Adam Johansson

All Publications

• Decision support for daily adaptive prostate radiotherapy Tilly, D., Fransson, S., Lundmark, M., Johansson, A., Isacsson, U., Radu, C., Flejmen, A. A. K., Kyriakogiannaki, A., Tilly, N. ELSEVIER IRELAND LTD. 2025: S3134-S3136

  View details for Web of Science ID 001512508000022

• Vendor-independent validation of the MR-to-MV isocentre shift at two MRI-linacs Riis, H., Fietje, A., Buthler, B., Bertelsen, A., Christiansen, R., Engstrom, K., Johansson, A., Tilly, D., Brink, C., Bernchou, U. ELSEVIER IRELAND LTD. 2024: S4773-S4776

  View details for Web of Science ID 001331355606006

• Volumetric MRI with sparse sampling for MR-guided 3D motion tracking via sparse prior-augmented implicit neural representation learning. Medical physics Liu, L., Shen, L., Johansson, A., Balter, J. M., Cao, Y., Vitzthum, L., Xing, L. 2023

  Abstract

  Volumetric reconstruction of magnetic resonance imaging (MRI) from sparse samples is desirable for 3D motion tracking and promises to improve magnetic resonance (MR)-guided radiation treatment precision. Data-driven sparse MRI reconstruction, however, requires large-scale training datasets for prior learning, which is time-consuming and challenging to acquire in clinical settings.To investigate volumetric reconstruction of MRI from sparse samples of two orthogonal slices aided by sparse priors of two static 3D MRI through implicit neural representation (NeRP) learning, in support of 3D motion tracking during MR-guided radiotherapy.A multi-layer perceptron network was trained to parameterize the NeRP model of a patient-specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first training the network to learn the NeRP of two static 3D MRI with different breathing motion states, prior information of patient breathing motion was embedded into network weights through optimization. The prior information was then augmented from two motion states to 31 motion states by querying the optimized network at interpolated and extrapolated motion state coordinates. Starting from the prior-augmented NeRP model as an initialization point, we further trained the network to fit sparse samples of two orthogonal MRI slices and the final volumetric reconstruction was obtained by querying the trained network at 3D spatial locations. We evaluated the proposed method using 5-min volumetric MRI time series with 340 ms temporal resolution for seven abdominal patients with hepatocellular carcinoma, acquired using golden-angle radial MRI sequence and reconstructed through retrospective sorting. Two volumetric MRI with inhale and exhale states respectively were selected from the first 30 s of the time series for prior embedding and augmentation. The remaining 4.5-min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to two orthogonal slices and compared model-reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking.Across the seven patients evaluated, the peak signal-to-noise-ratio between model-reconstructed and ground truth MR images was 38.02 ± 2.60 dB and the structure similarity index measure was 0.98 ± 0.01. Throughout the 4.5-min time period, gross tumor volume (GTV) motion estimated by deforming a reference state MRI to model-reconstructed and ground truth MRI showed good consistency. The 95-percentile Hausdorff distance between GTV contours was 2.41 ± 0.77 mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1 mm in all three orthogonal directions.A prior-augmented NeRP model has been developed to reconstruct volumetric MRI from sparse samples of orthogonal cine slices. Only one exhale and one inhale 3D MRI were needed to train the model to learn prior information of patient breathing motion for sparse image reconstruction. The proposed model has the potential of supporting 3D motion tracking during MR-guided radiotherapy for improved treatment precision and promises a major simplification of the workflow by eliminating the need for large-scale training datasets.

  View details for DOI 10.1002/mp.16845

  View details for PubMedID 38014764

• Real Time Volumetric MRI for 3D Motion Tracking via Geometry-Informed Deep Learning. Medical physics Liu, L., Shen, L., Johansson, A., Balter, J. M., Cao, Y., Chang, D., Xing, L. 2022

  Abstract

  To develop a geometry-informed deep learning framework for volumetric MRI with sub-second acquisition time in support of 3D motion tracking, which is highly desirable for improved radiotherapy precision but hindered by the long image acquisition time.A 2D-3D deep learning network with an explicitly defined geometry module that embeds geometric priors of the k-space encoding pattern was investigated, where a 2D generation network first augmented the sparsely sampled image dataset by generating new 2D representations of the underlying 3D subject. A geometry module then unfolded the 2D representations to the volumetric space. Finally, a 3D refinement network took the unfolded 3D data and outputted high-resolution volumetric images. Patient-specific models were trained for 7 abdominal patients to reconstruct volumetric MRI from both orthogonal cine slices and sparse radial samples. To evaluate the robustness of the proposed method to longitudinal patient anatomy and position changes, we tested the trained model on separate datasets acquired more than one month later and evaluated 3D target motion tracking accuracy using the model-reconstructed images by deforming a reference MRI with gross tumor volume (GTV) contours to a 5-min time series of both ground truth and model-reconstructed volumetric images with a temporal resolution of 340 ms.Across the 7 patients evaluated, the median distances between model-predicted and ground truth GTV centroids in the superior-inferior direction were 0.4±0.3 mm and 0.5±0.4 mm for cine and radial acquisitions respectively. The 95-percentile Hausdorff distances between model-predicted and ground truth GTV contours were 4.7±1.1 mm and 3.2±1.5 mm for cine and radial acquisitions, which are of the same scale as cross-plane image resolution.Incorporating geometric priors into deep learning model enables volumetric imaging with high spatial and temporal resolution, which is particularly valuable for 3D motion tracking and has the potential of greatly improving MRI-guided radiotherapy precision. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1002/mp.15822

