Determination of T1- and T2-relaxation times in the spleen of patients with splenomegaly

Thomsen, Carsten; Josephsen, P.; Karle, Hans; Juhl, E; Sørensen, P. G.; Henriksen, O.

doi:10.1016/0730-725x(90)90210-s

Cited by 7 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SPLEENOMEGALY, abnormal enlargement of the spleen, is associated with abnormal red blood cell destruction, which has been used as a clinical biomarker associated with liver disease [1], infection [2] and cancer [3]. Non-invasive medical imaging techniques (e.g., ultrasound [4,5], computed tomography (CT) [6][7][8][9], and magnetic resonance imaging (MRI) [10,11]) have been used for clinical and scientific investigations on splenomegaly. Manually tracing a spleen on two-dimensional (2D) slices from a three-dimensional (3D) volume has been regarded as the gold standard of imaging-based spleen volume estimation [11].…”

Section: Introductionmentioning

confidence: 99%

Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks

Huo

Bao

et al. 2019

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen disease. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal magnetic resonance imaging (MRI) scans. However, the task is challenging due to (1) large anatomical and spatial variations of splenomegaly, (2) large inter-and intra-scan intensity variations on multi-modal MRI, and (3) limited numbers of labeled splenomegaly scans. In this paper, we propose the Splenomegaly Segmentation Network (SS-Net) to introduce the deep convolutional neural network (DCNN) approaches in multi-modal MRI splenomegaly segmentation. Large convolutional kernel layers were used to address the spatial and anatomical variations, while the conditional generative adversarial networks (GAN) were employed to leverage the segmentation performance of SS-Net in an end-to-end manner. A clinically acquired cohort containing both T1-weighted (T1w) and T2weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multiatlas segmentation (MAS), 2D DCNN networks, and a 3D DCNN network. From the experimental results, the DCNN methods achieved superior performance to the state-of-the-art MAS method. The proposed SS-Net method achieved the highest median and mean Dice scores among investigated baseline DCNN methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks

Huo

Bao

et al. 2019

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

show abstract

“…Abnormal enlargement of the spleen, called splenomegaly [1], is a clinical finding in the patients with liver disease [2], cancer [3] and infection [4]. To quantify spleen enlargement, non-invasive spleen volume estimation approaches have been proposed using different imaging modalities (e.g., ultrasound [5–8], computed tomography (CT) [9–12], magnetic resonance imaging (MRI) [13, 14]). Slice-by-slice manual tracing on three-dimensional (3D) spleen volumes has been regarded as the gold standard of in vivo spleen size estimation [14].…”

Section: Introductionmentioning

confidence: 99%

Robust Multicontrast MRI Spleen Segmentation for Splenomegaly Using Multi-Atlas Segmentation

Huo

Liu

et al. 2018

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

show abstract

A segmented k‐space velocity mapping protocol for quantification of renal artery blood plow during breath‐holding

Thomsen

Cortsen

Søndergaard

et al. 1995

Magnetic Resonance Imaging

Self Cite

View full text Add to dashboard Cite

Two important prerequisites for MR velocity mapping of pulsatile motion are synchronization of the sequence execution to the time course of the flow pattern and robustness toward loss of signal in complex flow fields. Synchronization is normally accomplished by using either prospective ECG triggering or so-called retrospective gating. However, if the studied vessel moves periodically in space as a result of respiratory motion, as in the case of renal arteries, a second synchronization with respect to the vessel motion in space may be necessary. One method to overcome this problem is to use the segmented k-space technique, in which the entire data acquisition can be made within a breath-hold by the sampling of several phase-encoding lines within a small time window during each heart cycle. The aim of this study was to investigate the performance of a segmented k-space velocity mapping protocol for renal artery flow determination. The protocol uses 16 phase-encoding lines per heart beat during 16 heart cycles and gives a temporal velocity resolution of 160 msec. Comparison with a conventional ECG-triggered velocity mapping protocol was made in phantoms as well as in volunteers. In our study, both methods showed sufficient robustness toward complex flow in a phantom model. In comparison with the ECG technique, the segmentation technique reduced vessel blurring and pulsatility artifacts caused by respiratory motion, and average flow values obtained in vivo in the left renal artery agreed between the two methods studied.(ABSTRACT TRUNCATED AT 250 WORDS)

show abstract

Determination of T1- and T2-relaxation times in the spleen of patients with splenomegaly

Cited by 7 publications

References 16 publications

Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks

Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks

Robust Multicontrast MRI Spleen Segmentation for Splenomegaly Using Multi-Atlas Segmentation

A segmented k‐space velocity mapping protocol for quantification of renal artery blood plow during breath‐holding

Contact Info

Product

Resources

About