TOF data non-rigid motion correction

Panin, Vladimir; Bal, Harshali

doi:10.1109/nssmic.2015.7582254

Cited by 5 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MVF was applied in histo-projections in the case of computer simulations, through the technique presented in [12]. This resulted in additional blurring of MVF in the TOF direction of a particular angle sample and this may affect results, since histo-images based MVF estimation is blurred from the beginning.…”

Section: Discussionmentioning

confidence: 99%

“…After histo-image MFV estimation, motion correction is performed in histo-projection space [12] and images are reconstructed by OS-EM algorithm, 5 subsets. The gated images were also assembled by the "pull" method using simplest nearest neighbor interpolation, to produce the MC image with true motion.…”

Section: Computer Simulationsmentioning

confidence: 99%

“…Fig. 1 shows the principle of motion correction (MC) in histo-projections from our previous work [12]. There, we investigated motion correction in the TOF data space, assuming that the field of motion is known.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Histo-Projections TOF Data Non-Rigid Motion Estimation and Correction

Panin¹

2021

Preprint

View full text Add to dashboard Cite

<p>With advancements in PET technology, scanners with a very good time-of-flight (TOF) resolution became available. In addition, scanners with long axial extent are now commercialized. Although raw data sizes increase significantly with such scanners, good TOF resolution allows substantial data compression without any loss of spatial resolution: for example, the histo-projections with relatively coarse sampling in the TOF direction in the Siemens Biograph Vision and Vision Quadra PET/CT scanners. The patient motion during the scan is unavoidable. The pattern caused by breathing may result in a relatively large displacement of organs and the consequent blurring of clinically relevant features in regions affected by the motion. As was shown in our previous work, non-rigid motion correction can be performed in the quasi-image space of histo-projections. The TOF locality property can be used to locally perform motion correction; that is, the approximation of motion as locally rigid on a scale of TOF resolution. In this work, we investigate motion estimation in the TOF data space from histo-images by histogramming the data into a 3D array consistent (in geometry and size) with the reconstruction image. The histo-image estimated motion is then used in histo-projections motion correction. Initial results using an XCAT digital phantom showed that the presented methodology accommodates for changes in non-rigid body movements for a typical pattern of patient motion. The patient scan on newly developed Biograph Vision Quadra PET/CT scanner was used in validation of presented motion correction.</p>

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Computer Simulationsmentioning

confidence: 99%

See 1 more Smart Citation

Histo-Projections TOF Data Non-Rigid Motion Estimation and Correction

Panin¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The DIRECT frame work used a similar compression tactic with histo-images and was able to achieve faster reconstructions but faced challenges with high sensitivity to data inconsistencies. While the single histoimage approach did not find a place in traditional reconstruction techniques, it has demonstrated potential in motion detection [8] and motion pre-correction [9], where a deblurring procedure may not be necessary.…”

mentioning

confidence: 99%

FastPET: Near Real-Time Reconstruction of PET Histo-Image Data Using a Neural Network

Whiteley

Panin

Zhou

et al. 2021

IEEE Trans. Radiat. Plasma Med. Sci.

View full text Add to dashboard Cite

Direct reconstruction of positron emission tomography (PET) data using deep neural networks is a growing field of research. Initial results are promising, but often the networks are complex, memory utilization inefficient, produce relatively small 2-D image slices (e.g., 128 × 128), and low count rate reconstructions are of varying quality. This article proposes FastPET, a novel direct reconstruction convolutional neural network that is architecturally simple, memory space efficient, works for nontrivial 3-D image volumes and is capable of processing a wide spectrum of PET data including low-dose and multitracer applications. FastPET uniquely operates on a histoimage (i.e., image-space) representation of the raw data enabling it to reconstruct 3-D image volumes 67× faster than ordered subsets expectation maximization (OSEM). We detail the FastPET method trained on whole-body and low-dose whole-body data sets and explore qualitative and quantitative aspects of reconstructed images from clinical and phantom studies. Additionally, we explore the application of FastPET on a neurology data set containing multiple different tracers. The results show that not only are the reconstructions very fast, but the images are high quality and have lower noise than iterative reconstructions.

show abstract