  View details for PubMedID 35766221

• Volumetric prediction of breathing and slow drifting motion in the abdomen using radial MRI and multi-temporal resolution modeling. Physics in medicine and biology Liu, L., Johansson, A., Cao, Y., Lawrence, T. S., Balter, J. M. 2021; 66 (17)

  Abstract

  Abdominal organ motions introduce geometric uncertainties to radiotherapy. This study investigates a multi-temporal resolution 3D motion prediction scheme that accounts for both breathing and slow drifting motion in the abdomen in support of MRI-guided radiotherapy. Ten-minute MRI scans were acquired for 8 patients using a volumetric golden-angle stack-of-stars sequence. The first five-minutes was used for patient-specific motion modeling. Fast breathing motion was modeled from high temporal resolution radial k-space samples, which served as a navigator signal to sort k-space data into different bins for high spatial resolution reconstruction of breathing motion states. Slow drifting motion was modeled from a lower temporal resolution image time series which was reconstructed by sequentially combining a large number of breathing-corrected k-space samples. Principal components analysis (PCA) was performed on deformation fields between different motion states. Gaussian kernel regression and linear extrapolation were used to predict PCA coefficients of future motion states for breathing motion (340 ms ahead of acquisition) and slow drifting motion (8.5 s ahead of acquisition) respectively. k-space data from the remaining five-minutes was used to compare ground truth motions states obtained from retrospective reconstruction/deformation with predictions. Median distances between predicted and ground truth centroid positions of gross tumor volume (GTV) and organs at risk (OARs) were less than 1 mm on average. 95- percentile Hausdorff distances between predicted and ground truth GTV contours of various breathing motions states were 2 mm on average, which was smaller than the imaging resolution and 95-percentile Hausdorff distances between predicted and ground truth OAR contours of different slow drifting motion states were less than 0.2 mm. These results suggest that multi-temporal resolution motion models are capable of volumetric predictions of breathing and slow drifting motion with sufficient accuracy and temporal resolution for MRI-based tracking, and thus have potential for supporting MRI-guided abdominal radiotherapy.

  View details for DOI 10.1088/1361-6560/ac1f37

  View details for PubMedID 34412047

• Gastrointestinal 4D MRI with respiratory motion correction. Medical physics Johansson, A., Balter, J. M., Cao, Y. 2021; 48 (5): 2521-2527

  Abstract

  Gastrointestinal motion patterns such as peristalsis and segmental contractions can alter the shape and position of the stomach and intestines with respect to other irradiated organs during radiation therapy. Unfortunately, these deformations are concealed by conventional four-dimensional (4D)-MRI techniques, which were developed to visualize respiratory motion by binning acquired data into respiratory motion states without considering the phases of GI motion. We present a method to reconstruct breathing-compensated images showing the phases of periodic gastric motion and study the effect of this motion on regional anatomical structures.Sixty-seven DCE-MRI examinations were performed on patients undergoing MRI simulation for hepatocellular carcinoma using a golden-angle stack-of-stars sequence that collected 2000 radial spokes over 5 min. The collected data were reconstructed using a method with integrated respiratory motion correction into a time series of 3D image volumes without visible breathing motion. From this series, a gastric motion signal was extracted by temporal filtering of time-intensity curves in the stomach. Using this motion signal, breathing-corrected back-projection images were sorted according to the gastric phase and reconstructed into 21 gastric motion state images showing the phases of gastric motion.Reconstructed image volumes showed gastric motion states clearly with no visible breathing motion or related artifacts. The mean frequency of the gastric motion signal was 3 cycles/min with a standard deviation of 0.27 cycles/min.Periodic gastrointestinal motion can be visualized without confounding respiratory motion using the presented GI 4D MRI technique. GI 4D MRIs may help define internal target volumes for treatment planning, aid in planning organ at risk volume definition, or support motion model development for gastrointestinal motion tracking algorithms for real-time MR-guided radiation therapy.

  View details for DOI 10.1002/mp.14786

  View details for PubMedID 33595909

  View details for PubMedCentralID PMC8172093

• Modeling intra-fractional abdominal configuration changes using breathing motion-corrected radial MRI PHYSICS IN MEDICINE AND BIOLOGY Liu, L., Johansson, A., Cao, Y., Kashani, R., Lawrence, T. S., Balter, J. M. 2021; 66 (8)

