spots not filling the whole tissue image

In Seurat, SpatialPlot generates a plot with an enlarged/expanded image of tissue section as compared to the original spot image. This seems to happen on the relatively small image with a number of spots around 500.

I ‘d really appreciate any pointers to fix this.

SpatialPlot(DT006.rep1)

Original spot image:

Here is the 10xvisium Seurat object in question:

str(DT006.rep1@images)
List of 1
 $ slice1:Formal class 'VisiumV1' [package "Seurat"] with 6 slots
  .. ..@ image        : num [1:600, 1:600, 1:3] 0.698 0.847 0.851 0.851 0.851 ...
  .. ..@ scale.factors:List of 4
  .. .. ..$ spot    : num 0.15
  .. .. ..$ fiducial: num 145
  .. .. ..$ hires   : num 0.15
  .. .. ..$ lowres  : num 0.045
  .. .. ..- attr(*, "class")= chr "scalefactors"
  .. ..@ coordinates  :'data.frame':    481 obs. of  5 variables:
  .. .. ..$ tissue  : int [1:481] 1 1 1 1 1 1 1 1 1 1 ...
  .. .. ..$ row     : int [1:481] 42 37 35 45 42 47 44 40 47 48 ...
  .. .. ..$ col     : int [1:481] 28 19 79 67 96 69 56 96 39 42 ...
  .. .. ..$ imagerow: int [1:481] 6539 5941 5692 6893 6529 7133 6775 6289 7137 7257 ...
  .. .. ..$ imagecol: int [1:481] 4126 3504 7641 6816 8815 6954 6057 8814 4886 5093 ...
  .. ..@ spot.radius  : num 0.0109
  .. ..@ assay        : chr "Spatial"
  .. ..@ key          : chr "slice1_"

Sessioninfo:

sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Big Sur 11.5.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_3.3.5      Seurat_4.0.3.9015  SeuratObject_4.0.2

loaded via a namespace (and not attached):
  [1] nlme_3.1-152          matrixStats_0.60.0    spatstat.sparse_2.0-0
  [4] RcppAnnoy_0.0.19      RColorBrewer_1.1-2    httr_1.4.2           
  [7] sctransform_0.3.2     tools_4.1.0           utf8_1.2.2           
 [10] R6_2.5.0              irlba_2.3.3           rpart_4.1-15         
 [13] KernSmooth_2.23-20    uwot_0.1.10           mgcv_1.8-36          
 [16] lazyeval_0.2.2        colorspace_2.0-2      withr_2.4.2          
 [19] tidyselect_1.1.1      gridExtra_2.3         compiler_4.1.0       
 [22] plotly_4.9.4.1        labeling_0.4.2        scales_1.1.1         
 [25] lmtest_0.9-38         spatstat.data_2.1-0   ggridges_0.5.3       
 [28] pbapply_1.4-3         goftest_1.2-2         stringr_1.4.0        
 [31] digest_0.6.27         spatstat.utils_2.2-0  pkgconfig_2.0.3      
 [34] htmltools_0.5.1.1     parallelly_1.27.0     fastmap_1.1.0        
 [37] htmlwidgets_1.5.3     rlang_0.4.11          shiny_1.6.0          
 [40] farver_2.1.0          generics_0.1.0        zoo_1.8-9            
 [43] jsonlite_1.7.2        ica_1.0-2             dplyr_1.0.7          
 [46] magrittr_2.0.1        patchwork_1.1.1       Matrix_1.3-4         
 [49] Rcpp_1.0.7            munsell_0.5.0         fansi_0.5.0          
 [52] abind_1.4-5           reticulate_1.20-9002  lifecycle_1.0.0      
 [55] stringi_1.7.3         MASS_7.3-54           Rtsne_0.15           
 [58] plyr_1.8.6            grid_4.1.0            parallel_4.1.0       
 [61] listenv_0.8.0         promises_1.2.0.1      ggrepel_0.9.1        
 [64] crayon_1.4.1          miniUI_0.1.1.1        deldir_0.2-10        
 [67] lattice_0.20-44       cowplot_1.1.1         splines_4.1.0        
 [70] tensor_1.5            pillar_1.6.2          igraph_1.2.6         
 [73] spatstat.geom_2.2-2   future.apply_1.8.1    reshape2_1.4.4       
 [76] codetools_0.2-18      leiden_0.3.9          glue_1.4.2           
 [79] data.table_1.14.0     png_0.1-7             vctrs_0.3.8          
 [82] httpuv_1.6.1          gtable_0.3.0          RANN_2.6.1           
 [85] purrr_0.3.4           spatstat.core_2.3-0   polyclip_1.10-0      
 [88] tidyr_1.1.3           scattermore_0.7       future_1.21.0        
 [91] mime_0.11             xtable_1.8-4          later_1.2.0          
 [94] survival_3.2-11       viridisLite_0.4.0     tibble_3.1.3         
 [97] cluster_2.1.2         globals_0.14.0        fitdistrplus_1.1-5   
[100] ellipsis_0.3.2        ROCR_1.0-11