dynamess, Germany), and a pneumatic torsion testing module which was constructed by one of the authors. This device is able to apply axial load and torque to the specimen simultaneously. Results: Juvenile ovine bones were used in this study to generate similar conditions as in pediatric long bones. All samples failed by a closure of the initial osteotomy gap of 10 mm. The results of biomechanical tests showed significantly higher load bearing capability with each interlocking system than with the unlocked ESIN. (1000 N max. compared with 200 N). The unlocked system and the endcap ESIN failed very abrupt, whereas the 3-mm plug and the steel system failed slowly. Above all, the 3-mm plug with steel ESIN experienced gap closure without any damage to plugs or screws, which led to a distal penetration of the diaphysis by the nails. Conclusions: Interlocking systems seem to be beneficial for stability of ESIN nailing under cyclic and simultaneous axial and torsional loading. The strongest combinations in this study were Hofer steel nails and Hofer plugs with 3-mm locking screws. Significance: Different combinations of ESIN nails and interlocking systems show diverse load bearing behaviors. Desirable characteristics of nonabrupt failure during the nails loading and maximal strength of interlocking systems could be established. REFERENCE [1] Bishop, N.E., van Rhijn, M., Tami, I., Corveleijn, R., Schneider, E., Ito, K. Shear does not necessarily inhibit bone healing. Clinical Orthopaedics and Related Research. 443

Multilevel comparison of deep learning models for function quantification in cardiovascular magnetic resonance: On the redundancy of architectural variations

Background: Cardiac function quantification in cardiovascular magnetic resonance requires precise contouring of the heart chambers. This time-consuming task is increasingly being addressed by a plethora of ever more complex deep learning methods. However, only a small fraction of these have made their way from academia into clinical practice. In the quality assessment and control of medical artificial intelligence, the opaque reasoning and associated distinctive errors of neural networks meet an extraordinarily low tolerance for failure. Aim: The aim of this study is a multilevel analysis and comparison of the performance of three popular convolutional neural network (CNN) models for cardiac function quantification. Methods: U-Net, FCN, and MultiResUNet were trained for the segmentation of the left and right ventricles on short-axis cine images of 119 patients from clinical routine. The training pipeline and hyperparameters were kept constant to isolate the influence of network architecture. CNN performance was evaluated against expert segmentations for 29 test cases on contour level and in terms of quantitative clinical parameters. Multilevel analysis included breakdown of results by slice position, as well as visualization of segmentation deviations and linkage of volume differences to segmentation metrics via correlation plots for qualitative analysis. Results: All models showed strong correlation to the expert with respect to quantitative clinical parameters (r(z)(') = 0.978, 0.977, 0.978 for U-Net, FCN, MultiResUNet respectively). The MultiResUNet significantly underestimated ventricular volumes and left ventricular myocardial mass. Segmentation difficulties and failures clustered in basal and apical slices for all CNNs, with the largest volume differences in the basal slices (mean absolute error per slice: 4.2 +/- 4.5 ml for basal, 0.9 +/- 1.3 ml for midventricular, 0.9 +/- 0.9 ml for apical slices). Results for the right ventricle had higher variance and more outliers compared to the left ventricle. Intraclass correlation for clinical parameters was excellent (>= 0.91) among the CNNs. Conclusion: Modifications to CNN architecture were not critical to the quality of error for our dataset. Despite good overall agreement with the expert, errors accumulated in basal and apical slices for all models

Respiratory-resolved MR-based attenuation correction for motion-compensated cardiac PET-MR