  Abstract

  Abdominal organ motions introduce geometric uncertainties to gastrointestinal radiotherapy. This study investigated slow drifting motion induced by changes of internal anatomic organ arrangements using a 3D radial MRI sequence with a scan length of 20 minutes. Breathing motion and cyclic GI motion were first removed through multi-temporal resolution image reconstruction. Slow drifting motion analysis was performed using an image time series consisting of 72 image volumes with a temporal sampling rate of 17 seconds. B-spline deformable registration was performed to align image volumes of the time series to a reference volume. The resulting deformation fields were used for motion velocity evaluation and patient-specific motion model construction through principal component analysis (PCA). Geometric uncertainties introduced by slow drifting motion were assessed by Hausdorff distances between unions of organs at risk (OARs) at different motion states and reference OAR contours as well as probabilistic distributions of OARs predicted using the PCA model. Thirteen examinations from 11 patients were included in this study. The averaged motion velocities ranged from 0.8 to 1.9 mm/min, 0.7 to 1.6 mm/min, 0.6 to 2.0 mm/min and 0.7 to 1.4 mm/min for the small bowel, colon, duodenum and stomach respectively; the averaged Hausdorff distances were 5.6 mm, 5.3 mm, 5.1 mm and 4.6 mm. On average, a margin larger than 4.5 mm was needed to cover a space with OAR occupancy probability higher than 55%. Temporal variations of geometric uncertainties were evaluated by comparing across four 5-min sub-scans extracted from the full scan. Standard deviations of Hausdorff distances across sub-scans were less than 1mm for most examinations, indicating stability of relative margin estimates from separate time windows. These results suggested slow drifting motion of GI organs is significant and geometric uncertainties introduced by such motion should be accounted for during radiotherapy planning and delivery.

  View details for DOI 10.1088/1361-6560/abef42

  View details for Web of Science ID 000639521700001

  View details for PubMedID 33725676

• A hierarchical model of abdominal configuration changes extracted from golden angle radial magnetic resonance imaging. Physics in medicine and biology Zhang, Y., Kashani, R., Cao, Y., Lawrence, T. S., Johansson, A., Balter, J. M. 2021; 66 (4): 045018

  Abstract

  Abdominal organs are subject to a variety of physiological forces that superimpose their effects to influence local motion and configuration. These forces not only include breathing, but can also arise from cyclic antral contractions and a range of slow configuration changes. To elucidate each individual motion pattern as well as their combined effects, a hierarchical motion model was built for characterization of these 3 motion modes (characterized as deformation maps between states) using golden angle radial MR signals. Breathing motions are characterized first. Antral contraction states are then reconstructed after breathing motion-induced deformation are corrected; slow configuration change states are further extracted from breathing motion-corrected image reconstructions. The hierarchical model is established based on these multimodal states, which can be either individually shown or combined to demonstrate any arbitrary composited motion patterns. The model was evaluated using 20 MR scans acquired from 9 subjects. Poor reproducibility of breathing motions both within as well as between scan sessions was observed, with an average intra-subject difference of 1.6 cycles min-1 for average breathing frequencies of 12.0 cycles min-1. Antral contraction frequency distributions were more stable than breathing, but also presented poor reproducibility between scans with an average difference of 0.3 cycles min-1 for average frequencies of 3.2 cycles min-1. The magnitudes of motions beyond breathing were found to be significant, with 14.4 and 33.8 mm maximal motions measured from antral contraction and slow configuration changes, respectively. Hierarchical motion models have potential in multiple applications in radiotherapy, including improving the accuracy of dose delivery estimation, providing guidance for margin creation, and supporting advanced decisions and strategies for immobilization, treatment monitoring and gating.

  View details for DOI 10.1088/1361-6560/abd66e

  View details for PubMedID 33361579

  View details for PubMedCentralID PMC7993537

• Abdominal synthetic CT generation from MR Dixon images using a U-net trained with 'semi-synthetic' CT data PHYSICS IN MEDICINE AND BIOLOGY Liu, L., Johansson, A., Cao, Y., Dow, J., Lawrence, T. S., Balter, J. M. 2020; 65 (12): 125001

  Abstract

  Magnetic resonance imaging (MRI) is gaining popularity in guiding radiation treatment for intrahepatic cancers due to its superior soft tissue contrast and potential of monitoring individual motion and liver function. This study investigates a deep learning-based method that generates synthetic CT volumes from T1-weighted MR Dixon images in support of MRI-based intrahepatic radiotherapy treatment planning. Training deep neutral networks for this purpose has been challenged by mismatches between CT and MR images due to motion and different organ filling status. This work proposes to resolve such challenge by generating 'semi-synthetic' CT images from rigidly aligned CT and MR image pairs. Contrasts within skeletal elements of the 'semi-synthetic' CT images were determined from CT images, while contrasts of soft tissue and air volumes were determined from voxel-wise intensity classification results on MR images. The resulting 'semi-synthetic' CT images were paired with their corresponding MR images and used to train a simple U-net model without adversarial components. MR and CT scans of 46 patients were investigated and the proposed method was evaluated for 31 patients with clinical radiotherapy plans, using 3-fold cross validation. The averaged mean absolute errors between synthetic CT and CT images across patients were 24.10 HU for liver, 28.62 HU for spleen, 47.05 HU for kidneys, 29.79 HU for spinal cord, 105.68 HU for lungs and 110.09 HU for vertebral bodies. VMAT and IMRT plans were optimized using CT-derived electron densities, and doses were recalculated using corresponding synthetic CT-derived density grids. Resulting dose differences to planning target volumes and various organs at risk were small, with the average difference less than 0.15 Gy for all dose metrics evaluated. The similarities in both image intensity and radiation dose distributions between CT and synthetic CT volumes demonstrate the accuracy of the method and its potential in supporting MRI-only radiotherapy treatment planning.

