Local All-Pass Geometric Deformations

Free-breathing Arterial Spin Labeling MRI for the Detection of Pulmonary Embolism

Background Arterial spin labeling (ASL) MRI can be used to assess organ perfusion but has yet to be implemented for perfusion evaluation of the lung. Purpose To evaluate pseudo-continuous ASL (PCASL) MRI for the detection of acute pulmonary embolism (PE) and its potential as an alternative to CT pulmonary angiography (CTPA). Materials and Methods Between November 2020 and November 2021, 97 patients (median age, 61 years; 48 women) with suspected PE were enrolled in this prospective study. PCASL MRI was performed within a 72-hour period following CTPA under free-breathing conditions and included three orthogonal planes. The pulmonary trunk was labeled during systole, and the image was acquired during diastole of the subsequent cardiac cycle. Additionally, multisection, coronal, balanced, steady-state free-precession imaging was carried out. Two radiologists blindly assessed overall image quality, artifacts, and diagnostic confidence (five-point Likert scale, 5 = best). Patients were categorized as positive or negative for PE, and a lobe-wise assessment in PCASL MRI and CTPA was conducted. Sensitivity and specificity were calculated on a patient level with the final clinical diagnosis serving as the reference standard. Interchangeability between MRI and CTPA was also tested with use of an individual equivalence index (IEI). Results PCASL MRI was performed successfully in all patients with high scores for image quality, artifact, and diagnostic confidence (κ ≥ .74). Of the 97 patients, 38 were positive for PE. PCASL MRI depicted PE correctly in 35 of 38 patients with three false-positive and three false-negative findings, resulting in a sensitivity of 35 of 38 patients (92% [95% CI: 79, 98]) and a specificity of 56 of 59 patients (95% [95% CI: 86, 99]). Interchangeability analysis revealed an IEI of 2.6% (95% CI: 1.2, 3.8). Conclusion Free-breathing pseudo-continuous arterial spin labeling MRI depicted abnormal lung perfusion caused by acute pulmonary embolism and may be useful as a contrast material-free alternative to CT pulmonary angiography for selected patients. German Clinical Trials Register no. DRKS00023599 © RSNA, 2023.

LAPNet: Non-Rigid Registration Derived in k-Space for Magnetic Resonance Imaging

Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts. Motion correction techniques have been proposed to compensate for these types of motion during thoracic scans, relying on accurate motion estimation from undersampled motion-resolved reconstruction. A particular interest and challenge lie in the derivation of reliable non-rigid motion fields from the undersampled motion-resolved data. Motion estimation is usually formulated in image space via diffusion, parametric-spline, or optical flow methods. However, image-based registration can be impaired by remaining aliasing artifacts due to the undersampled motion-resolved reconstruction. In this work, we describe a formalism to perform non-rigid registration directly in the sampled Fourier space, i.e. k-space. We propose a deep-learning based approach to perform fast and accurate non-rigid registration from the undersampled k-space data. The basic working principle originates from the Local All-Pass (LAP) technique, a recently introduced optical flow-based registration. The proposed LAPNet is compared against traditional and deep learning image-based registrations and tested on fully-sampled and highly-accelerated (with two undersampling strategies) 3D respiratory motion-resolved MR images in a cohort of 40 patients with suspected liver or lung metastases and 25 healthy subjects. The proposed LAPNet provided consistent and superior performance to image-based approaches throughout different sampling trajectories and acceleration factors.

All-pass Parametric Image Registration

Image registration is a required step in many practical applications that involve the acquisition of multiple related images. In this paper, we propose a methodology to deal with both the geometric and intensity transformations in the image registration problem. The main idea is to modify an accurate and fast elastic registration algorithm (Local All-Pass-LAP) so that it returns a parametric displacement field, and to estimate the intensity changes by fitting another parametric expression. Although we demonstrate the methodology using a low-order parametric model, our approach is highly flexible and easily allows substantially richer parametrisations, while requiring only limited extra computation cost. In addition, we propose two novel quantitative criteria to evaluate the accuracy of the alignment of two images ("salience correlation") and the number of degrees of freedom ("parsimony") of a displacement field, respectively. Experimental results on both synthetic and real images demonstrate the high accuracy and computational efficiency of our methodology. Furthermore, we demonstrate that the resulting displacement fields are more parsimonious than the ones obtained in other state-of-the-art image registration approaches.