Respiratory motion during cardiac PET acquisitions can cause image blurring and erroneous uptake quantification. In particular the misalignment of attenuation correction (AC) maps and PET emission data can lead to severe quantification errors, because the AC value of the heart is five times higher than of the surrounding lung tissue. Standard PET-MR approaches assume accurate alignment between breathhold MR-based AC maps and free-breathing PET emission data but cannot necessarily ensure it. Here we propose a 75 s free-breathing MR-acquisition, which provides respiratory-resolved AC maps (ACDyn) and non-rigid respiratory motion information. This approach ensures accurate AC for free-breathing PET data and the motion information can be utilized to reduce image blurring caused by respiratory motion. 3D multi-echo MR data was acquired during a 75 s free-breathing scan in six patients. Both a respiratory-resolved dynamic AC map (ACDyn) and a non-rigid respiratory motion field are provided by the MR scan. ACDyn yielded AC values for different breathing phases ensuring accurate AC for each respiratory phase of the free-breathing PET data. In addition, motion-corrected image reconstruction (MCIR) of MR and PET data was used to minimize breathing artefacts. Motion amplitudes in the left ventricle were 8.2 ± 2.9 mm with a dominant motion direction along the anterior-anterolateral and inferior-inferoseptal axis of the heart. The proposed ACDyn-MCIR technique led to significant signal recovery of PET tracer uptake by 24 ± 5% (p &lt; 0.05). The maximum improvement was observed in patients with large misalignment between standard breathhold MR-based AC maps and PET emission data. PET image resolution was improved by 20 ± 12% (p &lt; 0.05). We have presented an efficient MR-scan, which ensures accurate motion information and AC values to improve PET quantification for cardiac PET-MR scans. The short scan time of 75 s makes this free-breathing acquisition easy to integrate into standard clinical PET-MR protocols.</p

Joint cardiac and respiratory motion estimation for motion-corrected cardiac PET-MR

Respiratory and cardiac motion can strongly impair cardiac PET image quality and tracer uptake quantification. Standard gating techniques can minimize these motion artefacts but suffer from low signal-to-noise ratio because only a small percentage of the total data is utilized. Motion correction approaches have been proposed to overcome this problem but require accurate knowledge of such physiological motion. Here we present a joint PET-MR motion estimation approach which combines complimentary dynamic image information from simultaneously acquired MR and PET to ensure improved cardiac and respiratory motion estimation for motion-corrected image reconstruction (MCIR) of PET images. A 3D triple-echo Dixon MR scan is used both for calculation of MR-based attenuation correction (AC) maps and estimation of physiological motion. PET listmode data is obtained simultaneously to the MR acquisition which is used for a joint motion estimation and reconstruction of the final MCIR PET. In a first step, dynamic cardiac and respiratory motion resolved 4D MR and PET images are reconstructed. These image series are used in a joint image registration to estimate non-rigid cardiac and respiratory motion fields. In a second step, the motion fields are utilized in a MR MCIR to obtain cardiac and respiratory resolved dynamic MR-based AC maps. In the last step, the non-rigid motion fields and the dynamic AC maps are applied in a PET MCIR to obtain the final motion-corrected PET images. PET-MR data has been obtained in six patients without any known heart disease. Motion amplitudes were between 5.6 and 16 mm, with higher values in the basal compared to the mid-ventricular and apical segments. The proposed joint PET-MR motion estimation provided more accurate motion estimation than using either modality separately. The underestimation of PET uptake due to respiratory and cardiac motion artefacts in the AC maps was up to 17%. The average increase in uptake values using MCIR was 23% ± 10% (p &lt; 0.0001), with values of 28% ± 11% (p &lt; 0.0001) for basal, 21% ± 8% (p &lt; 0.0001) for mid-cavity and 17% ± 7% (p &lt; 0.0001) for apical segments. With the proposed scheme we could ensure high PET image quality and improve local PET uptake quantification by up to 30%. Attenuation correction and motion information was obtained from the same PET-MR raw data, which was obtained during free-breathing to minimize scan times and to increase patient comfort.</p

Neural networks-based regularization for large-scale medical image reconstruction