  View details for DOI 10.1088/1361-6560/ab8cd2

  View details for Web of Science ID 000542583000001

  View details for PubMedID 32330923

  View details for PubMedCentralID PMC7991979

• Abdominal DCE-MRI reconstruction with deformable motion correction for liver perfusion quantification. Medical physics Johansson, A., Balter, J. M., Cao, Y. 2018; 45 (10): 4529-4540

  Abstract

  Abdominal dynamic contrast-enhanced (DCE) MRI suffers from motion-induced artifacts that can blur images and distort contrast-agent uptake curves. For liver perfusion analysis, image reconstruction with rigid-body motion correction (RMC) can restore distorted portal-venous input functions (PVIF) to higher peak amplitudes. However, RMC cannot correct for liver deformation during breathing. We present a reconstruction algorithm with deformable motion correction (DMC) that enables correction of breathing-induced deformation in the whole abdomen.Raw data from a golden-angle stack-of-stars gradient-echo sequence were collected for 54 DCE-MRI examinations of 31 patients. For each examination, a respiratory motion signal was extracted from the data and used to reconstruct 21 breathing states from inhale to exhale. The states were aligned with deformable image registration to the end-exhale state. Resulting deformation fields were used to correct back-projection images before reconstruction with view sharing. Images with DMC were compared to uncorrected images and images with RMC.DMC significantly increased the PVIF peak amplitude compared to uncorrected images (P << 0.01, mean increase: 8%) but not compared to RMC. The increased PVIF peak amplitude significantly decreased estimated portal-venous perfusion in the liver (P << 0.01, mean decrease: 8 ml/(100 ml·min)). DMC also removed artifacts in perfusion maps at the liver edge and reduced blurring of liver tumors for some patients.DCE-MRI reconstruction with DMC can restore motion-distorted uptake curves in the abdomen and remove motion artifacts from reconstructed images and parameter maps but does not significantly improve perfusion quantification in the liver compared to RMC.

  View details for DOI 10.1002/mp.13118

  View details for PubMedID 30098044

  View details for PubMedCentralID PMC6181780

• Quantification of liver function by linearization of a two-compartment model of gadoxetic acid uptake using dynamic contrast-enhanced magnetic resonance imaging. NMR in biomedicine Simeth, J., Johansson, A., Owen, D., Cuneo, K., Mierzwa, M., Feng, M., Lawrence, T. S., Cao, Y. 2018; 31 (6): e3913

  Abstract

  Dynamic gadoxetic acid-enhanced magnetic resonance imaging (MRI) allows the investigation of liver function through the observation of the perfusion and uptake of contrast agent in the parenchyma. Voxel-by-voxel quantification of the contrast uptake rate (k1 ) from dynamic gadoxetic acid-enhanced MRI through the standard dual-input, two-compartment model could be susceptible to overfitting of variance in the data. The aim of this study was to develop a linearized, but more robust, model. To evaluate the estimated k1 values using this linearized analysis, high-temporal-resolution gadoxetic acid-enhanced MRI scans were obtained in 13 examinations, and k1 maps were created using both models. Comparison of liver k1 values estimated from the two methods produced a median correlation coefficient of 0.91 across the 12 scans that could be used. Temporally sparse clinical MRI data with gadoxetic acid uptake were also employed to create k1 maps of 27 examinations using the linearized model. Of 20 scans, the created k1 maps were compared with overall liver function as measured by indocyanine green (ICG) retention, and yielded a correlation coefficient of 0.72. In the 27 k1 maps created via the linearized model, the mean liver k1 value was 3.93 ± 1.79 mL/100 mL/min, consistent with previous studies. The results indicate that the linearized model provides a simple and robust method for the assessment of the rate of contrast uptake that can be applied to both high-temporal-resolution dynamic contrast-enhanced MRI and typical clinical multiphase MRI data, and that correlates well with the results of both two-compartment analysis and independent whole liver function measurements.

  View details for DOI 10.1002/nbm.3913

  View details for PubMedID 29675932

  View details for PubMedCentralID PMC5980790

• Rigid-body motion correction of the liver in image reconstruction for golden-angle stack-of-stars DCE MRI. Magnetic resonance in medicine Johansson, A., Balter, J., Cao, Y. 2018; 79 (3): 1345-1353

  Abstract

  Respiratory motion can affect pharmacokinetic perfusion parameters quantified from liver dynamic contrast-enhanced MRI. Image registration can be used to align dynamic images after reconstruction. However, intra-image motion blur remains after alignment and can alter the shape of contrast-agent uptake curves. We introduce a method to correct for inter- and intra-image motion during image reconstruction.Sixteen liver dynamic contrast-enhanced MRI examinations of nine subjects were performed using a golden-angle stack-of-stars sequence. For each examination, an image time series with high temporal resolution but severe streak artifacts was reconstructed. Images were aligned using region-limited rigid image registration within a region of interest covering the liver. The transformations resulting from alignment were used to correct raw data for motion by modulating and rotating acquired lines in k-space. The corrected data were then reconstructed using view sharing.Portal-venous input functions extracted from motion-corrected images had significantly greater peak signal enhancements (mean increase: 16%, t-test, P <  0.001) than those from images aligned using image registration after reconstruction. In addition, portal-venous perfusion maps estimated from motion-corrected images showed fewer artifacts close to the edge of the liver.Motion-corrected image reconstruction restores uptake curves distorted by motion. Motion correction also reduces motion artifacts in estimated perfusion parameter maps. Magn Reson Med 79:1345-1353, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

  View details for DOI 10.1002/mrm.26782

  View details for PubMedID 28617993

  View details for PubMedCentralID PMC5747555

• Modeling of Normal Tissue Complications Using Imaging and Biomarkers After Radiation Therapy for Hepatocellular Carcinoma. International journal of radiation oncology, biology, physics El Naqa, I., Johansson, A., Owen, D., Cuneo, K., Cao, Y., Matuszak, M., Bazzi, L., Lawrence, T. S., Ten Haken, R. K. 2018; 100 (2): 335-343

  Abstract

  To develop normal tissue complications (NTCP) models for hepatocellular cancer (HCC) patients who undergo liver radiation therapy (RT) and to evaluate the potential role of functional imaging and measurement of blood-based circulating biological markers before and during RT to improve the performance of these models.The data from 192 HCC patients who had undergone RT from 2005 to 2014 were evaluated. Of the 192 patients, 146 had received stereotactic body RT (SBRT) and 46 had received conventional RT to a median physical tumor dose of 49.8 Gy and 50.4 Gy, respectively. The physical doses were converted into 2-Gy equivalents for analysis. Two approaches were investigated for modeling NTCP: (1) a generalized Lyman-Kutcher-Burman model; and (2) a generalization of the parallel architecture model. Three clinical endpoints were considered: the change in albumin-bilirubin (ALBI), change in Child-Pugh (C-P) score, and grade ≥3 liver enzymatic changes. Local dynamic contrast-enhanced magnetic resonance imaging portal venous perfusion information was used as an imaging biomarker for local liver function. Four candidate inflammatory cytokines were considered as biological markers. The imaging findings and cytokine levels were incorporated into NTCP modeling, and their role was evaluated using goodness-of-fit metrics.Using dosimetric information only, the Lyman-Kutcher-Burman model for the ALBI/C-P change had a steeper response curve compared with grade ≥3 enzymatic changes. Incorporating portal venous perfusion imaging information into the parallel architecture model to represent functional reserve resulted in relatively steeper dose-response curves compared with dose-only models. A larger loss of perfusion function was needed for enzymatic changes compared with ALBI/C-P changes. Increased transforming growth factor-β1 and eotaxin expression increased the trend of expected risk in both NTCP modeling approaches but did not reach statistical significance.The incorporation of imaging findings and biological markers into NTCP modeling of liver toxicity improved the estimates of expected NTCP risk compared with using dose-only models. In addition, such generalized NTCP models should contribute to a better understanding of the normal tissue response in HCC SBRT patients and facilitate personalized treatment.

  View details for DOI 10.1016/j.ijrobp.2017.10.005

  View details for PubMedID 29353652

  View details for PubMedCentralID PMC5779633

• ACCELERATED HIGH B-VALUE DIFFUSION-WEIGHTED MR IMAGING VIA PHASE-CONSTRAINED LOW-RANK TENSOR MODEL Liu, L., Johansson, A., Balter, J. M., Cao, Y., Fessler, J. A., IEEE IEEE. 2018: 344-348

  View details for Web of Science ID 000455045600078

• Utility of 4DMRI for Liver Radiation Planning Owen, D., Vineberg, K. A., Li, P., Cao, Y., Johansson, A., Cuneo, K. C., Lawrence, T. S., Balter, J. ELSEVIER SCIENCE INC. 2017: S176-S177

  View details for DOI 10.1016/j.ijrobp.2017.06.444

  View details for Web of Science ID 000411559107192

• Effect of gradient field nonlinearity distortions in MRI-based attenuation maps for PET reconstruction. Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Lundman, J. A., Johansson, A., Olofsson, J., Axelsson, J., Larsson, A., Nyholm, T. 2017; 35: 1-6

  Abstract

  Attenuation correction is a requirement for quantification of the activity distribution in PET. The need to base attenuation correction on MRI instead of CT has arisen with the introduction of integrated PET/MRI systems. The aim was to describe the effect of residual gradient field nonlinearity distortions on PET attenuation correction.MRI distortions caused by gradient field nonlinearity were simulated in CT images used for attenuation correction in PET reconstructions. The simulations yielded radial distortion of up to ±2.3mm at 15cm from the scanner isocentre for distortion corrected images. The mean radial distortion of uncorrected images were 6.3mm at the same distance. Reconstructions of PET data were performed using the distortion corrected images as well as the images where no correction had been applied.The mean relative difference in reconstructed PET uptake intensity due to incomplete distortion correction was less than ±5%. The magnitude of this difference varied between patients and the size of the distortions remaining after distortion correction.Radial distortions of 2mm at 15cm radius from the scanner isocentre lead to PET attenuation correction errors smaller than 5%. Keeping the gradient field nonlinearity distortions below this limit can be a reasonable goal for MRI systems used for attenuation correction in PET for quantification purposes. A higher geometrical accuracy may, however, be warranted for quantification of peripheral lesions. These distortions can, e.g., be controlled at acceptance testing and subsequent quality assurance intervals.