In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks (NNs) and cascaded NNs have been reported to achieve state-of-the-art results with respect to various quantitative quality measures as PSNR, NRMSE and SSIM across different imaging modalities. However, the fact that these approaches employ the application of the forward and adjoint operators repeatedly in the network architecture requires the network to process the whole images or volumes at once, which for some applications is computationally infeasible. In this work, we follow a different reconstruction strategy by strictly separating the application of the NN, the regularization of the solution and the consistency with the measured data. The regularization is given in the form of an image prior obtained by the output of a previously trained NN which is used in a Tikhonov regularization framework. By doing so, more complex and sophisticated network architectures can be used for the removal of the artefacts or noise than it is usually the case in iterative NNs. Due to the large scale of the considered problems and the resulting computational complexity of the employed networks, the priors are obtained by processing the images or volumes as patches or slices. We evaluated the method for the cases of 3D cone-beam low dose CT and undersampled 2D radial cine MRI and compared it to a total variation-minimization-based reconstruction algorithm as well as to a method with regularization based on learned overcomplete dictionaries. The proposed method outperformed all the reported methods with respect to all chosen quantitative measures and further accelerates the regularization step in the reconstruction by several orders of magnitude

Fast myocardial T(1) mapping using cardiac motion correction

PURPOSE: To improve the efficiency of native and postcontrast high-resolution cardiac T(1) mapping by utilizing cardiac motion correction. METHODS: Common cardiac T(1) mapping techniques only acquire data in a small part of the cardiac cycle, leading to inefficient data sampling. Here, we present an approach in which 80% of each cardiac cycle is used for T(1) mapping by integration of cardiac motion correction. Golden angle radial data was acquired continuously for 8 s with in-plane resolution of 1.3 × 1.3 mm(2). Cine images were reconstructed for nonrigid cardiac motion estimation. Images at different TIs were reconstructed from the same data, and motion correction was performed prior to T(1) mapping. Native T(1) mapping was evaluated in healthy subjects. Furthermore, the technique was applied for postcontrast T(1) mapping in 5 patients with suspected fibrosis. RESULTS: Cine images with high contrast were obtained, leading to robust cardiac motion estimation. Motion-corrected T(1) maps showed myocardial T(1) times similar to cardiac-triggered T(1) maps obtained from the same data (1288 ± 49 ms and 1259 ± 55 ms, respectively) but with a 34% improved precision (spatial variation: 57.0 ± 12.5 ms and 94.8 ± 15.4 ms, respectively, P < 0.0001) due to the increased amount of data. In postcontrast T(1) maps, focal fibrosis could be confirmed with late contrast-enhancement images. CONCLUSION: The proposed approach provides high-resolution T(1) maps within 8 s. Data acquisition efficiency for T(1) mapping was improved by a factor of 5 by integration of cardiac motion correction, resulting in precise T(1) maps

Motion corrected compressed sensing for free-breathing dynamic cardiac MRI

Compressed sensing (CS) has been demonstrated to accelerate MRI acquisitions by reconstructing sparse images of good quality from highly undersampled data. Motion during MR scans can cause inconsistencies in k-space data, resulting in strong motion artifacts in the reconstructed images. For CS to be useful in these applications, motion correction techniques need to be combined with the undersampled reconstruction. Recently, joint motion correction and CS approaches have been proposed to partially correct for effects of motion. However, the main limitation of these approaches is that they can only correct for affine deformations. In this work, we propose a novel motion corrected CS framework for free-breathing dynamic cardiac MRI that incorporates a general motion correction formulation directly into the CS reconstruction. This framework can correct for arbitrary affine or nonrigid motion in the CS reconstructed cardiac images, while simultaneously benefiting from highly accelerated MR acquisition. The application of this approach is demonstrated both in simulations and in vivo data for 2D respiratory self-gated free-breathing cardiac CINE MRI, using a golden angle radial acquisition. Results show that this approach allows for the reconstruction of respiratory motion corrected cardiac CINE images with similar quality to breath-held acquisitions