  View details for DOI 10.1016/j.ejmp.2017.02.019

  View details for PubMedID 28283354

• An Overdetermined System of Transform Equations in Support of Robust DCE-MRI Registration With Outlier Rejection. Tomography (Ann Arbor, Mich.) Johansson, A., Balter, J., Feng, M., Cao, Y. 2016; 2 (3): 188-196

  Abstract

  Quantitative hepatic perfusion parameters derived by fitting dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of liver to a pharmacokinetic model are prone to errors if the dynamic images are not corrected for respiratory motion by image registration. The contrast-induced intensity variations in pre- and postcontrast phases pose challenges for the accuracy of image registration. We propose an overdetermined system of transformation equations between the image volumes in the DCE-MRI series to achieve robust alignment. In this method, we register each volume to every other volume. From the transforms produced by all pairwise registrations, we constructed an overdetermined system of transform equations that was solved robustly by minimizing the L1/2-norm of the residuals. This method was evaluated on a set of 100 liver DCE-MRI examinations from 35 patients by examining the area under spikes appearing in the voxel time-intensity curves. The robust alignment procedure significantly reduced the area under intensity spikes compared with unregistered volumes (P<.001) and volumes registered to a single reference phase (P<.001). Our registration procedure provides a larger number of reliable time-intensity curve samples. The additional reliable samples in the precontrast baseline are important for calculating the postcontrast signal enhancement and thereby for converting intensity to contrast concentration. On the intensity ramp, retained samples help to better describe the uptake dynamics, providing a better foundation for parameter estimation. The presented method also simplifies the analysis of data sets with many patients by eliminating the need for manual intervention during registration.

  View details for DOI 10.18383/j.tom.2016.00145

  View details for PubMedID 28367502

  View details for PubMedCentralID PMC5373730

• Portal Venous Perfusion Quantitation From Liver DCE-MRI by Voxel Uptake Curve Clustering and Input Function Normalization Johansson, A., Balter, J., Feng, M., Cao, Y. WILEY. 2016: 3833

  View details for DOI 10.1118/1.4957940

  View details for Web of Science ID 000402383600008

• Accuracy of inverse treatment planning on substitute CT images derived from MR data for brain lesions. Radiation oncology (London, England) Jonsson, J. H., Akhtari, M. M., Karlsson, M. G., Johansson, A., Asklund, T., Nyholm, T. 2015; 10: 13

  Abstract

  In this pilot study we evaluated the performance of a substitute CT (s-CT) image derived from MR data of the brain, as a basis for optimization of intensity modulated rotational therapy, final dose calculation and derivation of reference images for patient positioning.S-CT images were created using a Gaussian mixture regression model on five patients previously treated with radiotherapy. Optimizations were compared using D max, D min, D median and D mean measures for the target volume and relevant risk structures. Final dose calculations were compared using gamma index with 1%/1 mm and 3%/3 mm acceptance criteria. 3D geometric evaluation was conducted using the DICE similarity coefficient for bony structures. 2D geometric comparison of digitally reconstructed radiographs (DRRs) was performed by manual delineation of relevant structures on the s-CT DRR that were transferred to the CT DRR and compared by visual inspection.Differences for the target volumes in optimization comparisons were small in general, e.g. a mean difference in both D min and D max within ±0.3%. For the final dose calculation gamma evaluations, 100% of the voxels passed the 1%/1 mm criterion within the PTV. Within the entire external volume between 99.4% and 100% of the voxels passed the 3%/3 mm criterion. In the 3D geometric comparison, the DICE index varied between approximately 0.8-0.9, depending on the position in the skull. In the 2D DRR comparisons, no appreciable visual differences were found.Even though the present work involves a limited number of patients, the results provide a strong indication that optimization and dose calculation based on s-CT data is accurate regarding both geometry and dosimetry.

  View details for DOI 10.1186/s13014-014-0308-1

  View details for PubMedID 25575414

  View details for PubMedCentralID PMC4299127

• CT substitutes derived from MR images reconstructed with parallel imaging. Medical physics Johansson, A., Garpebring, A., Asklund, T., Nyholm, T. 2014; 41 (8): 082302

  Abstract

  Computed tomography (CT) substitute images can be generated from ultrashort echo time (UTE) MRI sequences with radial k-space sampling. These CT substitutes can be used as ordinary CT images for PET attenuation correction and radiotherapy dose calculations. Parallel imaging allows faster acquisition of magnetic resonance (MR) images by exploiting differences in receiver coil element sensitivities. This study investigates whether non-Cartesian parallel imaging reconstruction can be used to improve CT substitutes generated from shorter examination times.The authors used gridding as well as two non-Cartesian parallel imaging reconstruction methods, SPIRiT and CG-SENSE, to reconstruct radial UTE and gradient echo (GE) data into images of the head for 23 patients. For each patient, images were reconstructed from the full dataset and from a number of subsampled datasets. The subsampled datasets simulated shorter acquisition times by containing fewer radial k-space spokes (1000, 2000, 3000, 5000, and 10,000 spokes) than the full dataset (30,000 spokes). For each combination of patient, reconstruction method, and number of spokes, the reconstructed UTE and GE images were used to generate a CT substitute. Each CT substitute image was compared to a real CT image of the same patient.The mean absolute deviation between the CT number in CT substitute and CT decreased when using SPIRiT as compared to gridding reconstruction. However, the reduction was small and the CT substitute algorithm was insensitive to moderate subsampling (≥ 5000 spokes) regardless of reconstruction method. For more severe subsampling (≤ 3000 spokes), corresponding to acquisition times less than a minute long, the CT substitute quality was deteriorated for all reconstruction methods but SPIRiT gave a reduction in the mean absolute deviation of down to 25 Hounsfield units compared to gridding.SPIRiT marginally improved the CT substitute quality for a given number of radial spokes as compared to gridding. However, the increased reconstruction time of non-Cartesian parallel imaging reconstruction is difficult to motivate from this improvement. Because the CT substitute algorithm was insensitive to moderate subsampling, data for a CT substitute could be collected in as little as minute and reconstructed with gridding without deteriorating the CT substitute quality.

  View details for DOI 10.1118/1.4886766

  View details for PubMedID 25086551

• Improved quality of computed tomography substitute derived from magnetic resonance (MR) data by incorporation of spatial information--potential application for MR-only radiotherapy and attenuation correction in positron emission tomography. Acta oncologica (Stockholm, Sweden) Johansson, A., Garpebring, A., Karlsson, M., Asklund, T., Nyholm, T. 2013; 52 (7): 1369-73

  Abstract

  Estimation of computed tomography (CT) equivalent data, i.e. a substitute CT (s-CT), from magnetic resonance (MR) images is a prerequisite both for attenuation correction of positron emission tomography (PET) data acquired with a PET/MR scanner and for dose calculations in an MR-only radiotherapy workflow. It has previously been shown that it is possible to estimate Hounsfield numbers based on MR image intensities, using ultra short echo-time imaging and Gaussian mixture regression (GMR). In the present pilot study we investigate the possibility to also include spatial information in the GMR, with the aim to improve the quality of the s-CT.MR and CT data for nine patients were used in the present study. For each patient, GMR models were created from the other eight patients, including either both UTE image intensities and spatial information on a voxel by voxel level, or only UTE image intensities. The models were used to create s-CT images for each respective patient.The inclusion of spatial information in the GMR model improved the accuracy of the estimated s-CT. The improvement was most pronounced in smaller, complicated anatomical regions as the inner ear and post-nasal cavities.This pilot study shows that inclusion of spatial information in GMR models to convert MR data to CT equivalent images is feasible. The accuracy of the s-CT is improved and the spatial information could make it possible to create a general model for the conversion applicable to the whole body.

  View details for DOI 10.3109/0284186X.2013.819119

  View details for PubMedID 23984810

• Treatment planning of intracranial targets on MRI derived substitute CT data. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Jonsson, J. H., Johansson, A., Söderström, K., Asklund, T., Nyholm, T. 2013; 108 (1): 118-22

  Abstract

  The use of magnetic resonance imaging (MRI) as a complement to computed tomography (CT) in the target definition procedure for radiotherapy is increasing. To eliminate systematic uncertainties due to image registration, a workflow based entirely on MRI may be preferable. In the present pilot study, we investigate dose calculation accuracy for automatically generated substitute CT (s-CT) images of the head based on MRI. We also produce digitally reconstructed radiographs (DRRs) from s-CT data to evaluate the feasibility of patient positioning based on MR images.Five patients were included in the study. The dose calculation was performed on CT, s-CT, s-CT data without inhomogeneity correction and bulk density assigned MRI images. Evaluation of the results was performed using point dose and dose volume histogram (DVH) comparisons, and gamma index evaluation.The results demonstrate that the s-CT images improve the dose calculation accuracy compared to the method of non-inhomogeneity corrected dose calculations (mean improvement 2.0% points) and that it performs almost identically to the method of bulk density assignment. The s-CT based DRRs appear to be adequate for patient positioning of intra-cranial targets, although further investigation is needed on this subject.The s-CT method is very fast and yields data that can be used for treatment planning without sacrificing accuracy.

  View details for DOI 10.1016/j.radonc.2013.04.028

  View details for PubMedID 23830190

• Bone contrast optimization in magnetic resonance imaging using experimental design of ultra-short echo-time parameters CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS Lindstrom, A., Andersson, C., Johansson, A., Karlsson, M., Nyholm, T. 2013; 125: 33-39

  View details for DOI 10.1016/j.chemolab.2013.03.011

  View details for Web of Science ID 000320217500004

• Uncertainty estimation in dynamic contrast-enhanced MRI. Magnetic resonance in medicine Garpebring, A., Brynolfsson, P., Yu, J., Wirestam, R., Johansson, A., Asklund, T., Karlsson, M. 2013; 69 (4): 992-1002

  Abstract

  Using dynamic contrast-enhanced MRI (DCE-MRI), it is possible to estimate pharmacokinetic (PK) parameters that convey information about physiological properties, e.g., in tumors. In DCE-MRI, errors propagate in a nontrivial way to the PK parameters. We propose a method based on multivariate linear error propagation to calculate uncertainty maps for the PK parameters. Uncertainties in the PK parameters were investigated for the modified Kety model. The method was evaluated with Monte Carlo simulations and exemplified with in vivo brain tumor data. PK parameter uncertainties due to noise in dynamic data were accurately estimated. Noise with standard deviation up to 15% in the baseline signal and the baseline T1 map gave estimated uncertainties in good agreement with the Monte Carlo simulations. Good agreement was also found for up to 15% errors in the arterial input function amplitude. The method was less accurate for errors in the bolus arrival time with disagreements of 23%, 32%, and 29% for K(trans) , ve , and vp , respectively, when the standard deviation of the bolus arrival time error was 5.3 s. In conclusion, the proposed method provides efficient means for calculation of uncertainty maps, and it was applicable to a wide range of sources of uncertainty.

  View details for DOI 10.1002/mrm.24328

  View details for PubMedID 22714717

• Evaluation of an attenuation correction method for PET/MR imaging of the head based on substitute CT images. Magma (New York, N.Y.) Larsson, A., Johansson, A., Axelsson, J., Nyholm, T., Asklund, T., Riklund, K., Karlsson, M. 2013; 26 (1): 127-36

  Abstract

  The aim of this study was to evaluate MR-based attenuation correction of PET emission data of the head, based on a previously described technique that calculates substitute CT (sCT) images from a set of MR images.Images from eight patients, examined with (18)F-FLT PET/CT and MRI, were included. sCT images were calculated and co-registered to the corresponding CT images, and transferred to the PET/CT scanner for reconstruction. The new reconstructions were then compared with the originals. The effect of replacing bone with soft tissue in the sCT-images was also evaluated.The average relative difference between the sCT-corrected PET images and the CT-corrected PET images was 1.6% for the head and 1.9% for the brain. The average standard deviations of the relative differences within the head were relatively high, at 13.2%, primarily because of large differences in the nasal septa region. For the brain, the average standard deviation was lower, 4.1%. The global average difference in the head when replacing bone with soft tissue was 11%.The method presented here has a high rate of accuracy, but high-precision quantitative imaging of the nasal septa region is not possible at the moment.

  View details for DOI 10.1007/s10334-012-0339-2

  View details for PubMedID 22955943

• Voxel-wise uncertainty in CT substitute derived from MRI. Medical physics Johansson, A., Karlsson, M., Yu, J., Asklund, T., Nyholm, T. 2012; 39 (6): 3283-90

  Abstract

  In an earlier work, we demonstrated that substitutes for CT images can be derived from MR images using ultrashort echo time (UTE) sequences, conventional T2 weighted sequences, and Gaussian mixture regression (GMR). In this study, we extend this work by analyzing the uncertainties associated with the GMR model and the information contributions from the individual imaging sequences.An analytical expression for the voxel-wise conditional expected absolute deviation (EAD) in substitute CT (s-CT) images was derived. The expression depends only on MR images and can thus be calculated along with each s-CT image. The uncertainty measure was evaluated by comparing the EAD to the true mean absolute prediction deviation (MAPD) between the s-CT and CT images for 14 patients. Further, the influence of the different MR images included in the GMR model on the generated s-CTs was investigated by removing one or more images and evaluating the MAPD for a spectrum of predicted radiological densities.The largest EAD was predicted at air-soft tissue and bone-soft tissue interfaces. The EAD agreed with the MAPD in both these regions and in regions with lower EADs, such as the brain. Two of the MR images included in the GMR model were found to be mutually redundant for the purpose of s-CT generation.The presented uncertainty estimation method accurately predicts the voxel-wise MAPD in s-CT images. Also, the non-UTE sequence previously used in the model was found to be redundant.

  View details for DOI 10.1118/1.4711807

  View details for PubMedID 22755711

• CT substitute derived from MRI sequences with ultrashort echo time. Medical physics Johansson, A., Karlsson, M., Nyholm, T. 2011; 38 (5): 2708-14

  Abstract

  Methods for deriving computed tomography (CT) equivalent information from MRI are needed for attenuation correction in PET/MRI applications, as well as for patient positioning and dose planning in MRI based radiation therapy workflows. This study presents a method for generating a drop in substitute for a CT image from a set of magnetic resonance (MR)images.A Gaussian mixture regression model was used to link the voxel values in CT images to the voxel values in images from three MRI sequences: one T2 weighted 3D spin echo based sequence and two dual echo ultrashort echo time MRI sequences with different echo times and flip angles. The method used a training set of matched MR and CT data that after training was able to predict a substitute CT (s-CT) based entirely on the MR information for a new patient. Method validation was achieved using datasets covering the heads of five patients and applying leave-one-out cross-validation (LOOCV). During LOOCV, the model was estimated from the MR and CT data of four patients (training set) and applied to the MR data of the remaining patient (validation set) to generate an s-CT image. This procedure was repeated for all five training and validation data combinations.The mean absolute error for the CT number in the s-CT images was 137 HU. No large differences in method accuracy were noted for the different patients, indicating a robust method. The largest errors in the s-CT images were found at air-tissue and bone-tissue interfaces. The model accurately discriminated between air and bone, as well as between soft tissues and nonsoft tissues.The s-CT method has the potential to provide an accurate estimation of CT information without risk of geometrical inaccuracies as the model is voxel based. Therefore, s-CT images could be well suited as alternatives to CT images for dose planning in radiotherapy and attenuation correction in PET/MRI.

  View details for DOI 10.1118/1.3578928

  View details for PubMedID 21776807

• VOXEL BASED METHOD TO GENERATE CT EQUIVALENT IMAGES FROM MR IMAGES FOR TREATMENT PLANNING PURPOSES Nyholm, T., Johansson, A., Karlsson, M. ELSEVIER IRELAND LTD. 2011: S149-S150

  View details for DOI 10.1016/S0167-8140(11)70501-1

  View details for Web of Science ID 000433476201271